An Application of Fuzzy Analytic Hierarchy Process in Risk Evaluation Model

Conflicts in land exploration are incisive social problems which have been the subject in many studies. Risk assessment of land conflicts is effective to resolve such problems. Specifically, fuzzy mathematics and the analytic hierarchy process were combined together to evaluate risk in land conflicts in our work, which is proved useful to solve uncertainty and imprecision problems. Based on the analysis of the principles for the risk assessment of a land conflicts index system, a set of risk assessment indexes using the fuzzy analytic hierarchy process (FAHP) was presented. The results show that the overall risk is at medium level, and the risk of the feasibility index and controllability index need to be paid more attention to. The contribution of this article is reflected in two aspects: (1) the application of FAHP in risk assessments of land conflicts is effective and valid; (2) it is helpful for governments to establish a stricter management system of work safety for conflicts in land exploration based on the risk assessment results.

Evaluation of HEN controllability

In the last three decades the importance of resolve a robust, rigorous and operable heat exchanger networks (HEN) is boosted due to the change in the concepts of effective cost of a HEN several methodologies are applied to solve the operability aspects of a HEN including flexibility and controllability of a HEN in this work a systematic approach is introduced to solve the controllability issue for HENs. The work framework consist of two major steps where in the first step a disturbance propagation model for a HEN is constructed adopting Yang et.al disturbance model[1][2][3]. The second step is to install a bypass to reject the disturbance then pair the manipulated and controlled variables in a HEN using NS-RGA (None Square Relative Gain Array) and SVD (Singular Value decomposition), by adopting controllability index developed by Westphalen et al.[4]to measure the HEN controllability and finally a cost tradeoff between the bypass installation and utility installation to control the same disturbance is evaluated to check for the more cost effective method to eliminate and reject the disturbance in hand.

Quantitative Controllability Index of Complex Networks

In this paper, the controllability issue of complex network is discussed. A new quantitative index using knowledge of control centrality and condition number is constructed to measure the controllability of given networks. For complex networks with different controllable subspace dimensions, their controllability is mainly determined by the control centrality factor. For the complex networks that have the equal controllable subspace dimension, their different controllability is mostly determined by the condition number of subnetworks’ controllability matrix. Then the effect of this index is analyzed based on simulations on various types of network topologies, such as ER random network, WS small-world network, and BA scale-free network. The results show that the presented index could reflect the holistic controllability of complex networks. Such an endeavour could help us better understand the relationship between controllability and network topology.

Controllability Analysis of an Aggregate Demand Response System

In order to characterize the controllability of the aggregate demand response, that is, the aggregated consumer behaviors, the paper introduces the concept of controllability index which expresses the lowest occurrence probability in total electric consumption which can be changed by electric price. As the number of consumers is larger, it becomes difficult to directly analyze the controllability index. To resolve the difficulty of the large number of consumers, by using the central limit theorem, the paper approximates the controllability index and gives the solution maximizing the approximated controllability index.

A Higher-Order Method for Dynamic Optimization of Controllable LTI Systems

This work describes a new procedure for dynamic optimization of controllable Linear time-invariant (LTI) systems. Unlike the traditional approach, which results in 2n first order differential equations, the method proposed here yields a set of m differential equations, whose highest order is twice the controllability index of the system p. This paper generalizes the approach presented in a previous work [1] to any controllable LTI system.