In this paper, a microcontroller-based temperature controller for furnaces and ovens is introduced. Improvements are attained on the two basic problems of the controllers in naturally-cooled furnaces, namely, the osci...
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In this paper, a microcontroller-based temperature controller for furnaces and ovens is introduced. Improvements are attained on the two basic problems of the controllers in naturally-cooled furnaces, namely, the oscillations caused by integral control and the variation in system parameters depending on the materials inside the furnace. A low-cost microcontroller is used for the effectiveness and simplicity of the control algorithm.
The promise of new architectures and more cost-effective miniaturization has prompted interest in hybrid molecular and semiconductor computers. Nature has already optimized through serendipitous natural selection some...
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The promise of new architectures and more cost-effective miniaturization has prompted interest in hybrid molecular and semiconductor computers. Nature has already optimized through serendipitous natural selection some molecules for such applications. We examine here the use of the protein bacteriorhodopsin in three-dimensional optical memories. By using a sequential one-photon process, parallel read and write processes can be carried out without disturbing data outside of the irradiated volume. We examine the architecture and the methods currently under study to enhance the relevant photophysical properties of the protein.
作者：
Zimmermann, W[?]Prof. Dri-Ing. Werner Zimmermann (1956)
VDE. received his Dip1.-Ing. and Dr.-Ing. degrees in Electrical Engineering from the University of Stuttgart Germany. At the Institute for Power Electronics of the same University he worked in the field of electrical drives power electronics and control. In 1987 he joined Robert Bosch GmbH. As department manager he was responsible for the hardware and software development of electronic control units for diesel-powered passenger cars and trucks. Since 1993 he is Professor for computer and control engineering at Esslingen Polytechnic. He received the VDE-ETG prize for technical publications in 1989. (Fachhochschule fur Technik Esslingen FB Nachrichtentechnik Flandernstra Be 101 D-73732 Esslingen T +711/397-3749 Fax +7111397-3792)
In many applications inverter-fed AC machines with high dynamic performance are needed, but position control is not required. Conventional field-oriented control fulfils the dynamic requirements, but usually needs a c...
In many applications inverter-fed AC machines with high dynamic performance are needed, but position control is not required. Conventional field-oriented control fulfils the dynamic requirements, but usually needs a costly position or speed sensor. Simple voltage-frequency control avoids these sensors, but will only achieve a moderate dynamic performance. For such applications a self controlled scheme based on the measured flux vector may be used. In this paper flux phase control loop and a method of backing the flux model with an observer to improve the behaviour at low stator frequencies are described.
In this paper, we proposed a discrete-time cellular neural network (DTCNN) structure for the labelling of digital images. First, we present the concept and the structure of reversible DTCNN, which can be used to gener...
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In this paper, we proposed a discrete-time cellular neural network (DTCNN) structure for the labelling of digital images. First, we present the concept and the structure of reversible DTCNN, which can be used to generate 2D binary random image sequences. Then both the original image and the copyright label, which is often another binary image, are used to generate a binary random key image. The key image is then used to scramble the original image. Due to the reversibility of a reversible DTCNN, the same reversible DTCNN is used to recover the copyright label from a labelled image. Due to the high speed of a DTCNN chip, our method can be used to label image sequences, e.g., video sequences, in real time. computer simulation results are presented.
This paper presents significant new results on the stability and convergence properties of a general class of iterative learning control schemes derived using two-dimensional(2D) systems theory. These results apply fo...
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This paper presents significant new results on the stability and convergence properties of a general class of iterative learning control schemes derived using two-dimensional(2D) systems theory. These results apply for a general learning law which (explicitly) uses information from previous iterations or trials. A key feature of these results is that they are expressed in terms of standard linear systems theoretic properties, such as relative degree and the location of the zeros.
Repetitive, or multipass, processes are uniquely characterized by a series of sweeps, or passes through a set of dynamics defined over the so-called pass length which is finite and constant. The unique systems theoret...
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Repetitive, or multipass, processes are uniquely characterized by a series of sweeps, or passes through a set of dynamics defined over the so-called pass length which is finite and constant. The unique systems theoretic/control problem is that the sequence of outputs, or pass profiles, can contain oscillations which increase in amplitutde in the pass to pass direction. These processes can be modelled as a class of quarter plane causal 2D linear systems and this paper shows that the boundary (or pass initial) conditions alone can destabilize them. Hence they must be 'adequately modelled' in a given application and it is the boundary conditions which essentially distinguish the dynamic behaviour of linear repetitive processes from other classes of 2D linear systems.
Computing the trajectories generated by an arbitrary system or process is extremely important for its analysis, especially for control and stability investigations. This paper analyses the fundamental matrix sequence ...
Computing the trajectories generated by an arbitrary system or process is extremely important for its analysis, especially for control and stability investigations. This paper analyses the fundamental matrix sequence (a discrete counterpart of the transition matrix in the continous case) for a linear unit memory repetitive process. The main result refers to the representation of the repetitive process in terms of the general singular Kurek model.
In this letter, we study the use of adaptive controllers to maintain the synchronization of two Chua's oscillators when the channel and circuit parameters are time-varying. We present both computer simulation resu...
In this letter, we study the use of adaptive controllers to maintain the synchronization of two Chua's oscillators when the channel and circuit parameters are time-varying. We present both computer simulation results and physical experimental results to verify the operation of the designs.
Reactive ion etching is an important process in the fabrication of microelectronic devices. This article reports on work in progress towards developing a strategy for controlling sidewall profile during this process. ...
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Reactive ion etching is an important process in the fabrication of microelectronic devices. This article reports on work in progress towards developing a strategy for controlling sidewall profile during this process. In this strategy, a response surf...
An improved neural-net approach based on a combined unsupervised/supervised learning concept is proposed. A 'moving window' procedure is applied to the most recent load and weather information for creating tra...
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An improved neural-net approach based on a combined unsupervised/supervised learning concept is proposed. A 'moving window' procedure is applied to the most recent load and weather information for creating training set data base. A forecasting lead time that varies from 16 hours to 88 hours is introduced to produce the short term electric load forecasting that meets requirements of real electric utility operating practice. The unsupervised learning (UL) is used to identify days with similar daily load patterns. A feed forward three-layer neural net is designed to predict 24-hour loads within the supervised learning (SL) phase. The effectiveness of proposed methods is demonstrated by comparison of forecasted hourly loads in every single day during 1991 with data realized in the same period in the Electric Power Utility of Serbia (EPS). A better choice of input features and more appropriate training set selection procedure allow significant improvement in forecasting results comparing with our previous UL/SL concept characterized by a fixed neural-net structure and absence of re-training procedure. The improvement is illustrated by reduction of average error in daily energy forecasting for 0.83% and reduction of 90th percentile of 2.04%.
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