• 대한전기학회논문지:시스템및제어부문D
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• 제51권6호
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• pp.241-251
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• 2002

The effect of sensor faults in direct torque control(DTC) based induction motor drives is analyzed and a new Instrument fault detection isolation scheme(IFDIS) is proposed. The proposed IFDIS, which operated in real-time, detects and isolates the incipient fault(s) of speed sensor and current sensors that provide the feedback information. The scheme consists of an adaptive gain scheduling observer as a residual generator and a special sequential test logic unit. The observer provides not only the estimate of stator flux, a key variable in DTC system, but also the estimates of stator current and rotor speed that are useful for fault detection. With the test logic, the IFDIS has the functionality of fault isolation that only multiple estimator based IFDIS schemes can have. Simulation results for various type of sensor faults show the detection and isolation performance of the IFDIS and the applicability of this scheme to fault tolerant control system design.

• 정보보호학회지
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• 제5권2호
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• pp.32-48
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• 1995

Our paper describes an Intrusion Detection Parallel System(IDPS) which detects an anomaly activity corresponding to the actions that interaction between near detection events. IDES uses parallel inductive approaches regarding the problem of real-time anomaly behavior detection on rule-based system. This approach uses sequential rule that describes user's behavior and characteristics dependent on time. and that audits user's activities by using rule base as data base to store user's behavior pattern. When user's activity deviates significantly from expected behavior described in rule base. anomaly behaviors are recorded. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the parallel inductive system.

• 지능정보연구
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• 제23권1호
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• pp.109-126
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• 2017

최근 지능형 서비스를 제공하기 위해 감정을 인식하기 위한 많은 연구가 진행되고 있다. 특히, 전시 분야에서 관중에게 개인화된 서비스를 제공하기 위해 얼굴표정을 이용한 감정인식 연구가 수행되고 있다. 그러나 얼굴표정은 시간에 따라 변함에도 불구하고 기존연구는 특정시점의 얼굴표정 데이터를 이용한 문제점이 있다. 따라서 본 연구에서는 전시물을 관람하는 동안 관중의 얼굴표정의 변화로부터 감정을 인식하기 위한 예측 모델을 제안하였다. 이를 위하여 본 연구에서는 시계열 데이터를 이용하여 감정예측에 적합한 순차적 인공신경망 모델을 구축하였다. 제안된 모델의 유용성을 평가하기 위하여 일반적인 표준인공신경망 모델과 제안된 모델의 성능을 비교하였다. 시험결과 시계열성을 고려한 제안된 모델의 예측이 더 뛰어남으로 보였다.

• Animal Bioscience
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• 제34권12호
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• pp.1995-2002
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• 2021

Objective: Detection of adulteration in processed meats is an important issue for some countries due to substitution of beef with a cheaper source of protein like poultry. In this study, the presence of chicken meat was investigated using real-time polymerase chain reaction (real-time PCR) and enzyme-linked immunosorbent assay (ELISA) techniques to verify adulteration of fermented sausage samples. Methods: A total of 60 commercial samples were collected from 20 establishments in three replicates including 10 fermented sausage manufacturers and 10 butchers to investigate the presence of chicken meat with the sequential use of real-time PCR and ELISA techniques. In addition, pH, moisture content, water activity and color values of the samples were determined. Results: Both real-time PCR and ELISA showed agreement on the presence or absence of chicken meat in 55 out of 60 fermented sausage samples and chicken meat was identified with both methods in 16 samples. Five samples produced inconsistent results for the presence of chicken meat in the first run. Nevertheless, the presence of chicken meat was verified with both methods when these samples were analyzed for the second time. In addition, the average physico-chemical values of the fermented sausage samples tested positive for chicken meat were not significantly different from some of those fermented sausage samples tested negative for the chicken meat. Conclusion: The sequential use of real-time PCR and ELISA techniques in fermented sausages could be beneficial for the government testing programs to eliminate false negatives for detection of adulteration with chicken meat. Furthermore, consumers should not rely on some of the quality cues including color to predict the adulteration of fermented sausages with chicken meat since there were no statistical differences among some of the samples tested positive and negative for chicken meat.

