Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.

The Fuzzy Modeling Algorithm for Complex Systems Based on Stochastic Neural Network

A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.

VALIDATION OF NEAR-SURFACE WINDS OBTAINED BY A HYBRID WRF/CALMET MODELING SYSTEM OVER A COASTAL ISLAND WITH COMPLEX TERRAIN

The results from a hybrid approach that combines a mesoscale meteorological model with a diagnostic model to produce high-resolution wind fields in complex coastal topography are evaluated.The diagnostic wind model(California Meteorological Model,CALMET) with 100-m horizontal spacing was driven with outputs from the Weather Research and Forecasting(WRF) model to obtain near-surface winds for the 1-year period from 12 September 2003 to 11 September 2004.Results were compared with wind observations at four sites.Traditional statistical scores,including correlation coefficients,standard deviations(SDs) and mean absolute errors(MAEs),indicate that the wind estimates from the WRF/CALMET modeling system are produced reasonably well.The correlation coefficients are relatively large,ranging from 0.5 to 0.7 for the zonal wind component and from 0.75 to 0.85 for the meridional wind component.MAEs for wind speed range from 1.5 to 2.0 m s-1 at 10 meters above ground level(AGL) and from 2.0 to 2.5 m s-1 at 60 m AGL.MAEs for wind direction range from 30 to 40 degrees at both levels.A spectral decomposition of the time series of wind speed shows positive impacts of CALMET in improving the mesoscale winds.Moreover,combining the CALMET model with WRF significantly improves the spatial variability of the simulated wind fields.It can be concluded that the WRF/CALMET modeling system is capable of providing a detailed near-surface wind field,but the physics in the diagnostic CALMET model needs to be further improved.

Hierarchical Modeling by Recursive Unsupervised Spectral Clustering and Network Extended Importance Measures to Analyze the Reliability Characteristics of Complex Network Systems

The complexity of large-scale network systems made of a large number of nonlinearly interconnected components is a restrictive facet for their modeling and analysis. In this paper, we propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method. The hierarchical model serves the purpose of facilitating the management of complexity in the analysis of real-world critical infrastructures. We exemplify this by referring to the reliability analysis of the 380 kV Italian Power Transmission Network (IPTN). In this work of analysis, the classical component Importance Measures (IMs) of reliability theory have been extended to render them compatible and applicable to a complex distributed network system. By utilizing these extended IMs, the reliability properties of the IPTN system can be evaluated in the framework of the hierarchical system model, with the aim of providing risk managers with information on the risk/safety significance of system structures and components.

Knowledge-Based Multifaceted Modeling Methodology for Open Complex Giant Systems

In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.

The Complex System Modeling Method Based on Uniform Design and Neural Network

In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.

Modelling Immune System:Principles,Models,Analysis and Perspectives

The biological immune system is a complex adaptive system.There are lots of benefits for building the model of the immune system.For biological researchers,they can test some hypotheses about the infection process or simulate the responses of some drugs.For computer researchers,they can build distributed,robust and fault tolerant networks inspired by the functions of the immune system.This paper provides a comprehensive survey of the literatures on modelling the immune system.From the methodology perspective,the paper compares and analyzes the existing approaches and models,and also demonstrates the focusing research effort on the future immune models in the next few years.

PANSYSTEMS CLUSTERING ANALYSIS OF COMPLEX SYSTEMS

In this paper, by use of equivalence operators δi and semi-equivalence operators Εi we study the clustering problems of complex systems, present δ (1,3) disconnection principle, dual transformation principle and large-scale systems decomposition principle for analizing and operating complex systems, discuss interconnectivity and disconnectivity of complex systems in detail and present some related theorems. Finally, we discuss the levels of systems according to pansystems clustering approach proposed in this paper.

