• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.491-494
• /
• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.349-354
• /
• 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.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.1
• /
• pp.538-543
• /
• 2016

For a good product quality and plant safety, it is necessary to implement the on-line monitoring and diagnosis schemes of industrial processes. Combined with monitoring systems, reliable diagnosis schemes seek to find assignable causes of the process variables responsible for faults or special events in processes. This study deals with the real-time diagnosis of complicated industrial processes from the intelligent use of multivariate statistical techniques. The presented diagnosis scheme consists of a classification-based diagnosis using nonlinear representation and filtering of process data. A case study based on the simulation data was conducted, and the diagnosis results were obtained using different diagnosis schemes. In addition, the choice of future estimation methods was evaluated. The results showed that the performance of the presented scheme outperformed the other schemes.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.4
• /
• pp.669-678
• /
• 1994

This paper presents a new data predistortion technique with memory to compensate for the nonlinearities of high-power amplifiers (HPA`s) in digital radio systems employing QAM signal formats. In contrast with the conventional data predistortion technique which is designed to reduce nonlinearity of memoryless HPA`s, the proposed technique in this paper compensates not only for nonlinear warping of the signal constellation but also for clustering of the signal points caused by transmitter pulse sharping filter with memory. A practical implementation method which can reduce the size of memory at the predistortion stage is described by utilizing symmetry of QAM constellation format and Modulo-4 operation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37C no.12
• /
• pp.1289-1295
• /
• 2012

An adaptive digital predistortion (PD) technique is proposed for linearization of power amplifiers (PAs) in multiple-input multiple-output (MIMO) transmitters. We consider a PD structure equipped with only one combined feedback path while conventional systems have multiple feedback paths. Hence, the proposed structure is much simpler than that of multiple feedback paths. Based on the structure, a new PD algorithm is derived. The simulation results show that linearization performance of the proposed method is almost the same as the conventional multiple feedback technique while the former is much simpler to implement than the latter.

• Proceedings of the KIEE Conference
• /
• summer
• /
• pp.1291-1293
• /
• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.9
• /
• pp.512-519
• /
• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.3
• /
• pp.173-181
• /
• 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
• /
• v.8 no.4
• /
• pp.621-652
• /
• 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
• /
• v.6 no.3
• /
• pp.226-234
• /
• 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.