• 센서학회지
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• 제17권3호
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• pp.229-235
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• 2008

In order to monitor the anticancer drug in stable conditions, the Web based anticancer drug management system and alarm services were constructed and assessed in this study. Anticancer drug should be exact to the correct patient in the right environment. To overcome the restriction of existing equipment that only monitors fragmentarily, temperature and humidity were continuously monitored to maintain stable environments using sensor networks and RFID for the monitoring and management of anticancer drug. Construction drug identification and the effect of normal air outside the anticancer dispensary with obstacles were evaluated in working hour. Pre-installed control system in the dispensary could be alternated with auto sensing and alarming. We expected that the efficiency of anticancer drug management and the reliability of drug medication by handwork would be increase accordingly.

• 한국안전학회지
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• 제7권2호
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• pp.71-79
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• 1992

This paper presents a least-square algorithms of lattice structures and their use for adaptive prediction of time series generated from the dynamic system. As the view point of adaptive prediction, a new method of Identification of dynamic characteristics by means of estimating the parameters of linear auto regressive model is proposed. The fast convergence of adaptive lattice algorithms is seen to be due to the orthogonalization and decoupling properties of the lattice. The superiority of the least-square lattice is verified by computer simulation, then predictor coefficients are computed from the linear sequential time data. For the application to the dynamic characteristic analysis of unknown system, the transfer function of ideal system represented in frquency domain and the estimated one obtained by predicted coefficients are compared. Using the proposed method, the damping ratio and the natural frequency of a dynamic structure subjected to random excitations can be estimated. It is expected that this method will be widely applicable to other technical dynamic problem in which estimation of damping ratio and fundamental vibration modes are required.

• 한국지능시스템학회논문지
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• 제9권5호
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• pp.555-565
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• 1999

복잡하고 비선형적인 시스템의 규칙베이스 퍼지모델링을 위하여 퍼지시스템의 최적 동정알고리즘을 연구한다. 비선형 시스템은 퍼지모델의 입력변수와 퍼지 입력공간 분할에 의한 구조동정과 파라미터 동정을 통해 표현된다. 본 논문에서 규칙베이스 퍼지모델링은 비선형 시스템을 위해 퍼지추론방법과 두 종류의 최적화 이론의 결합에 의한 하이브리드 구졸를 이용하여 시스템 구조와 파라미터동정을 수행한다. 퍼지모델의 추론방법은 간략추론 및 선형추론에 의한다. 제안된 하이브리드 최적 동정 알고리즘은 유전자 알고리즘과 개선된 콤플렉스 방법을 이용한다. 여기서 유전자 알고리즘은 전반부 퍼지규칙의 멤버쉽함수의 초기 파라미터들을 결정하기 위해 사용되고 강력한 자동동조 알고리즘인 개선된 콤플렉스 방법은 정교한 파라미터들을 얻기 위해 수행된다. 따라서 최적 퍼지모델을 위해 전반부 파라미터 동정에는 하이브리드형의 최적 알고리즘을 이용하고 후반부 동정에는 최소자승법을 이용한다. 또한 학습과 테스트 데이터에 의해 생성된 퍼지모델의 성능결과 사이의 상호균형을 얻기 위해 하중계수를 가지는 합성 성능지수를 제안한다. 제안된 모델의 성능평가를 위해 두가지 수치적 예를이용한다.

• 대한기계학회논문집A
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• 제36권7호
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• pp.797-803
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• 2012

비례솔레노이드밸브는 코일전류에 비례하는 전자기력을 이용하여 밸브 변위를 연속적으로 가변시키는 밸브이다. 대용량 비례유량제어밸브는 발전소나 화학 플랜트에서 물, 스팀, 가스 등과 같은 공정유체의 대용량 유량제어에 사용되며 공압이나 모터를 이용하는 밸브에 비하여 우수한 응답성능과 소형화의 장점을 가진다. 본 연구에서는 비례제어밸브를 대상으로 밸브의 동적 특성을 식별한 후 목표 성능이 만족되도록 위치제어기의 비례적분이득을 자동으로 조정하는 기능을 설계하였다. 동특성 식별은 릴레이 피드백을 통하여 한계 안정 상태에서의 임계이득과 임계주기로 파악하였으며, 비례적분이득 결정에는 Ziegler-Nichols 방법을 적용하였다. 구현된 기능은 시험을 통하여 성능을 검증하였으며 밸브 작동점과 릴레이 제어기 변수가 자동조정에 미치는 영향을 분석하였다.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• 한국항공우주학회지
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• 제32권3호
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• pp.45-50
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• 2004

