The reliability of artificially generated DNA molecules is a key factor for applications which depend on DNA-based technologies, such as DNA computing or nanotechnology. In those cases, interactions between sequences ...
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The reliability of artificially generated DNA molecules is a key factor for applications which depend on DNA-based technologies, such as DNA computing or nanotechnology. In those cases, interactions between sequences have to be controlled to avoid undesirable reactions. In the specific case of molecular computing, the design of robust sets of sequences prevent from incorrect computations because DNA sequences are designed in order to avoid potentially conflicting interactions between the DNA molecules within the artificially generated library. However, the design of reliable DNA libraries which can be used for molecular computing involves several heterogeneous and conflicting design criteria that cannot be properly modeled by using traditional optimization algorithms. In this paper, we formulate the problem as a multiobjective optimization problem and we solve it with a novel multiobjectivealgorithm based on the behaviour of fireflies. Specifically, our approach, multiobjective firefly algorithm (MO-FA), works with six different conflicting design criteria that measure the reliability of the generated sequences. Furthermore, in order to compare our results in multiobjective terms, we have also developed and adjusted the well-known fast non-dominated sorting genetic algorithm (NSGA-II). Results show that our proposal obtains very satisfactory results. In fact, the reliability of DNA sequences generated significantly surpasses the reliability of sequences obtained with other approaches previously published in the literature. (C) 2013 Elsevier Inc. All rights reserved.
This study aims to perform fast fault diagnosis and intelligent protection in an active distribution network (ADN) with high renewable energy penetration. Several time -domain simulations are carried out in EMTP-RV to...
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This study aims to perform fast fault diagnosis and intelligent protection in an active distribution network (ADN) with high renewable energy penetration. Several time -domain simulations are carried out in EMTP-RV to extract time -synchronized current and voltage data. The Stockwell transform (ST) was used in MATLAB/SIMULINK to preprocess these input datasets to train the adaptive fault diagnosis deep convolutional neural network (AFDDCNN) for fault location identification, fault type identification, and fault phase -detection for different penetration levels. Based on the AFDDCNN output, the intelligent protection scheme (IDOCPS) generates the signal for isolating a faulty section of the ADN. An intelligent fault diagnosis scheme that combines ST and deep learning methods aids the artificial intelligence -based protection scheme in isolating the faulty section. This study uses the PyTorch framework to build both the AFDDCNN and IDOCPS. The proposed protection technique classifies and isolates faults and coordinates protection with minimum operating time in the IEEE 13 -bus ADN. It consistently gives high accuracy for fault diagnosis and minimum operating time for the IDOCPS even when the network's topology is modified to the IEEE 34 -bus ADN. The experimental results indicate that the proposed model is more accurate and provides faster fault diagnosis and isolation than state-of-the-art methods.
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