Analysis of Membrane Process Model from Black Box to Machine Learning

The membrane processes include the complex frameworks, typically integrating various physio-chemical aspects, and the biological activities, based on the systems researched. In that regard, the process modeling is essential to predict and simulate the process and the performance of membranes, to infer concerning the optimum process aspects, meant to analyze fouling developments, and principally, the controls and monitoring of processes. Irrespective of the real terminological dissemination such as Machine Learning (ML), the application of computing instruments to the processes of model membrane was considered in the past are insignificant from the scholarly perspective, not contributing to our knowledge of the aspects included. Irrespective of the controversies, in the past two decades, non-mechanistic and data-driven modeling is applicable to illustrate various membrane process, and in the establishment of novel tracking and modeling approaches. In that regard, this paper concentrates on the provision of a custom aspect regarding the use of Non-Mechanistic Modeling (NMM) in membrane processing, assessing the transformations endorsed by our experience, accomplished as a research segment operational in the membrane process segment. Furthermore, the guidelines are the problems for the application of the state-of-the-art computational instruments Membrane Computing (MC).

P Systems with Evolutional Communication and Division Rules

A widely studied field in the framework of membrane computing is computational complexity theory. While some types of P systems are only capable of efficiently solving problems from the class P, adding one or more syntactic or semantic ingredients to these membrane systems can give them the ability to efficiently solve presumably intractable problems. These ingredients are called to form a frontier of efficiency, in the sense that passing from the first type of P systems to the second type leads to passing from non-efficiency to the presumed efficiency. In this work, a solution to the SAT problem, a well-known NP-complete problem, is obtained by means of a family of recognizer P systems with evolutional symport/antiport rules of length at most (2,1) and division rules where the environment plays a passive role; that is, P systems from CDEC^(2,1). This result is comparable to the one obtained in the tissue-like counterpart, and gives a glance of a parallelism and the non-evolutionary membrane systems with symport/antiport rules.

Time-free Solution to Independent Set Problem using P Systems with Active Membranes

Membrane computing is a branch of natural computing aiming to abstract computing models from the structure and functioning of living cells. The computation models obtained in the field of membrane computing are usually called P systems. P systems have been used to solve computationally hard problems efficiently on the assumption that the execution of each rule is completed in exactly one time-unit (a global clock is assumed for timing and synchronizing the execution of rules). However, in biological reality, different biological processes take different times to be completed, which can also be influenced by many environmental factors. In this work, with this biological reality, we give a time-free solution to independent set problem using P systems with active membranes, which solve the problem independent of the execution time of the involved rules.

Probabilistic Membrane Computing-Based SLAM for Patrol UAVs in Coal Mines

Patrol unmanned aerial vehicles (UAVs) in coal mines have high requirements for environmental perception. Because there are no GPS signals in a mine, it is necessary to use simultaneous localization and mapping (SLAM) to realize environmental perception for UAVs. Combined with complex coal mine environments, an integrated navigation algorithm for unmanned helicopter inertial measurement units (IMUs), light detection and ranging (LiDAR) systems, and depth cameras based on probabilistic membrane computing-based SLAM (PMC-SLAM) is proposed. First, based on an analysis of the working principle of each sensor, the mathematical models for the corresponding sensors are given. Second, an algorithm is designed for the membrane, and a probabilistic membrane system is constructed. The probabilistic SLAM map is constructed by sparse filtering. The experimental results show that PMC can further improve the accuracy of map construction. While adapting to the trend of intelligent precision mining in coal mines, this approach provides theoretical support and application practice for coal mine disaster prevention and control.