위성의 성공적인 임무 수행을 위한 자세 안정화와 성능요구조건을 만족하기 위해서 반작용휠 제어로직의 설계가 중요하다. 실제 위성궤도 상에서 발생하는 여러 가지 불확실성으로 인해 지상실험을 통해 획득한 모델 파라미터 값들만으로 제어로직을 설계하는데 한계가 있다. 그러므로 위성이 궤도상에 있을 때의 반작용휠 입력 및 출력 데이터를 이용하여 모델 파라미터를 보정하고 자세제어기에 반영하는 것이 요구된다. 본 논문에서는 다목적실용위성의 Telemetry 데이터를 활용한 시스템인식 (System Identification)을 수행하였고, 이를 통한 반작용휠의 모델 파라미터를 추출한다. 또한, 반작용휠을 모델링 하고 또한 제어기설계에 사용된 모델 파라미터를 추출하여 지상실험 데이터와 비교분석한다.

• 대한기계학회논문집
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• 제15권6호
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• pp.1819-1830
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• 1991

본 연구에서는 구조물이 순수한 선형계인 경우에 국한하여 선형 구조물의 여 러지점에서 측정한 신호들을 기존의 방법과 같이 다중입력으로 간주하고 선형 구조물 로부터 방사된 소음, 즉 관측자의 위치에서 측정한 신호를 출력으로 가정한 두 부분 기여도 함수를 적용하여 소음/진동원을 규명하였을 때 발생하는 결과를 이론적으로 해 석하여 보았다.

• Earthquakes and Structures
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• 제8권4호
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• pp.915-934
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• 2015

Within the context of Structural Health Monitoring (SHM), it is often the case that structural systems are described by uncertainty, both with respect to their parameters and the characteristics of the input loads. For the purposes of system identification, efficient modeling procedures are of the essence for a fast and reliable computation of structural response while taking these uncertainties into account. In this work, a reduced order metamodeling framework is introduced for the challenging case of nonlinear structural systems subjected to earthquake excitation. The introduced metamodeling method is based on Nonlinear AutoRegressive models with eXogenous input (NARX), able to describe nonlinear dynamics, which are moreover characterized by random parameters utilized for the description of the uncertainty propagation. These random parameters, which include characteristics of the input excitation, are expanded onto a suitably defined finite-dimensional Polynomial Chaos (PC) basis and thus the resulting representation is fully described through a small number of deterministic coefficients of projection. The effectiveness of the proposed PC-NARX method is illustrated through its implementation on the metamodeling of a five-storey shear frame model paradigm for response in the region of plasticity, i.e., outside the commonly addressed linear elastic region. The added contribution of the introduced scheme is the ability of the proposed methodology to incorporate uncertainty into the simulation. The results demonstrate the efficiency of the proposed methodology for accurate prediction and simulation of the numerical model dynamics with a vast reduction of the required computational toll.

• 제어로봇시스템학회논문지
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• 제12궈1호
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• pp.28-32
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• 2006

Because of the recent development of radio frequency identification (RFID) technologies, various systems for RFID have been proposed. and it expected to become pervasive and ubiquitous. offers tantalizing benefits for supply chain management, inventory control, and many other applications. recently, however, has the convergence of lower cost and increased capabilities made businesses take a hard look at what RFID can do fer them. In this paper, We propose the real-time RFID face recognition system using Hippocampus neuron modeling algorithm(HNMA) and PCA-LDA mixture algorithm. this system store an extracted face-feature in tag and uses for individual authentication.

• 전기학회논문지P
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• 제56권1호
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• pp.28-36
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• 2007

Sensor networks are usually composed of tens or thousands of tiny devices with limited resources. Because of their limited resources, many researchers have studied on the energy management in the WSNs(Wireless Sensor Networks), especially taking into account communications efficiency. For effective data transmission and sensor fault detection in sensor network environment, a new remote monitoring system based on PCA(Principle Component Analysis) and AANN(Auto Associative Neural Network) is proposed. PCA and AANN have emerged as a useful tool for data compression and identification of abnormal data. Proposed system can be effectively applied to sensor network working in LEA2C(Low Energy Adaptive Connectionist Clustering) routing algorithms. To verify its applicability, some simulation studies on the data obtained from real WSNs are executed.