• ETRI Journal
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• 제36권3호
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• pp.439-449
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• 2014

In this paper, a novel vision-based nighttime vehicle detection approach is presented, combining state machines and downhill simplex optimization. In the proposed approach, vehicle detection is modeled as a sequential state transition problem; that is, vehicle arrival, moving, and departure at a chosen detection area. More specifically, the number of bright pixels and their differences, in a chosen area of interest, are calculated and fed into the proposed state machine to detect vehicles. After a vehicle is detected, the location of the headlights is determined using the downhill simplex method. In the proposed optimization process, various headlights were evaluated for possible headlight positions on the detected vehicles; allowing for an optimal headlight position to be located. Simulation results were provided to show the robustness of the proposed approach for nighttime vehicle and headlight detection.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권10호
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• pp.479-485
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• 2001

In this paper, we introduce a charge pump phase-locked loop (PLL) architecture which employs a precharge phase frequency detector (PFD) and a sequential PFD to achieve a high frequency operation and a fast acquisition. Operation frequency is increased by using the precharge PFD when the phase difference is within $-{\pi}{\sim}{\pi}$ and acquisition time is shortened by using the sequential PFD and the increased charge pump current when the phase difference is larger than ${\pm}{\pi}$. So error detection range of the proposed PLL structure is not limited to $-{\pi}{\sim}{\pi}$ and a high frequency operation and a higher speed lock-up time can be achieved. The proposed PLL was designed using 1.5 ${\mu}m$ CMOS technology with 5V supply voltage to verify the lock in process. The proposed PLL shows successful acquisition for 200 MHz input frequency. On the other hand, the conventional PLL with the sequential PFD cannot operate at up to 160MHz. Moreover, the lock-up time is drastically reduced from 7.0 ${\mu}s\;to\;2.0\;{\mu}s$ only if the loop bandwidth to input frequency ratio is regulated by the divide-by-4 counter during the acquisition process. By virtue of this dual PFDs, the proposed PLL structure can improve the trade-off between acquisition behavior and locked behavior.

• 응용통계연구
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• 제29권4호
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• pp.699-706
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• 2016

검정절차가 생략된 이상치 탐지법은 구조적으로 수렁효과나 가면효과에 취약하기 때문에 다수의 이상치를 제대로 탐지하지 못할 때가 있다. 본 연구에서는 군집화에 의하여 구분된 소수 관찰치군을 이상치로 판정하는 방법에 보완될 검정절차를 다룬다. 이에 관련된 일반적인 방법은 탐지된 이상치 후보군의 개별적인 관찰치에 대해 다양한 종류의 t-검정을 수행하는 것이다. 본 연구에서는 이상치 후보군에 대한 검정을 수행하고 군집나무의 절단기준을 변경시켜 새로운 이상치군을 탐색해 나가는 순차적인 방법을 제안한다. 예제와 모의실험을 통해 제시된 방법과 기존의 방법들을 비교한다.

• 한국컴퓨터정보학회논문지
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• 제18권11호
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• pp.125-136
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• 2013

본 논문에서는 SSL VPN을 기반으로 사용자 인증과 사용자 단말의 무결성을 검증할 수 있는 네트워크 접근제어 기술을 제안한다. 사용자 단말이 VPN을 이용해 내부 네트워크에 접속할 때 사용자 인증과 사용자 단말의 보안패치, 바이러스 백신 등의 보안 서비스를 확인하는 안전성 검사를 수행한다. 그리고 변종의 악성코드를 탐지하기 위해 사용자 단말의 윈도우 API 정보를 통한 행위패턴을 바탕으로 악성코드를 탐지하고, 탐지의 신뢰도를 높이기 위해 순서패턴의 유사도를 비교하여 변종의 악성코드를 탐지하여 외부의 보안 위협으로부터 시스템을 보호한다.

• Wind and Structures
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• 제24권4호
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• pp.385-403
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• 2017

In recent years, the wind energy has played an increasingly important role in national energy sector of many countries. To harvest more electric power, the wind turbine (WT) tower structure becomes physically larger, which may cause more risks during long-term operation. Associated with the great development of WT projects, the number of accidents related to large-scaled WT has also been increased. Therefore, a structural health monitoring (SHM) system for WT structures is needed to ensure their safety and serviceability during operational time. The objective of this study is to develop a hybrid damage detection method for WT tower structures by measuring vibration and impedance responses. To achieve the objective, the following approaches are implemented. Firstly, a hybrid damage detection scheme which combines vibration-based and impedance-based methods is proposed as a sequential process in three stages. Secondly, a series of vibration and impedance tests are conducted on a lab-scaled model of the WT structure in which a set of bolt-loosening cases is simulated for the segmental joints. Finally, the feasibility of the proposed hybrid damage detection method is experimentally evaluated via its performance during the damage detection process in the tested model.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2009년도 정보통신설비 학술대회
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.