An improved self-adaptive membrane computing optimization algorithm and its applications in residue hydrogenating model parameter estimation

In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.

Hybrid membrane computing and pigeon-inspired optimization algorithm for brushless direct current motor parameter design

In this paper, a novel approach is proposed for solving the parameter design problem of brushless direct current(BLDC) motor, which is based on the membrane computing(MC) and pigeon-inspired optimization(PIO) algorithm. The motor parameter design problem is converted to an optimization problem with five design parameters and six constraints. The PIO algorithm is introduced into the framework of MC for improving the global convergence performance. The hybrid algorithm can improve the population diversity with better searching efficiency. Comparative simulations are conducted, and comparative results are given to show the feasibility and effectiveness of our proposed hybrid algorithm for high nonlinear optimization problems.

Membrane-inspired quantum bee colony optimization and its applications for decision engine

In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorithm applies the membrane computing theory to quantum bee colony optimization(QBCO),which is an effective discrete optimization algorithm.The global convergence performance of MQBCO is proved by Markov theory,and the validity of MQBCO is verified by testing the classical benchmark functions.Then the proposed MQBCO algorithm is used to solve decision engine problems of cognitive radio system.By hybridizing the QBCO and membrane computing theory,the quantum state and observation state of the quantum bees can be well evolved within the membrane structure.Simulation results for cognitive radio system show that the proposed decision engine method is superior to the traditional intelligent decision engine algorithms in terms of convergence,precision and stability.Simulation experiments under different communication scenarios illustrate that the balance between three objective functions and the adapted parameter configuration is consistent with the weights of three normalized objective functions.

Membrane-inspired quantum shuffled frog leaping algorithm for spectrum allocation

To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm,which is an effective discrete optimization algorithm.Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems.By hybridizing the quantum frog colony optimization and membrane computing,the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure.The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time.Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.

CFD-based optimization and design of multi-channel inorganic membrane tubes

As a major configuration of membrane elements,multi-channel porous inorganic membrane tubes were studied by means of theoretical analysis and simulation.Configuration optimization of a cylindrical 37-channel porous inorganic membrane tube was studied by increasing membrane filtration area and increasing permeation efficiency of inner channels.An optimal ratio of the channel diameter to the inter-channel distance was proposed so as to increase the total membrane filtration area of the membrane tube.The three-dimensional computational fluid dynamics(CFD) simulation was conducted to study the cross-flow permeation flow of pure water in the 37-channel ceramic membrane tube.A model combining Navier–Stokes equation with Darcy's law and the porous jump boundary conditions was applied.The relationship between permeation efficiency and channel locations,and the method for increasing the permeation efficiency of inner channels were proposed.Some novel multichannel membrane configurations with more permeate side channels were put forward and evaluated.

Tubular electrocatalytic membrane reactor for alcohol oxidation：CFD simulation and experiment

A functional electrocatalytic membrane reactor(ECMR) was performed for the electrocatalytic oxidation of2,2,3,3-tetrafluoro-l-propanol(TFP) into high value-added sodium 2,2,3,3-tetrafluoropropionate(STFP),A computational fluid dynamics(CFD) technique was applied to simulate the hydrodynamic distributions along a tubular ECMR so as to provide guidance for the design and optimization of ECMR Two-dimensional simulation with porous media model was employed to predict the properties of fluid dynamics in ECMR.The experimental investigation was carried to confirm the CFD simulation.Results showed that a uniform distribution of permeate velocity along the tubular reactor with short length and large diameter could be obtained.TFP conversion of97.7%,the selectivity to STFP of 99.9%and current efficiency of 40.1%were achieved from the ECMR with a length of 40 mm and an inside diameter of 53 mm.The simulations were in good agreement with the experimental results.

Spiking neural P systems with anti-spikes and without annihilating priority as number acceptors

Spiking neural P systems with anti-spikes （ASN P systems） are variant forms of spiking neural P systems, which are inspired by inhibitory impulses/spikes or inhibitory synapses. The typical feature of ASN P systems is when a neuron contains both spikes and anti-spikes, spikes and anti-spikes wil immediately annihilate each other in a maximal way. In this paper, a restricted variant of ASN P systems, cal ed ASN P systems without anni-hilating priority, is considered, where the annihilating rule is used as the standard rule, i.e., it is not obligatory to use in the neuron associated with both spikes and anti-spikes. If the annihilating rule is used in a neuron, the annihilation wil consume one time unit. As a result, such systems using two categories of spiking rules （identified by （a, a） and （a,a^-）） can achieve Turing completeness as number accepting devices.

Membrane Automata with Priorities

In this paper the one-way P automata with priorities are introduced. Suchautomata are P systems where the membranes are only allowed to consume objects from parentmembranes, under the given conditions. The result of computation of these systems is the set ofmultiset sequences consumed by skin membrane into the system. The rules associated in some orderwith each membrane cannot modify any objects, they can only move them through membrane. We show thatP automata with priorities and two membranes can accept every recursively enumerated language.