Establishment of New Solution for Complex Systems in Multidisciplinary Sci-ence Based on Feedback System Analysis Method and Proven by Simulator

This paper establishes a very important scientific solution to science of complexity for physicists, and presents a multidisciplinary involved physics and engineering. The innovative solution for complex systems presented here is verified on the basis of principles in engineering such as feed-back-system analysis using the classical control theory. This paper proposes that a complex system is a closed-loop system with a negative feedback element and is a solvable problem. A complex system can be analyzed using the system analysis theory in control engineering, and its behavior can be realized using a specially designed simulator.

Explosive synchronization of multi-layer frequency-weighted coupled complex systems

Synchronization is a phenomenon that is ubiquitous in engineering and natural ecosystems.The study of explosive synchronization on a single-layer network gives the critical transition coupling strength that causes explosive synchronization.However, no significant findings have been made on multi-layer complex networks.This paper proposes a frequency-weighted Kuramoto model on a two-layer network and the critical coupling strength of explosive synchronization is obtained by both theoretical analysis and numerical validation.It is found that the critical value is affected by the interaction strength between layers and the number of network oscillators.The explosive synchronization will be hindered by enhancing the interaction and promoted by increasing the number of network oscillators.Our results have importance across a range of engineering and biological research fields.

Development and Investigation of the Program Model of Multichannel Queueing System with Unreliable Recoverable Servers in Matlab Environment

In real life, in different industries, we often deal with systems designed for multiple use for performing single-type tasks. Processes taking place at this time are called service of requirements, and the systems themselves—Queueing Systems. This article is dedicated to computer software modelling of processes taking place in the systems in question, Markov processes in particular. In this article, by means of Matlab environment, software realization of one of the typical models of queueing service theory-multichannel QS with unreliable recoverable servers and limited number of requirements in the system, is fulfilled. The results of this research are important because it gives the possibility to use received results to determine optimality degree of some real queueing systems that possess Markov property.

Clustering-based maintainability demonstration for complex systems with a mixed maintenance time distribution

During maintainability demonstration,the maintenance time for complex systems consisting of mixed technologies generally conforms to a mixture distribution.However existing maintainability standards and guidance do not explain explicitly how to deal with this situation.This paper develops a comprehensive maintainability demonstration method for complex systems with a mixed maintenance time distribution.First of all,a K-means algorithm and an expectation-maximization(EM)algorithm are used to partition the maintenance time data for all possible clusters.The Bayesian information criterion(BIC)is then used to choose the optimal model.After this,the clustering results for equipment are obtained according to their degree of membership.The degree of similarity for the maintainability of different kinds of equipment is then determined using the projection method.By using a Bootstrap method,the prior distribution is obtained from the maintenance time data for the most similar equipment.Then,a test method based on Bayesian theory is outlined for the maintainability demonstration.Finally,the viability of the proposed approach is illustrated by means of an example.

Modeling the Spatio-Temporal Dynamics of Local Context for a Contextualized Diffusion of Agroecological Intensification Options in Niger

Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization;the scale of the “village terroir” seems to be a better option. This is the hypothesis we put forward in the framework of the Global Collaboration for Resilient Food Systems program (CRFS), i.e. local context is spatially defined by village terroir. The study is based on data collected through participatory mapping and surveys in “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri). Then the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of local context are analyzed. This study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agrosocio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio- temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.

Complex Sequence Quality Evaluation Model for Concealed Water Supply Pipeline of Prefabricated Construction

The accurate assessment of the quality can materially affect the safety and life of the prefabricated construction.In this paper,we studied the overall module of concealed water supply pipeline,and a new complex sequence quality evaluation model was established.By analyzing the small evaluation index elements of the model,the weight of the larger evaluation index could be deduced.The total quality evaluation score of the overall module could be obtained by calculating the weight value of the evaluation index,and then the quality standard of the overall module could be obtained.

