Using the transient current switch circuit in parallel with the energizing contacts, the slow decay of the contact current due to thermal fusion of metal was observed just after the contact voltage exceeded the melting contact voltage Um. At that time, the contact voltage was higher than the boiling contact voltage Ub. These results contradict Holm's θ theory. A new melting model of breaking mechanical contact is proposed. The area surrounding a cluster of contacting a-spots melts, the melt metal diffuses, and the contact spot thermally shrinks. Including the metal phase transition from solid to liquid, the increase of contact resistance is introduced to the electric circuit analysis. The numerical analysis agrees qualitatively with measured V-I characteristics.

The very fast transients of micro-gap discharges driven by low voltage electrostatic discharging (ESDs) are investigated in the time domain. We previously developed a 12 GHz wideband measurement setup consisting of a distributed constant line system, however the observed transients due to micro-gap discharges had very fast rise times of 34 ps or less, which reached the limitation on our system. In this paper, we proposed a method for estimating wideband transients beyond the measurement limit by using the transmission loss of a high performance coaxial transmission line. The proposed method is validated by estimating an impulsive voltage waveform with rise/fall time of 16 ps from the waveform measured through a semi-rigid coaxial cable with a length of 10 m.

A Lyapunov-based recurrent neural networks unified power flow controller (UPFC) is developed for improving transient stability of power systems. First, a simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics. Secondly, we study the control configuration of the UPFC with two major blocks: the primary control, and the supplementary control. The primary control is implemented by standard PI techniques when the power system is operated in a normal condition. The supplementary control will be effective only when the power system is subjected by large disturbances. We propose a new Lyapunov-based UPFC controller of the classical single-machine-infinite-bus system for damping enhancement. In order to consider more complicated detailed generator models, we also propose a Lyapunov-based adaptive recurrent neural network controller to deal with such model uncertainties. This controller can be treated as neural network approximations of Lyapunov control actions. In addition, this controller also provides online learning ability to adjust the corresponding weights with the back propagation algorithm built in the hidden layer. The proposed control scheme has been tested on two simple power systems. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.

VI time responses of a conventional electromagnetic relay during breaking contact operations were measured. In a conventional switching circuit, unstable contact resistance, irregular bouncing, and poor reproducibility were confirmed. Using a transient current switch circuit and two sharpened contact electrodes, bouncing during a breaking operation was suppressed, and unstable contact resistance changes and reproducibility of breaking operation were also improved.

This paper presents a new method for classification of underwater transient signals, which employs a binary image pattern of the mel-frequency cepstral coefficients as a feature vector and a feed-forward neural network as a classifier. The feature vector is obtained by taking DCT and 1-bit quantization for the square matrix of the mel-frequency cepstral coefficients that is derived from the frame based cepstral analysis. The classifier is a feed-forward neural network having one hidden layer and one output layer, and a back propagation algorithm is used to update the weighting vector of each layer. Experimental results with underwater transient signals demonstrate that the proposed method is very promising for classification of underwater transient signals.

A new BIST (Built-in Self-test) method for static ADC testing is proposed. The proposed method detects offset, gain, INL (Integral Non-linearity) and DNL (Differential Non-linearity) errors with a low hardware overhead. Moreover, it can solve a transient zone problem which is derived from the ADC noise in real test environments.

A new method is presented in order to improve the transient response of distributed amplifiers. The method is based on fitting the parameters of the distributed amplifier to those of a predesigned lowpass filter. Analytical expressions are derived to show the performance of the new structure. Three distributed amplifiers are designed based on the proposed method and it has been shown that the new method can significantly improve the transient response of the amplifier. It has been shown that the new method can improve the other characteristics of the distributed amplifier too. The effects of parasitic and lossy elements has also been considered and it has been shown that such effects doesn't violate the generality of the proposed theory.

The compound element pseudo-transient analysis (PTA) algorithm is an effective practical method for finding the DC operating point when the Newton-Raphson method fails. It is able to effectively prevent from the oscillation problems compared with conventional PTA algorithms. In this paper, an effective SPICE3 implementation method for the compound element PTA algorithm is proposed. It has the characteristic of not expanding the Jacobian matrix and not changing the Jacobian matrix structure when the pseudo-transient numerical simulation is being done. Thus a high simulation efficiency is guaranteed. The ability of the proposed SPICE3 implementation to avoid the oscillation problems and the simulation efficiency are demonstrated by examples.

An electrooptic near-field mapping system based on a gain-switched distributed feedback (DFB) pulsed laser and a CdTe electrooptic crystal was used for characterizing stationary and transient near-field patterns of conventional and uniplanar compact photonic band gap (UC-PBG) patch antennas. Effect of the UC-PBG structure on reduction in surface waves in the UC-PBG patch antenna was experimentally verified by comparing stationary and transient near-field measurement of the conventional and UC-PBG patch antennas.

This paper deals with a synthesis of a nonautonomous system with a stable limit cycle. We propose a synthesis method of a nonautonomous system whose transient trajectories converge to a prescribed limit cycle. We use receding horizon control to control a transient behavior of the nonautonomous system, and confirm its validity by simulation.