A Fault Diagnosis Method for Power Systems Based on Temporal Tissue-like P Systems

To quickly and accurately identify faulty components based on the alarm information is critical for the fault diagnosis of power grids.To address this chal-lenge,this paper proposes a novel fault diagnosis method based on temporal tissue-like P system(TTPS).In the proposed method,suspected faulty components are iden-tifiedfirst via a network topology analysis method.An TTPS-based fault diagnosis model is then built for each suspected faulty component to perform fault reasoning,so as to accurately detect the faulty components.To take full advantage of the action signals and temporal information of protection devices,TTPS and its forward temporal reasoning algorithm are proposed.TTPS can synchro-nously model the action and temporal logics of protection devices in an intuitive and graphical way,while the rea-soning algorithm can process the fault alarm information in parallel.To demonstrate the effectiveness and superi-ority of the proposed method,simulations are carried out on the IEEE 14-bus and 118-bus systems,while the results are compared to other two widely adopted methods.Index Terms—Alarm signal,fault diagnosis,membrane computing,power system,tissue-like P system.

A Low-carbon Operation Optimization Method of ETG-RIES Based on Adaptive Optimization Spiking Neural P Systems

To enhance multi-energy complementarity and foster a low carbon economy of energy resources,this paper proposes an innovative low-carbon operation opti-mization method for electric-thermal-gas regional inte-grated energy systems.To bolster the low-carbon operation capabilities of such systems,a coordinated operation framework is presented that integrates carbon capture devices,power to gas equipment,combined heat and power units,and a multi-energy storage system.To address the challenge of high-dimensional constraint imbalance in the optimization process,a novel low-carbon operation opti-mization method is then proposed.The new method is based on an adaptive single-objective continuous optimiza-tion spiking neural P system,specifically designed for this purpose.Furthermore,simulation models of four typical schemes are established and employed to test and analyze the economy and carbon environmental pollution degree of the proposed system model,as well as the performance of the operation optimization method.Finally,simulation results show that the proposed method not only considers the economic viability of the target integrated energy sys-tem,but also significantly improves the wind power utilization and carbon reduction capabilities.

Reversible spiking neural P systems

Spiking neural （SN） P systems are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes. In this work, we investigate reversibility in SN P systems, as well as the computing power of reversible SN P systems. Reversible SN P systems are proved to have Turing creativity, that is, they can compute any recursively enumerable set of non-negative integers by simulating universal reversible register machine.

A uniform solution to the independent set problem through tissue P systems with cell separation

Membrane computing is an emergent branch of natural computing, which is inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. Tissue P systems are a class of the most investigated computing mod- els in the framework of membrane computing, especially in the aspect of efficiency. To generate an exponential resource in a polynomial time, cell separation is incorporated into such systems, thus obtaining so called tissue P systems with cell separation. In this work, we exploit the computational efficiency of this model and construct a uniform family of such tissue P systems for solving the independent set problem, a well-known NP-complete problem, by which an efficient so- lution can be obtained in polynomial time.

Neuroprotective Effect of Shenfu Injection (参附注射液) Following Cardiac Arrest in Pig Correlates with Improved Mitochondrial Function and Cerebral Glucose Uptake

Objective：To test whether Shenfu Injection（参附注射液,SFI）might attenuate the impact of cerebral energy dysfunction after resuscitation in a pig model of cardiac arrest（CA）.Methods：Thirty-four Wuzhishan miniature inbred pigs were randomly divided into three groups：the SFI group（n=12）,the saline group（SA group,n=12）,and the sham-operated group（sham group,n=10）.Following successful return of spontaneous circulation（ROSC）from 8-min untreated ventricular fibrillation,animals received a continuous infusion of either SFI（0.2 mL/min）or saline for 6 h.Cerebral performance category score was evaluated at 24and 48 h after ROSC,followed by positron emission tomography and computed tomography scans of cerebral glucose uptake.Surviving pigs were euthanized 48 h after ROSC,and the brains were removed for detecting mitochondrial function.Results：Compared with the SA group,SFI treatment produced a better neurologic outcome48 h after ROSC（P〈0.05）.However,there was no significant difference of survival rate between the SA and SFI groups（83.3%vs.81.8%,P〉0.05）.After ROSC,the SA group showed a decrease in the maximum standardized uptake value of different regions in the brain tissue,where SFI treatment can ameliorate these decreases（P〈0.01or P〈0.05）.Improved mitochondrial respiratory properties and higher mitochondrial membrane potential were also found following SFI treatment compared with the SA group at 48 h after ROSC（P〈0.05 or P〈0.01）.Conclusion：SFI treatment after resuscitation has significant neuroprotective effects against disruption of cerebral energy metabolism from CA by improving glucose uptake and by normalizing mitochondrial function.