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This paper presents a general method combining microwave empirical/equivalent model with artificial neural networks for efficient modeling of microwave components. The method, called Generalized Knowledge-Based Neural Network (GKBNN), unifies several existing methods and provides increased model accuracy and extrapolation capability, even if the training data is limited. It is applied to microwave...
Although the railway transport is regarded as a relatively safe transportation tool, many railway accidents have still happened worldwide. In this research, an image-based 3D scene reconstruction framework was proposed to help railway accident emergency rescues. Based on the improved constrained non-linear least square optimization, the framework can automatically model the accident scene with only...
This paper presents batch-to-batch iterative learning control (ILC) of a fed-batch fermentation process using batch-wise linearised models identified from process operation data. The newly obtained process operation data after each batch is added to the historical data base and an updated linearised model is adaptively identified. In an effort to adapt to the current process environment, the updated...
This paper provides an overview of a fast modeling approach for modeling the nonlinear IO buffers for signal integrity based simulation and design of high-speed electronic interconnect and packages. Techniques based on artificial neural network (ANN) modeling are developed, where the neural network is trained to learn from IO buffer data, and trained neural network becomes fast models representing...
A compact model has been developed to capture the variability of flicker noise resulting from the reduction in size of state of the art MOSFETs. The underlying physics of flicker noise in small area MOSFETs has been verified by two means: Monte Carlo simulation and analytic modeling. The statistical distribution of flicker noise is reported for the first time, supported by experimental data from two...
A key challenge in real-world structural health monitoring (SHM) is diversity of damage phenomena and variability in environmental and operational conditions. Conventional learning techniques, while adequate for moderately complex inference tasks, can be limiting in highly complex and rapidly changing environments, especially when insufficient data is available. We present an adaptive learning methodology...
Fractional abundances predicted for a given pixel using spectral mixture analysis (SMA) are most accurate when only the spectral endmembers that comprise it are used, with larger errors occurring if inappropriate endmembers are included in the mixing process. Thus, in order to produce accurate results from spectral mixture analysis it is necessary to acquire representative endmember spectra of all...
In this paper, we propose a new sentence selection method using large written text corpora to augment the language model of conversational speech recognition in order to resolve the insufficiency of in-domain training data coverage in conversational speech recognition. In the proposed method, the large written text corpora are clustered by an entropy-based method. Clusters similar to the target development...
Chip multi-processor exploits both instruction-level and thread-level parallelism effectively. In a typical chip multi-processor architecture, L2 cache is shared by multiple cores. Sharing the L2 cache allows high cache utilization and avoids duplicating cache hardware resources. Unfortunately, the mis-predictions of any processor core could lead the load miss from the wrong path to write some useless...
Currently, combining multiple neural networks appears to be a very promising approach in improving neural network generalisation since it is very difficult, if not impossible, to develop a perfect single neural network. In this paper, individual networks are developed from bootstrap re-samples of the original training and testing data sets. Instead of combining all the developed networks, this paper...
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