In this paper, various aspects in magnetoencephalography (MEG) source localization are studied. To minimize the errors in experimental data, an approximation technique using a polynomial function is proposed. The simulation shows that the proposed technique yields more accurate results. To improve the convergence characteristics in the optimization algorithm, a hybrid algorithm of evolution strategy and sensitivity analysis is applied to the neuromagnetic inverse problem. The effectiveness of the hybrid algorithm is verified by comparison with conventional algorithms. In addition, an artificial neural network (ANN) is applied to find an initial source location quickly and accurately. The simulation indicates that the proposed technique yields more accurate results effectively.

About two thirds of patients admitted to hospitals world-wide suffer from acute abdomen pains of varying degrees of severity. Acute abdomen pain due to appendicitis or pancreatitis usually requires urgent surgical treatment, whereas pain due to heart ischemia or enteroviral infection requires only drug treatment. In general, making an immediate decision about whether or not acute abdomen pain requires urgent surgery is very difficult. This decision becomes even more difficult when the patient is a young child who can't properly describe the abdominal pain. In this case, thermo-visual inspection can alternatively be used to decide whether urgent surgical treatment is necessary to cure the abdominal pain.

In this paper, an experiment for the active vibration control of a cantilever beam uses electromagnet as an actuator and uses a laser sensor to measure the position of the bending beam, constituting a non-contacting control system. A mathematical model of the overall system is derived to analytically design an appropriate controller. Dynamic equations of the electromagnetic actuator and the beam are combined to find the transfer function from the actuator to the sensor. The effectiveness of the obtained model is verified by various experiments and an improper PID controller is designed based on the obtained model. According to analysis, the coefficient of the derivative controller is the most important parameter for handling the performance and the stability margin of the control system. The experimental results of the active control system are compared with those of the open loop system.

Delay and noise in networked control systems are inevitable and can degrade system performance or stability This paper propose a compensation method for networked control systems with network-induced delay and noise using LMI(linear matrix inequality)-based H_\infty optimization. The H_\infty optimization methods have adapted to account for both the time delay and noise effects. Some simulations applied to inverted pendulum with networked control show that the proposed method works well.

In the thermal power plant, there are six manipulated variables: main steam flow, feedwater flow, fuel flow, air flow, spray flow, and gas recirculation flow. There are five controlled variables: generator output, main steam pressure, main steam temperature, exhaust gas density, and reheater steam temperature. Therefore, the thermal power plant control system is a multinput and output system. In the control system, the main steam temperature is typically regulated by the fuel flow rate and the spray flow rate, and the reheater steam temperature is regulated by the gas recirculation flow rate. However, strict control of the steam temperature must be maintained to avoid thermal stress. Maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature versus changes in fuel flow rate, difficulty of control of the main steam temperature control and the reheater steam temperature control system owing to the dynamic response characteristics of changes in steam temperature and the reheater steam temperature, and the fluctuation of inner fluid water and steam flow rates during the load-following operation. Up to the present time, the Proportional-Integral-Derivative Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on the characteristic comparison of the PID controller and the modified 2-DOF PID Controller (Two-Degrees-Freedom Proportional-Integral-Derivative) on the DCS (Distributed Control System). The method is to design an optimal controller that can be operated on the thermal generating plant in Seoul, Korea. The modified 2-DOF PID controller is designed to enable parameters to fit into the thermal plant during disturbances. To attain an optimal control method, transfer function and operating data from start-up, running, and stop procedures of the thermal plant have been acquired. Through this research, the stable range of a 2-DOF parameter for only this system could be found for the start-up procedure and this parameter could be used for the tuning problem. Also, this paper addressed whether an intelligent tuning method based on immune network algorithms can be used effectively in tuning these controllers.

This paper is concerned with properties of a Lyapunov functional of state delay systems. It is shown that if a state delay system has a pure imaginary pole for some state delay, then no Lyapunov functional satisfying a Lyapunov condition exists periodically with respect to change of the state delay. This periodic property is unique in state delay systems and has been well known in the frequency domain stability conditions. However, in the time domain stability conditions using a Lyapunov functional, the periodic property is not known explicitly.

In this paper, we propose the concept of orthonormalized compressed measurement for the stability analysis of discrete linear time-varying Kalman filters. Unlike previous studies that deal with the homogeneous portion of Kalman filters, the proposed Lyapunov method directly deals with the stochastically-driven system. The orthonorrmalized compressed measurement provides information on the a priori state estimate of the Kalman filter at the k-th step that is propagated from the a posteriori state estimate at the previous block of time. Since the complex multiple-step propagations of a candidate Lyapunov function with process and measurement noises can be simplified to a one-step Lyapunov propagation by the orthonormalized compressed measurement, a stochastic radius of attraction can be derived that would be impractically difficult to obtain by the conventional multiple-step Lyapunov method.

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

In this paper we present a generalized directional morphological filtering algorithm for the removal of impulse noise, which is based on a combination of impulse noise detection and a weighted rank-order morphological filtering technique. For salt (or pepper) noise suppression, the generalized directional opening (or closing) filtering of the input signal is selectively used. The detection of impulse noise can be done by the geometrical difference of opening and closing filtering. Simulations show that this new filter has better detail feature preservation with effective noise reduction compared to other nonlinear filtering techniques.

A new methodology is presented to diagnose faults in equipment plasma. This is accomplished by using neural networks as a pattern recognizer of radio frequency (rf) impedance match data. Using a match monitor system, the match data were collected. The monitor system consisted mainly of a multifunction board and a signal flow diagram coded by Visual Designer. Plasma anomaly was effectively represented by electrical match positions. Twenty sets of fault-symptom patterns were experimentally simulated with variations in process factors, which include rf source power, pressure, Ar, and $O_$2 flow rates. As an input to neural networks, two means and standard deviations of positions were used as well as a reflected power. Diagnostic accuracy was measured as a function of training factors, which include the number of hidden neurons, the magnitude of initial weights, and two gradients of neuron activation functions. The accuracy was the most sensitive to the number of hidden neurons. Interaction effects on the accuracy were also examined by performing a 2$^$4 full factorial experiment. The experiments were performed on multipole inductively coupled plasma equipment.

This paper proposes a method for identifying temporal pattern clusters to predict events in time series. Instead of predicting future values of the time series, the proposed method forecasts specific events that may be arbitrarily defined by the user. The prediction is defined by an event characterization function, which is the target of prediction. The events are predicted when the time series belong to temporal pattern clusters. To identify the optimal temporal pattern clusters, fuzzy goal programming is formulated to combine multiple objectives and solved by an adaptive differential evolution technique that can overcome the sensitivity problem of control parameters in conventional differential evolution. To evaluate the prediction method, five test examples are considered. The adaptive differential evolution is also tested for twelve optimization problems.