Application of Neural-Like P Systems With State Values for Power Coordination of Photovoltaic/Battery Microgrids

The power coordination control of a photovoltaic/battery microgrid is performed with a novel bio-computing model within the framework of membrane computing. First, a neural-like P system with state values (SVNPS) is proposed for describing complex logical relationships between different modes of Photovoltaic (PV) units and energy storage units. After comparing the objects in the neurons with the thresholds, state values will be obtained to determine the con guration of the SVNPS. Considering the characteristics of PV/battery microgrids, an operation control strategy based on bus voltages of the point of common coupling and charging/discharging statuses of batteries is proposed. At rst, the SVNPS is used to construct the complicated unit working modes; each unit of the microgrid can adjust the operation modes automatically. After that, the output power of each unit is reasonably coordinated to ensure the operation stability of the microgrid. Finally, a PV/battery microgrid, including two PV units, one storage unit, and some loads are taken into consideration, and experimental results show the feasibility and effectiveness of the proposed control strategy and the SVNPS-based power coordination control models

The Roadmap to Realize Memristive Three-Dimensional Neuromorphic Computing System

Neuromorphic computing, an emerging non-von Neumann computing mimicking the physical structure and signal processing technique of mammalian brains, potentially achieves the same level of computing and power efficiencies of mammalian brains. This chapter will discuss the state-of-the-art research trend on neuromorphic computing with memristors as electronic synapses. Furthermore, a novel three-dimensional (3D) neuromorphic computing architecture combining memristor and monolithic 3D integration technology would be introduced; such computing architecture has capabilities to reduce the system power consumption, provide high connectivity, resolve the routing congestion issues, and offer the massively parallel data processing. Moreover, the design methodology of applying the capacitance formed by the through-silicon vias (TSVs) to generate a membrane potential in 3D neuromorphic computing system would be discussed in this chapter

A Model of Antibiotic Resistance Evolution Dynamics Through P Systems with Active Membranes and Communication Rules

Baquero, F.; Campos Frances, M.; Llorens, C.; Sempere Luna, JM. (2018). A Model of Antibiotic Resistance Evolution Dynamics Through P Systems with Active Membranes and Communication Rules. Lecture Notes in Computer Science. 11270:33-44. https://doi.org/10.1007/978-3-030-00265-7_3S334411270Barbacari, N., Profir, A., Zelinschi, C.: Gene regulatory network modeling by means of membrane computing. In: Proceedings of the 7th International Workshop on Membrane Computing WMC 2006. LNCS, vol. 4361, pp. 162–178 (2006)Besozzi, D., Cazzaniga, P., Cocolo, S., Mauri, G., Pescini, D.: Modeling diffusion in a signal transduction pathway: the use of virtual volumes in P systems. Int. J. Found. Comput. Sci. 22(1), 89–96 (2011)Campos, M.: A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biol. Direct 10(1), 41 (2015)Ciobanu, G., Păun, Gh., Pérez-Jiménez, M.J.: Applications of Membrane Computing. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-29937-8Colomer, M.A., Margalida, A., Sanuy, D., Pérez-Jiménez, M.J.: A bio-inspired model as a new tool for modeling ecosystems: the avian scavengeras a case study. Ecol. Model. 222(1), 33–47 (2011)Colomer, M.A., Martínez-del-Amor, M.A., Pérez-Hurtado, I., Pérez-Jiménez, M.J., Riscos-Núñez, A.: A uniform framework for modeling based on P systems. In: Li, K., Nagar, A.K., Thamburaj, R. (eds.) IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2010), vol. 1, pp. 616–621 (2010)Dassow, J., Păun, Gh.: On the power of membrane computing. TUCS Technical Report No. 217 (1998)Frisco, P., Gheorghe, M., Pérez-Jiménez, M.J. (eds.): Applications of Membrane Computing in Systems and Synthetic Biology. ECC, vol. 7. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-03191-0Păun, Gh.: Computing with membranes. J. Comput. Syst. Sci. 61(1), 108–143 (2000)Păun, Gh.: Membrane Computing: An Introduction. Springer, Heidelberg (2002). https://doi.org/10.1007/978-3-642-56196-2Păun, Gh., Rozenberg, G., Salomaa, A. (eds.): The Oxford Handbook of Membrane Computing. Oxford University Press, Oxford (2010)World Health Organization: Antimicrobial Resistance: Global Report on Surveillance (2014