The pseudo-transient method is discussed in this paper as one of practical methods to find DC operating points of nonlinear circuits when the Newton-Raphson method fails. The mathematical description for this method is presented and an effective pseudo-transient algorithm utilizing compound pseudo-elements is proposed. Numerical examples are demonstrated to prove that our algorithm is able to avoid the oscillation problems effectively and also improve the simulation efficiency.

The paper presents an adaptive algorithm named adaptive threshold nonlinear algorithm for use in adaptive filters in the complex-number domain (c-ATNA) in applications to digital QAM systems. Although the c-ATNA is very simple to implement, it makes adaptive filters highly robust against impulse noise and at the same time it ensures filter convergence as fast as that of the well-known LMS algorithm. Analysis is developed to derive a set of difference equations for calculating transient behavior as well as steady-state performance. Experiment with simulations and theoretical calculations for some examples of filter convergence in the presence of Contaminated Gaussian Noise demonstrates that the c-ATNA is effective in combating impulse noise. Good agreement between simulated and theoretical convergence proves the validity of the analysis.

Multi-swing trajectories, which refer to those trajectories which oscillate several cycles and then become unbounded, has been a nuisance in general simulation programs for power system stability study since the corresponding transient stability is very difficult to access accurately. In this letter, two possible models are developed to explain possible scenarios of such multi-swing behaviors. Theoretical investigation has strongly indicated a close relationship between multi-swing instability problems and chaotic behaviors of the power system.

A new simple recovery scheme for transient bit errors in the RAM of a ROM-based embedded system is presented, which exploits the information stored in the ROM. And a new scrubbing technique suitable to the proposed recovery scheme is also presented. With the proposed recovery scheme and scrubbing technique, the reliability of the RAM against transient bit errors can be improved remarkably with no additional extra memory and scrubbing overhead.

We propose a new electric contact device for arc discharge suppression. The functions of conventional electric contacts are categorized into energizing switch contacts and transient current switch contacts. A capacitor is connected in series to a transient current switch. Suppression of power consumption and arc discharge at breaking contacts are proposed, experimentally measured, and theoretically analyzed. The transient V-I characteristics at breaking contacts are controlled by the transient current switch and the capacitor. The transient responses at contacts were numerically derived by SPICE, and the energizing switch contacts voltage could be controlled to less than the minimum arc voltage. Using 2 conventional relays, no arc ignition at breaking contacts was confirmed for 50 V/25 A.

This paper presents the design of a novel method for improvement of the operation of distance relays during capacitive voltage transformer transients using artificial neural network. The proposed module uses voltage and current signals to learn the hidden relationship existing in the input patterns. Simulation studies are preformed and the influence of changing system parameters, such as fault resistance and source impedance is studied. Details of the design procedure and the results of performance studies with the proposed relay are given in the paper. Performance studies results show that the proposed algorithm decreases the effects of CVT transients and is fast and accurate.

Transient scattering from parallel plate waveguide cavities is studied by using the combination of a point matching technique and numerical inversion of Laplace transform. We thoroughly investigate the scattering mechanism for a half-sine pulse and modulated-sine pulse incidence. The advantages and disadvantages on the target recognition are clarified in terms of the internal objects, incident waveforms, and polarizations.

This paper deals with a design problem of an adaptive robust control system for linear systems with structured uncertainties. The control law consists of a state feedback with a fixed gain designed by using the nominal system, a state feedback with an adaptive gain tuned by a parameter adjustment law and a compensation input. We show the parameter adjustment law and that sufficient conditions for the existence of the compensation input are given in terms of linear matrix inequalities (LMIs). Finally, a numerical example is included.

In this paper, we propose a new parallel algorithm for cellular radio channel assignment problem that can help the expanded maximum neural network escape from local minima by introducing a transient chaotic neurodynamics. The goal of the channel assignment problem, which is an NP-complete problem, is to minimize the total interference between the assigned channels needed to satisfy all of the communication needs. The expanded maximum neural model always guarantees a valid solution and greatly reduces search space without a burden on the parameter-tuning. However, the model has a tendency to converge to local minima easily because it is based on the steepest descent method. By adding a negative self-feedback to expanded maximum neural network, we proposed a new parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm then is fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the expanded maximum neural network and the chaotic neurodynamics. Simulations on benchmark problems demonstrate the superior performance of the proposed algorithm over other heuristics and neural network methods.

In this paper, based on maximum neural network, we propose a new parallel algorithm that can escape from local minima and has powerful ability of searching the globally optimal or near-optimum solution for the maximum clique problem (MCP). In graph theory a clique is a completely connected subgraph and the MCP is to find a clique of maximum size of a graph. The MCP is a classic optimization problem in computer science and in graph theory with many real-world applications, and is also known to be NP-complete. Lee and Takefuji have presented a very powerful neural approach called maximum neural network for this NP-complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to the local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm.