Feng Qiao

Linking and implementation of fuzzy logic control to ordinate plant strategies

Fuzzy control algorithms are used to give structure to the spatial categorisation of plant species by integrating digital elevation model data at increased resolution with data of selected climatic variables (mean precipitation, temperature, ground frost frequency and elevation). In previous studies, the climatic variables are obtained by minimising them to those essential for expression of the water-energy dynamic (the way in which water and energy are distributed in relation to diversity) in order to model life-history strategies of plant species. In this paper, Takagi-Sugeno-Kang fuzzy modelling is applied to infer which factors of the water energy dynamic may be used for efficient prediction of plant strategies. Implications of this paper include modelling of plant life-forms and metabolic patterning.

Multiple rules decision-based DE solution for the earliness-tardiness case of hybrid flow-shop scheduling problem

The earliness/tardiness (E/T) case of hybrid flow-shop scheduling problem (HFSP) is an NP hard problem, which is difficult to deal with; however, the existence of the multi-rules relating to the practical production increases the complexity of this problem. How to solve the combinatorial optimisation problem effectively and optimally is still an open issue today. In this paper, the joint scheduling strategy of differential evolution (DE) algorithm and factor space-based multiple rules decision method is used to solve this E/T scheduling problem. Firstly, DE algorithm is used to make global assignment and obtain each job’s process route. Secondly, factor space method is used to describe the scheduling rules in production process; then a scheduling decision method based on variable weight comprehensive function is considered to figure out the jobs’ operating priority in buffer area during the local production assignment between stages; subsequently the starting time of each job can be determined. Finally, u...

Implementing stochastic distribution within the utopia plane of primary producers using a hybrid genetic algorithm

The two key variables in estimating the water-energy dynamic, which determines proportions of plant strategy components on a macro basis, are temperature and precipitation. Additionally, use of high-resolution elevation data facilitates formation of the fuzzy rule base for ordination of the strategical nodes. Application of adaptive neural fuzzy inference systems produces sets of rules, which may be minimised to increase the efficiency of modelling the distribution of plants and their characters. A modified objective genetic evolutionary algorithm was employed in this study to show the distribution of elements of strategies within a strength Pareto. Distribution of the elements showed an approximate Poisson curve in objective space that may be extrapolated to a real-numbered population via application of optimisation algorithms to reflect the stochastic organisation of the populations.

Adaptive observer-based non-linear stochastic system control with sliding mode schemes

In this paper, an adaptive sliding mode observer is designed to reconstruct the states of non-linear stochastic systems with uncertainties from the measurable system output and the reconstructed states are employed to construct a sliding mode controller for the stabilization control of complex non-linear systems. It takes the advantages of the sliding mode schemes to design both the observer and the controller. The convergence of the observer and the globally asymptotical stability of the controller are analysed in terms of stochastic Lyapunov stability, and the effectiveness of the control strategy is verified with numerical simulation studies.

Mathematical methods to quantify and characterise the primary elements of trophic systems

Mathematical approaches to plant characterisation are explained, there being a range of tools available to categorise and describe biological and environmental systems. Genetic algorithms GAs and fuzzy-logic are techniques used for multiple criteria-based decision-making. Data from a previously described algorithm are shown across an ecological continuum. Examples of the use of fuzzy logic to quantify environmental predictors are given. A fusion of techniques is proposed for the simultaneous categorisation of large numbers of plant species. The use of linguistic terms in fuzzy based systems is discussed and examples are given. Fuzzy systems use covariates of the prevailing conditions water-energy dynamic to guide characterisation of plants within different global environments. Knowledge guidance systems used with the technique for order similarity based on ideal situation TOPSIS categorise life history-based strategies of plants within trophic systems. Hybrid-techniques potential application to future research methods is given.

