A web interface for WMN-PSODGA hybrid intelligent simulation system: performance evaluation for different mesh client distributions

Purpose The purpose of this paper is to implement a web interface for a hybrid intelligent system. By using the implemented web interface, one can find optimal assignments of mesh routers in wireless mesh networks (WMNs). This study evaluates the implemented system considering three distributions of mesh clients to solve the node placement problem in WMNs. Design/methodology/approach The node placement problem in WMNs is well known to be a computationally hard problem. Therefore, intelligent algorithms are used for solving this problem. The implemented system is a hybrid intelligent system based on meta-heuristics algorithms: particle swarm optimization (PSO) and distributed genetic algorithm (DGA). The proposed system is called WMN-PSODGA. Findings This study carried out simulations using the implemented simulation system. From the simulations results, it was found that the WMN-PSODGA system performs better for chi-square distribution of mesh clients compared with Weibull and exponential distributions. Research limitations/implications For simulations, three different distributions of mesh clients were considered. In the future, other mesh client distributions, number of mesh nodes and communication distance need to be considered. Originality/value This research work, different from other research works, implemented a hybrid intelligent simulation system for WMNs. This study also implemented a web interface for the proposed system, which make the simulation system user-friendly.

Intelligent Simulation of Children’s Psychological Path Selection Based on Chaotic Neural Network Algorithm

In recent years, there are many problems in the study of intelligent simulation of children’s psychological path selection, among which the main problem is to ignore the factors of children’s psychological path selection. Based on this, this paper studies the application of chaotic neural network algorithm in children’s mental path selection. First, an intelligent simulation model for children’s mental path selection based on chaotic neural network algorithm is established; second, it will combine the network based on different types of visual analysis strategies. The model is used to analyze the influencing factors of children in different regions in the choice of psychological paths. Finally, experiments are designed to verify the actual application effect of the simulation model. The results show that compared with the current mainstream intelligent simulation methods with iterative loop algorithms as the core, it adopts the intelligent simulation model based on the chaotic neural network algorithm has a good classification effect. It can effectively select the optimal psychological path according to the differences in children’s personality and can adaptively classify children in different regions, and the experimental results are accurate. Compared with the traditional method, it is improved by at least 37%.

Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models

It is difficult to determine the main control factors owing to the complex geological conditions of heavy oil reservoirs, including high viscosity, a wide range of variation of crude oil, and the great difference in production between different recovery methods. In this context, main control factors of heavy oil production in different recovery methods are analyzed and obtained based on the Apriori algorithm. The prediction of heavy oil production is faced with problems such as low prediction precision and insufficient data usage. Therefore, a novel intelligent simulation and prediction model of data-driven heavy oil production with time-varying characteristics is established based on differential simulation, machine learning, and intelligent optimization theory, which overcomes the defects of nonlinear, multifactor, and low fitting precision of dynamic data of heavy oil development. The parameters of the heavy oil production time-varying simulation model are identified by the least square support vector machine (LSSVM) to realize the intelligent prediction of the production. Numerical experiments show that the prediction result of the novel intelligent simulation and prediction model is better than the BP neural network model and the GM (1, N) model. This study provides a novel feasible method for data-driven heavy oil production prediction, and it can be helpful in further study of data-driven heavy oil production.

An intelligent simulation-based framework for automated planning of concrete construction works

PurposeThis paper proposes an innovative intelligent simulation-based construction planning framework that introduces a new approach to simulation-based construction planning.Design/methodology/approachIn this approach, the authors developed an ontological inference engine as an integrated part of a constraint-based simulation system that configures the construction processes, defines activities and manages resources considering a variety of requirements and constraints during the simulation. It allows for the incorporation of the latest project information and a deep level of construction planning knowledge in the planning. The construction planning knowledge is represented by an ontology and several semantic rules. Also, the proposed framework uses the project building information model (BIM) to extract information regarding the construction product and the relations between elements. The extracted information is then converted to an ontological format to be useable by the framework.FindingsThe authors implemented the framework in a case study project and tested its usefulness and capabilities. It successfully generated the construction processes, activities and required resources based on the construction product, available resources and the planning rules. It also allowed for a variety of analyses regarding different construction strategies and resource planning. Moreover, 4D BIM models that provide a very good understanding of the construction plan can be automatically generated using the proposed framework.Originality/valueThe active integration between BIM, discrete-event simulation (DES) and ontological knowledge base and inference engine defines a new class of construction simulation with expandable applications.