Simulating counting oracles with cooperation

With the advancement of technologies it is becoming imperative to have a stable, secure and uninterrupted supply of power to electronic systems as well as to ensure the identification of faults occurring in these systems quickly and efficiently in case of any accident. Spiking neural P system (SNPS) is a popular parallel distributed computing model. It is inspired by the structure and functioning of spiking neurons. It belongs to the category of neural-like P systems and is well-known as a branch of the third generation neural networks. SNPS and its variants can perform the task of fault diagnosis in power systems efficiently. In this paper, we provide a comprehensive survey of these models, which can perform the task of fault diagnosis in transformers, power transmission networks, traction power supply systems, metro traction power supply systems, and electric locomotive systems. Furthermore, we discuss the use of these models in fault section estimation of power systems, fault location identification in distribution network, and fault line detection. We also discuss a software tool which can perform the task of fault diagnosis automatically. Finally, we discuss future research lines related to this topic.

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A Protocol for Solutions to DP-Complete Problems through Tissue Membrane Systems

Orellana-Martín

Ramírez-de-Arellano

Andreu-Guzmán

et al. 2023

Mathematics

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Considering a class R comprising recognizer membrane systems with the capability of providing polynomial-time and uniform solutions for NP-complete problems (referred to as a “presumably efficient” class), the corresponding polynomial-time complexity class PMCR encompasses both the NP and co – NP classes. Specifically, when R represents the class of recognizer presumably efficient cell-like P systems that incorporate object evolution rules, communication rules, and dissolution rules, PMCR includes both the DP and co – DP classes. Here, DP signifies the class of languages that can be expressed as the difference between any two languages in NP (it is worth noting that NP ⊆ DP and co – NP ⊆ co – DP). As DP-complete problems are believed to be more complex than NP-complete problems, they serve as promising candidates for studying the P vs NP problem. This outcome has previously been established within the realm of recognizer P systems with active membranes. In this paper, we extend this result to encompass any class R of presumably efficient recognizer tissue-like membrane systems by presenting a detailed protocol for transforming solutions of NP-complete problems into solutions of DP-complete problems.

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Simulating counting oracles with cooperation

Cited by 3 publications

References 20 publications

Depth-two P systems can simulate Turing machines with NP oracles

Depth-two P systems can simulate Turing machines with NP oracles

A Review of Power System Fault Diagnosis with Spiking Neural P Systems

A Protocol for Solutions to DP-Complete Problems through Tissue Membrane Systems

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