DE solution for the earliness/tardiness case of Hybrid Flow-shop Scheduling problem with priority strategy

The earliness/tardiness (E/T) case of Hybrid Flow-shop Scheduling problem (HFSP) is an NP hard problem, which is difficult to deal with, however, the local assignment existing in the practical production increases the complexity of this problem. How to solve the combinatorial optimization problem effectively and optimally is still an open issue today. In this paper, differential evolution algorithm (DE) combined with priority strategy is used to solve this E/T scheduling problem. Firstly, DE algorithm is used to make global assignment and obtain each jobs process route. Secondly, the operating priority of the jobs in buffer area deduced from the expectation completion sequence is used to direct the local production assignment between stages, then the starting time of each job can be determined. Finally, under the constraints of the due-date, the global optimization with the minimal penalty sum of E/T is obtained. Several scheme comparisons with simulation results show the effectiveness of the proposed method.

NSQGA-Based Optimization of Traffic Signal in Isolated Intersection with Multiple Objectives

In this chapter, a novel multi-objective optimization algorithm is investigated to deal with the issue of signal timing in an isolated traffic intersection aiming at releasing traffic congestion, reducing travel delay, maximizing the traffic flow, and minimizing pollution. The throughput maximum, stop times, and delay time of motorized traffic and non-motorized traffic are selected as the objectives of the optimization problem, and quantum computing is integrated with the genetic algorithm to obtain optimized traffic signal timing plan to upgrade the performance of intersection with faster convergence and higher accuracy. A numerical simulation study is conducted on MATLAB in this research work as a case study with a Non-dominated Sorting Quantum Genetic Algorithm (NSQGA), and the simulation results show that the proposed NSQGA algorithm performed superior to the conventional NSGA-II algorithm in effectively coordinating the traffic signal timing plan for an isolated intersection to improve the traffic capacity, efficiency, and safety of traffic system.

Biological Modelling for Sustainable Ecosystems

Modelling of biological systems is discussed in terms of the primary producers of trophic levels of organisation of life. Richness of plant communities enables sustainability to be reached within subsequent trophic levels. Plant dimensions of life history strategy, primary metabolic type and life form are defined and discussed with respect to the water–energy topography dynamic of climatic variables. The role of biogeography is given and classical approaches to species distributions and both antecedent and consequent variables within plants differentiated modelling frameworks are discussed. The algorithmic modelling frameworks applied in plant systems are justified statistically and with reference to established models. Mechanisms of dispersing discrete distributes are covered in consideration of genetic programming techniques. Further analysis expanding discrete approaches through functional transformations is considered and detail of a Gaussian Process Model is shown. Case study data of global locations is considered by means of discrete approaches of strategy and photosynthetic type and a continual approach of life form distribution. Algorithms for all processes and techniques are shown and graphical distributions made in illustration of the techniques. Synergy which may take place between the techniques is elaborated and flow diagrams of multiple benefits in pattern identification and further analysis are given. Recommendations of planting policies and policy implementation methods are covered in the final section, which also gives further direction on which we can base investigations which allow rational truth of distributes to be maximised and hence modulation of future modelling frameworks. Value is provided in terms of empirical, cause–effect and combinatorial approaches allowing us to process information effectively, structuring trophic levels and our own communities’ expansive needs.

Pinning Synchronization of Switched Complex Dynamical Networks

Network topology and node dynamics play a key role in forming synchronization of complex networks. Unfortunately there is no effective synchronization criterion for pinning synchronization of complex dynamical networks with switching topology. In this paper, pinning synchronization of complex dynamical networks with switching topology is studied. Two basic problems are considered: one is pinning synchronization of switched complex networks under arbitrary switching; the other is pinning synchronization of switched complex networks by design of switching when synchronization cannot achieved by using any individual connection topology alone. For the two problems, common Lyapunov function method and single Lyapunov function method are used respectively, some global synchronization criteria are proposed and the designed switching law is given. Finally, simulation results verify the validity of the results.

Identifying individuals from gait pattern using waist-mounted accelerometer

A novel algorithm to identify individuals via gait pattern by waist-mounted MEMS accelerometer is presented. Vertical acceleration signal is selected and represented as a seven-feature-tuple extracted from continuous wavelet transform. A multi-criterion model is designed to match the feature sequences using dynamic time wrapping algorithm. Experiments with a dataset of 24 subjects show that the equal error rate of the proposed algorithm has achieved 5% which is superior to that of 6.4% and 6.7% in the previous work.