A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights

Song, Tao; Pang, Shanchen; Hao, Shaohua; Rodríguez‐Patón, Alfonso; Zheng, Pan

doi:10.1007/s11063-018-9947-9

Cited by 86 publications

(39 citation statements)

References 41 publications

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“…This should promote further development of DNA storage, using our successful improvement of this algorithm. In addition, the constructed reliable DNA coding set can also be applied to other fields of DNA, such as DNA neural network computing model [56], DNA coding image encryption [57], DNA parallel computing model [58,59], Using the DNA storage architecture to make embedded storage materials [60], etc., which has a wide range of application value.…”

Section: Discussionmentioning

confidence: 99%

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Yin

Cao

et al. 2020

IJMS

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The high density, large capacity, and long-term stability of DNA molecules make them an emerging storage medium that is especially suitable for the long-term storage of large datasets. The DNA sequences used in storage need to consider relevant constraints to avoid nonspecific hybridization reactions, such as the No-runlength constraint, GC-content, and the Hamming distance. In this work, a new nonlinear control parameter strategy and a random opposition-based learning strategy were used to improve the Harris hawks optimization algorithm (for the improved algorithm NOL-HHO) in order to prevent it from falling into local optima. Experimental testing was performed on 23 widely used benchmark functions, and the proposed algorithm was used to obtain better coding lower bounds for DNA storage. The results show that our algorithm can better maintain a smooth transition between exploration and exploitation and has stronger global exploration capabilities as compared with other algorithms. At the same time, the improvement of the lower bound directly affects the storage capacity and code rate, which promotes the further development of DNA storage technology.

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Section: Discussionmentioning

confidence: 99%

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Yin

Cao

et al. 2020

IJMS

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“…Dietze [ 13 ] proposed biological ontologies such as the gene ontology (GO) and the human phenotype ontology (HP) that provided a rich set of constructs for describing biological entities such as genes, alleles and diseases. Overton et al [ 14 ] proposed to use XOD strategy and powerful XOD tool development to greatly support ontology interoperability and powerful ontology applications to support searchable, accessible, interoperable and reusable data. With the continuous improvement of the information level in the petroleum field, although the ontology storage can be applied to many fields, the rapid growth of data volume will bring more problems to the ontology storage and retrieval.…”

Section: Related Workmentioning

confidence: 99%

Neo4j graph database realizes efficient storage performance of oilfield ontology

Gong

et al. 2018

PLoS ONE

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The integration of oilfield multidisciplinary ontology is increasingly important for the growth of the Semantic Web. However, current methods encounter performance bottlenecks either in storing data and searching for information when processing large amounts of data. To overcome these challenges, we propose a domain-ontology process based on the Neo4j graph database. In this paper, we focus on data storage and information retrieval of oilfield ontology. We have designed mapping rules from ontology files to regulate the Neo4j database, which can greatly reduce the required storage space. A two-tier index architecture, including object and triad indexing, is used to keep loading times low and match with different patterns for accurate retrieval. Therefore, we propose a retrieval method based on this architecture. Based on our evaluation, the retrieval method can save 13.04% of the storage space and improve retrieval efficiency by more than 30 times compared with the methods of relational databases.

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“…Many variants of SNPS have been introduced by incorporating features from the biological neurons such as asynchronous systems [8], astrocytes [9], rule on synapses [10], communication on request [11,12], synapses with schedules [13], structural plasticity [14], weighted synapses [15], inhibitory synapses [16], anti-spikes [17], etc. These models have also been used in solving problems related to real-life applications, such as fault diagnosis of power systems [18][19][20][21][22][23][24][25][26][27][28][29][30][31], pattern recognition [32][33][34], computational biology [35], performing arithmetic and logical operations and hardware implementation [36][37][38][39][40][41][42][43][44][45][46][47], solving computational hard problems [18,[48][49][50][51][52]…”

Section: Introductionmentioning

confidence: 99%

On Applications of Spiking Neural P Systems

Fan

Paul

et al. 2020

Applied Sciences

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Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations.

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A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights

Cited by 86 publications

References 41 publications

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Neo4j graph database realizes efficient storage performance of oilfield ontology

On Applications of Spiking Neural P Systems

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