• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.349-354
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• 2006

For robust INS/GPS navigation system, an efficient multi-filter fusion technique is proposed. In the filtering for nonlinear systems, the representative filter - EKF, and the alternative filters - RHKF filter, SPKF, etc. have individual advantages and weak points. The key concept of the multi-filter fusion is the mergence of the strong points of the filters. This paper fuses the IIR type filter - EKF and the FIR type filter - RHKF filter using the adaptive strategy. The result of the fusion has several advantages over the EKF, and the RHKF filter. The advantages include the robustness to the system uncertainty, temporary unknown bias, and so on. The multi-filter fusion technique is applied to the tightly coupled INS/GPS navigation system and the performance is verified by simulation.

• 한국산학기술학회논문지
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• 제17권1호
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• pp.538-543
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• 2016

고품질의 제품과 조업 안전을 확보하기 위해서는 적절한 실시간 공정 감시 및 진단 시스템이 설치되어있는 것이 무엇보다 중요하다. 공정 감시 시스템과 결합된 신뢰도 높은 진단 시스템은 공정에서 발생한 특별한 사건이나 사고의 근본적인 원인과 공정 변수를 알려준다. 본 연구에서는 다변량 통계 분석과 분류기법에 기반한 공정진단 체계를 제시한다. 이 진단시스템은 비선형 데이터 표현과 필터링을 통한 지능적 데이터 표현으로 구성되어 있다. 진단 성능을 평가하기 위해 사례연구를 수행하였으며 다른 방법론과의 결과를 비교하기 위하여 진단 결과와 미래값 추정 방법을 평가하였다. 그 결과 본 연구에서 비교된 진단 방법론들에 비해 신뢰도 높은 진단 결과를 얻을 수 있었다.

• 한국통신학회논문지
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• 제19권4호
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• pp.669-678
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• 1994

본 논문에서는 QAM 신호를 전송하는 디지털 통신 시스템에서 고출력 증폭기(HPA)의 비선형성을 보상하기 위한 메모리 있는 데이터 사전왜곡 방법을 제안한다. 메모리 없는 HPA의 비선형성을 줄이기 위해 구현된 종래의 데이터 사전왜곡 방법에 비해, 본 논문에서 제안된 방법은 신호 성상도의 비선형 왜곡(warping)을 줄여 줄 뿐만 아니라 메모리 있는 전송 펄스 형성 필터로 인해 일어나는 심볼의 군집(cluster)을 원래의 심볼로 보상한다. 본 논문에서는 사전왜곡단의 메모리 크기를 줄이기 위해 QAM 신호 성상도의 대칭성을 고려하여 modulo-4 연산을 이용한다.

• 한국통신학회논문지
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• 제37C권12호
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• pp.1289-1295
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• 2012

본고에서는 MIMO 송신기에서 비선형 전력증폭기를 선형화하기 위한 디지털 전치왜곡 기법을 제안한다. 기존의 시스템에서는 각 전력증폭기에 한 개씩 되먹임 회로가 필요한 반면 본고에서 제안하는 전치왜곡 시스템은 전력증폭기 출력 신호를 모두 결합하여 한 개의 되먹임 회로만 가지는 특징이 있다. 따라서 기존 시스템에 비해 훨씬 간단한 구조를 가진다. 이러한 구조를 바탕으로 결합된 피드백 신호로부터 각 전력증폭기를 선형화하는 전치왜곡 알고리즘을 제안한다. 모의실험 결과에 의하면 제안된 방식은 각 전력증폭기에 하나씩 되먹임 회로를 구성한 기존 방식과 거의 동일한 선형화 특성을 보임을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 B
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• pp.1291-1293
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• 2004

The control method of programmable dynamometer for overall test of machine is to load the reference torque which is computed from torque transducer into motor under test. But the torque information detected from torque transducer have a lot of noise when the load torque of meter is a small quantity or changing. Thus, torque transducer must have a low pass filter to detect a definite torque information. But The torque delay generated by filter with torque transducer occur a torque trouble for moter torque of programmable dynamometer. Therefore, this kind of system could not perform dynamic and nonlinear load. In this paper, the control method using the load torque observer without a measure for torque transducer is proposed. The proposed system improved the problem of the torque measuring delay with torque transducer, and the load torque is estimated by the minimal order state observer based on the torque component of the vector control induction meter. Therefore, the torque controller is not affected by a load torque disturbance.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권9호
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권3호
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• pp.173-181
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• 2004

In normal robot control systems without any actuator failures, it is assumed that actuator torque coefficients applied at each joint have normally 1's all the time. However, it is more practical that actuator torque coefficients applied at each joint are nonlinear time-varying. In other words, it has to be considered that actuators equipped at joints may fail due to hardware or software faults. In this work, actuator torque coefficients are assumed to have non-zero values at all joints. In the case of an actuator torque coefficient which has a zero value at a joint, it means the complete loss of torque on the joint. This paper doesn't deal with the case. As factors of performance degradation of robots, both actuator failures and uncertainties are considered in this paper at the same time. This paper proposes a robust adaptive fault-tolerant control scheme to maintain the required performance and achieve task completion for robot manipulators with performance degradation due to actuator failures and uncertainties. Simulation results are shown to verify the fault tolerance and robustness of the Proposed control scheme.

• Journal of Information Processing Systems
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• 제8권4호
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• pp.621-652
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• 2012

Software Estimations provide an inclusive set of directives for software project developers, project managers, and the management in order to produce more realistic estimates based on deficient, uncertain, and noisy data. A range of estimation models are being explored in the industry, as well as in academia, for research purposes but choosing the best model is quite intricate. Estimation by Analogy (EbA) is a form of case based reasoning, which uses fuzzy logic, grey system theory or machine-learning techniques, etc. for optimization. This research compares the estimation accuracy of some conventional data mining models with a hybrid model. Different data mining models are under consideration, including linear regression models like the ordinary least square and ridge regression, and nonlinear models like neural networks, support vector machines, and multivariate adaptive regression splines, etc. A precise and comprehensible predictive model based on the integration of GRA and regression has been introduced and compared. Empirical results have shown that regression when used with GRA gives outstanding results; indicating that the methodology has great potential and can be used as a candidate approach for software effort estimation.

• Journal of Power Electronics
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• 제6권3호
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• pp.226-234
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• 2006

Photovoltaic (PV) generators have nonlinear V-I characteristics and maximum power points which vary with illumination level and temperature. Using a maximum power point tracker (MPPT) with an intermediate converter can increase the system efficiency by matching the PV systems to the load. This paper presents a maximum power point tracker based on fuzzy logic and a control scheme for a single-phase inverter connected to the utility grid. The fuzzy logic controller (FLC) provides an adaptive nature for system performance. Also the FLC provides excellent features such as fast response, good performance and the ability to change the fuzzy parameters to improve the control system. A single-phase AC-DC inverter is used to connect the PV system to the grid utility and local loads. While a control scheme is implemented to inject the PV output power to the utility grid at unity power factor and reduced harmonic level. The simulation results have shown the effectiveness of the proposed scheme.