A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk Abstract-This letter presents a fault detection method for modular multilevel converters (MMC) which is capable of locating a faulty semiconductor switching device in the circuit. The proposed fault detection method is based on a sliding mode observer (SMO) and a switching model of a half-bridge, the approach taken is to conjecture the location of fault, modify the SMO accordingly and then compare the observed and measured states to verify, or otherwise, the assumption. This technique requires no additional measurement elements and can easily be implemented in a DSP or micro-controller. The operation and robustness of the fault detection technique are confirmed by simulation results for the fault condition of a semiconductor switching device appearing as an open-circuit.

The avalanche of newly found protein sequences in the post-genomic era has motivated and challenged us to develop an automated method that can rapidly and accurately predict the localization of an uncharacterized protein in cells because the knowledge thus obtained can greatly speed up the process in finding its biological functions. However, it is very difficult to establish such a desired predictor by acquiring the key statistical information buried in a pile of extremely complicated and highly variable sequences. In this paper, based on the concept of the pseudo amino acid composition (Chou, K. C. PROTEINS: Structure, Function, and Genetics, 2001, 43: 246-255), the approach of cellular automata image is introduced to cope with this problem. Many important features, which are originally hidden in the long amino acid sequences, can be clearly displayed through their cellular automata images. One of the remarkable merits by doing so is that many image recognition tools can be straightforwardly applied to the target aimed here. High success rates were observed through the self-consistency, jackknife, and independent dataset tests, respectively.

A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419-424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their "fingerprint". It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246-255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.

The structural class is an important feature widely used to characterize the overall folding type of a protein. How to improve the prediction quality for protein structural classification by effectively incorporating the sequence-order effects is an important and challenging problem. Based on the concept of the pseudo amino acid composition [Chou, K. C. Proteins Struct Funct Genet 2001, 43, 246; Erratum: Proteins Struct Funct Genet 2001, 44, 60], a novel approach for measuring the complexity of a protein sequence was introduced. The advantage by incorporating the complexity measure factor into the pseudo amino acid composition as one of its components is that it can catch the essence of the overall sequence pattern of a protein and hence more effectively reflect its sequence-order effects. It was demonstrated thru the jackknife crossvalidation test that the overall success rate by the new approach was significantly higher than those by the others. It has not escaped our notice that the introduction of the complexity measure factor can also be used to improve the prediction quality for, among many other protein attributes, subcellular localization, enzyme family class, membrane protein type, and G-protein couple receptor type.

Recent advances in large-scale genome sequencing have led to the rapid accumulation of amino acid sequences of proteins whose functions are unknown. Because the functions of these proteins are closely correlated with their subcellular localizations, it is vitally important to develop an automated method as a high-throughput tool to timely identify their subcellular location. Based on the concept of the pseudo amino acid composition by which a considerable amount of sequence-order effects can be incorporated into a set of discrete numbers (Chou, K. C., Proteins: Structure, Function, and Genetics, 2001, 43: 246-255), the complexity measure approach is introduced. The advantage by incorporating the complexity measure factor as one of the pseudo amino acid components for a protein is that it can more effectively reflect its overall sequence-order feature than the conventional correlation factors. With such a formulation frame to represent the samples of protein sequences, the covariant-discriminant predictor (Chou, K. C. and Elrod, D. W., Protein Engineering, 1999, 12: 107-118) was adopted to conduct prediction. High success rates were obtained by both the jackknife cross-validation test and independent dataset test, suggesting that introduction of the concept of the complexity measure into prediction of protein subcellular location is quite promising, and might also hold a great potential as a useful vehicle for the other areas of molecular biology.