Modeling and Analysis of Complex Equipment Maintenance Dynamics

An equipment maintenance system is naturally a complex dynamical system. The effective mamanagement must be based on the knowledge of the system＇s intrinsic dynamics. And the strueture of the maintenance system determines its behavior. This paper analyzes the basic structures and elements of a maintenance system for complex multi-components equipment. The maintenance system is considered as a dynamic system whose behavior is influenced by its structure＇s feedback and interaction, and the system＇s available resources. Building the dynamical model with Simulink, we show some results about the maintenance system＇s nonlinear dynamics, ods. The model can be used for understanding and which operational adjustments of maintenance which are never given by stochastic process methdetermining maintenance system behavior, towards n of maintenance requirements and timely supply of maintenance resources can be made in a more informed way.

Gravitationally Quantized Orbits in the Solar System: Computations Based on the Global Polytropic Model

The so-called “global polytropic model” is based on the assumption of hydrostatic equilibrium for the solar system, or for a planet’s system of statellites (like the Jovian system), described by the Lane-Emden differential equation. A polytropic sphere of polytropic index?n?and radius?R1?represents the central component?S1?(Sun or planet) of a polytropic configuration with further components the polytropic spherical shells?S2,?S3,?..., defined by the pairs of radi (R1,?R2), (R2,?R3),?..., respectively.?R1,?R2,?R3,?..., are the roots of the real part Re(θ) of the complex Lane-Emden function?θ. Each polytropic shell is assumed to be an appropriate place for a planet, or a planet’s satellite, to be “born” and “live”. This scenario has been studied numerically for the cases of the solar and the Jovian systems. In the present paper, the Lane-Emden differential equation is solved numerically in the complex plane by using the Fortran code DCRKF54 (modified Runge-Kutta-Fehlberg code of fourth and fifth order for solving initial value problems in the complex plane along complex paths). We include in our numerical study some trans-Neptunian objects.

The Golden Ratio Theorem: A Framework for Interchangeability and Self-Similarity in Complex Systems

The Golden Ratio Theorem, deeply rooted in fractal mathematics, presents a pioneering perspective on deciphering complex systems. It draws a profound connection between the principles of interchangeability, self-similarity, and the mathematical elegance of the Golden Ratio. This research unravels a unique methodological paradigm, emphasizing the omnipresence of the Golden Ratio in shaping system dynamics. The novelty of this study stems from its detailed exposition of self-similarity and interchangeability, transforming them from mere abstract notions into actionable, concrete insights. By highlighting the fractal nature of the Golden Ratio, the implications of these revelations become far-reaching, heralding new avenues for both theoretical advancements and pragmatic applications across a spectrum of scientific disciplines.

基于Modelica语言的复杂机械系统统一建模平台研究

研究了基于Modelica语言的复杂机械系统统一建模平台MWorks的关键技术。在分析Modelica模型描述和Modelica标准库(MSL)结构的基础上,设计了基于Modelica语言的可视化建模系统框架,提出了基于有向无环图的MSL内存存储模式,提高了可视化建模速度;提出了基于实体句子匹配的快速图文切换算法;设计了基于句子的线性二叉树结构,实现了文本和图形编辑环境相统一的恢复操作。最后给出了MWorks系统的建模实例。

基于Modelica的半物理仿真代码生成方法研究

针对当前复杂机电系统的多领域模型不支持半物理仿真或者已有半物理仿真工具无法建立复杂机电系统多领域模型的问题,围绕目标代码生成这一关键技术,提出了两种由Modelica模型生成半物理仿真代码的方法。首先深入研究了Modelica模型直接代码生成技术,并详细描述了其实现过程。其次介绍了基于Simulink的联合建模方法及间接代码生成过程。最后通过示例对两种代码生成方法分别进行了应用验证。

基于System Generator的雷达干扰信号模拟

针对雷达干扰信号模拟中算法设计快速验证的应用需求,为降低系统可重构开发中的复杂程度,研究了一种基于System Generator模型的FPGA设计开发方法,并以噪声调频和噪声卷积算法实现。对应用实例进行了设计演示验证,为雷达干扰信号算法验证实验提供技术参考。