Efficient computation in rational-valued P systems

In this paper, we describe a new representation for deterministic rational-valued P systems that allows us to form a bridge between membrane computing and linear algebra. On the one hand, we prove that an efficient computation for these P systems can be described using linear algebra techniques. In particular, we show that the computation for getting a configuration in such P systems can be carried out by multiplying appropriate matrices. On the other hand, we also show that membrane computing techniques can be used to get the nth power of a given matrix.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58

Membrane systems with limited parallelism

Membrane computing is an emerging research field that belongs to the more general area of molecular computing, which deals with computational models inspired from bio-molecular processes. Membrane computing aims at defining models, called membrane systems or P systems, which abstract the functioning and structure of the cell. A membrane system consists of a hierarchical arrangement of membranes delimiting regions, which represent various compartments of a cell, and with each region containing bio-chemical elements of various types and having associated evolution rules, which represent bio-chemical processes taking place inside the cell. This work is a continuation of the investigations aiming to bridge membrane computing (where in a compartmental cell-like structure the chemicals to evolve are placed in compartments defined by membranes) and brane calculi (where one considers again a compartmental cell-like structure with the chemicals/proteins placed on the membranes themselves). We use objects both in compartments and on membranes (the latter are called proteins), with the objects from membranes evolving under the control of the proteins. Several possibilities are considered (objects only moved across membranes or also changed during this operation, with the proteins only assisting the move/change or also changing themselves). Somewhat expected, computational universality is obtained for several combinations of such possibilities. We also present a method for solving the NP-complete SAT problem using P systems with proteins on membranes. The SAT problem is solved in O(nm) time, where n is the number of boolean variables and m is the number of clauses for an instance written in conjunctive normal form. Thus, we can say that the solution for each given instance is obtained in linear time. We succeeded in solving SAT by a uniform construction of a deterministic P system which uses rules involving objects in regions, proteins on membranes, and membrane division. Then, we investigate the computational power of P systems with proteins on membranes in some particular cases: when only one protein is placed on a membrane, when the systems have a minimal number of rules, when the computation evolves in accepting or computing mode, etc. This dissertation introduces also another new variant of membrane systems that uses context-free rewriting rules for the evolution of objects placed inside compartments of a cell, and symport rules for communication between membranes. The strings circulate across membranes depending on their membership to regular languages given by means of regular expressions. We prove that these rewriting-symport P systems generate all recursively enumerable languages. We investigate the computational power of these newly introduced P systems for three particular forms of the regular expressions that are used by the symport rules. A characterization of ET0L languages is obtained in this context

Spiking Neural P Systems: Stronger Normal Forms

Spiking neural P systems are computing devices recently introduced as a bridge between spiking neural nets and membrane computing. Thanks to the rapid research in this eld there exists already a series of both theoretical and application studies. In this paper we focus on normal forms of these systems while preserving their computational power. We study combinations of existing normal forms, showing that certain groups of them can be combined without loss of computational power, thus answering partially open problems stated in. We also extend some of the already known normal forms for spiking neural P systems considering determinism and strong acceptance condition. Normal forms can speed-up development and simplify future proofs in this area

Uniformity is weaker than semi-uniformity for some membrane systems

We investigate computing models that are presented as families of finite computing devices with a uniformity condition on the entire family. Examples of such models include Boolean circuits, membrane systems, DNA computers, chemical reaction networks and tile assembly systems, and there are many others. However, in such models there are actually two distinct kinds of uniformity condition. The first is the most common and well-understood, where each input length is mapped to a single computing device (e.g. a Boolean circuit) that computes on the finite set of inputs of that length. The second, called semi-uniformity, is where each input is mapped to a computing device for that input (e.g. a circuit with the input encoded as constants). The former notion is well-known and used in Boolean circuit complexity, while the latter notion is frequently found in literature on nature-inspired computation from the past 20 years or so. Are these two notions distinct? For many models it has been found that these notions are in fact the same, in the sense that the choice of uniformity or semi-uniformity leads to characterisations of the same complexity classes. In other related work, we showed that these notions are actually distinct for certain classes of Boolean circuits. Here, we give analogous results for membrane systems by showing that certain classes of uniform membrane systems are strictly weaker than the analogous semi-uniform classes. This solves a known open problem in the theory of membrane systems. We then go on to present results towards characterising the power of these semi-uniform and uniform membrane models in terms of NL and languages reducible to the unary languages in NL, respectively.Comment: 28 pages, 1 figur

