RESUMEN
Manually designing a convolutional neural network (CNN) is an important deep learning method for solving the problem of image classification. However, most of the existing CNN structure designs consume a significant amount of time and computing resources. Over the years, the demand for neural architecture search (NAS) methods has been on the rise. Therefore, we propose a novel deep architecture generation model based on Aquila optimization (AO) and a genetic algorithm (GA). The main contributions of this paper are as follows: Firstly, a new encoding strategy representing the CNN coding structure is proposed, so that the evolutionary computing algorithm can be combined with CNN. Secondly, a new mechanism for updating location is proposed, which incorporates three typical operators from GA cleverly into the model we have designed so that the model can find the optimal solution in the limited search space. Thirdly, the proposed method can deal with the variable-length CNN structure by adding skip connections. Fourthly, combining traditional CNN layers and residual blocks and introducing a grouping strategy provides greater possibilities for searching for the optimal CNN structure. Additionally, we use two notable datasets, consisting of the MNIST and CIFAR-10 datasets for model evaluation. The experimental results show that our proposed model has good results in terms of search accuracy and time.
RESUMEN
The traditional parameter estimation methods for photovoltaic (PV) module are strictly limited by the reference standards. On the basis of the double diode model (DDM), this paper proposes a modified PV module that is independent of the reference conditions and can be used for the transformation and reconfiguration of PV module. With respect to the issue of the slow convergence precision and the tendency to trap in the local extremum of the QUATRE algorithm, this research incorporates the QUATRE algorithm with recombination mechanism (RQUATRE) to tackle the problem of parameter estimation for the improved PV modules described above. Simulation data show that the RQUATRE wins 29, 29, 21, 17 and 15 times with the FMO, PIO, QUATRE, PSO and GWO algorithms on the CEC2017 test suite. In addition, in a modified PV module for the parameter extraction problem, the final experimental results achieved a value of 2.99 × 10-3 at RMSE, all better than the accuracy values of the compared algorithms. In the fitting process of IAE, the final values are also all less than 10%, which can satisfy the fitting needs.
RESUMEN
Current research on routing in wireless sensor computing concentrates on increasing the service lifetime, enabling scalability for large number of sensors and supporting fault tolerance for battery exhaustion and broken nodes. A sensor node is naturally exposed to various sources of unreliable communication channels and node failures. Sensor nodes have many failure modes, and each failure degrades the network performance. This work develops a novel mechanism, called Reliable Routing Mechanism (RRM), based on a hybrid cluster-based routing protocol to specify the best reliable routing path for sensor computing. Table-driven intra-cluster routing and on-demand inter-cluster routing are combined by changing the relationship between clusters for sensor computing. Applying a reliable routing mechanism in sensor computing can improve routing reliability, maintain low packet loss, minimize management overhead and save energy consumption. Simulation results indicate that the reliability of the proposed RRM mechanism is around 25% higher than that of the Dynamic Source Routing (DSR) and ad hoc On-demand Distance Vector routing (AODV) mechanisms.
RESUMEN
Insecticide resistance has recently become a critical concern for control of many insect pest species. Genome sequencing and global quantization of gene expression through analysis of the transcriptome can provide useful information relevant to this challenging problem. The oriental fruit fly, Bactrocera dorsalis, is one of the world's most destructive agricultural pests, and recently it has been used as a target for studies of genetic mechanisms related to insecticide resistance. However, prior to this study, the molecular data available for this species was largely limited to genes identified through homology. To provide a broader pool of gene sequences of potential interest with regard to insecticide resistance, this study uses whole transcriptome analysis developed through de novo assembly of short reads generated by next-generation sequencing (NGS). The transcriptome of B. dorsalis was initially constructed using Illumina's Solexa sequencing technology. Qualified reads were assembled into contigs and potential splicing variants (isotigs). A total of 29,067 isotigs have putative homologues in the non-redundant (nr) protein database from NCBI, and 11,073 of these correspond to distinct D. melanogaster proteins in the RefSeq database. Approximately 5,546 isotigs contain coding sequences that are at least 80% complete and appear to represent B. dorsalis genes. We observed a strong correlation between the completeness of the assembled sequences and the expression intensity of the transcripts. The assembled sequences were also used to identify large numbers of genes potentially belonging to families related to insecticide resistance. A total of 90 P450-, 42 GST-and 37 COE-related genes, representing three major enzyme families involved in insecticide metabolism and resistance, were identified. In addition, 36 isotigs were discovered to contain target site sequences related to four classes of resistance genes. Identified sequence motifs were also analyzed to characterize putative polypeptide translational products and associate them with specific genes and protein functions.