Synthetic Heterosynaptic Plasticity Enhances the Versatility of Memristive Systems Emulating Bio-synapse Structure and Function

Memristive systems occur in nature and are hallmarked via pinched hysteresis, the difference in the forward and reverse pathways for a given phenomenon. For example, neurons of the human brain are composed of synapses which apply the properties of memristance for neuronal communication, learning, and memory consolidation. Modern technology has much to gain from the characteristics of memristive systems, including lower power operation, on-chip memory, and bio-inspired computing. What is more, a relationship between memristive systems and synaptic plasticity exists and can be investigated focusing on homosynaptic and heterosynaptic plasticity. Where homosynaptic plasticity applies to interactions between neurons at a synapse, heterosynaptic plasticity applies to an interneuron, a neuron that is not a part of the synapse, that modulates the neuronal interactions of synapses located elsewhere. Here, a synthetic synapse was used to study the heterosynaptic modulatory effects of osmotic stress via macromolecular crowding in the aqueous environment, membrane defects introduced from pH-sensitive secondary membrane species, and oxidative stress via oxidation of lipid species present in the membrane. Osmotic stress lowers the voltage threshold for alamethicin ion channels via depletion interactions and transmembrane water gradients. Secondary membrane species lowered the voltage threshold for alamethicin and lower pH environments enhanced the self-interaction between alamethicin monomers in a pore upon dissolution from the membrane. Oxidative stress created lipid species that compete for space in the polar-apolar interface of the lipid bilayer, leading to pore formation extending cell-free gene expression reactions. These findings help reveal how to environmentally modulate the synthetic synapse. Harnessing the power of memristive systems to create a biological computer enables the creation of new computers capable of adaptation, self-repair, and low-power operation while maintaining powerful computing and memory storage schemes

Design of a CMOS-Memristive Mixed-Signal Neuromorphic System with Energy and Area Efficiency in System Level Applications

The von Neumann architecture has been the backbone of modern computers for several years. This computational framework is popular because it defines an easy, simple and cheap design for the processing unit and memory. Unfortunately, this architecture faces a huge bottleneck going forward since complexity in computations now demands increased parallelism and this architecture is not efficient at parallel processing. Moreover, the post-Moore\u27s law era brings a constant demand for energy-efficient computing with fewer resources and less area. Hence, researchers are interested in establishing alternatives to the von Neumann architecture and neuromorphic computing is one of the few aspiring computing architectures that contributes to this research effectively. Initially, neuromorphic computing attracted attention because of the parallelism found in the bio-inspired networks and they were interested in leveraging this advantage on a single chip. Moreover, the need for speed in real time performance also escalated the popularity of neuromorphic computing and different research groups started working on hardware implementations of neural networks. Also, neuroscience is consistently building a better understanding of biological networks that provides opportunities for bridging the gap between biological neuronal activities and artificial neural networks. As a consequence, the idea behind neuromorphic computing has continued to gain in popularity. In this research, a memristive neuromorphic system for improved power and area efficiency has been presented. This particular implementation introduces a mixed-signal platform to implement neural networks in a synchronous way. In addition to mixed-signal design, a nano-scale memristive device has been introduced that provides power and area efficiency for the overall system. The system design also includes synchronous digital long term plasticity (DLTP), an online learning methodology that helps train the neural networks during the operation phase, improving the efficiency in learning when considering power consumption and area overhead. This research also proposes a stochastic neuron design with a sigmoidal firing rate. The design introduces variability in the membrane capacitance to reach different membrane potential leading to a variable stochastic firing rate