A New Approach to Analyzing Behavioral Change Propagation in Redesigning of Drone Camera Stabilizer

Redesigning of complex products is not an easy task. Engineering change requirements can be extracted at any stage of the product redesign process, and it makes the management of engineering change become a challenging mission. The motivation for this study is to find the shortest path of behavioral change propagation (BCP), minimize the BCP, access to the special behavioral elements in order to better managing the BCP and classifying the behavioral attribute of the elements in terms of their relationship to change by betweenness centrality coefficient (BNCC), clustering coefficient (CLC), reachability coefficient (RC) and change propagation index (CPI). In this article, the procedure of managerial decision-making is proposed by combining system restrictions in behavioral clustering design structure matrix (BCDSM) with optimization algorithms. The practicality of suggested method is verified in redesign procedure of a phantom drone camera stabilizer as a case study. The results, indicate that the absorption of change by behavioral elements is dominant in the mechanical (63.9%), electrical (61.1%) and thermal (38.9%) behaviors of the drone camera stabilizer system in redesign process. These elements are best candidates for reducing the cost and time of behavioral changes in the system redesign and are desirable for the designer.

Behaviors analysis of on-coming cluster based on knowledge under cluster security requirements

Analysis of cluster behaviors of the on-coming cluster is an essential measure for high value locations on the battlefield.Unlike target tracking and clustering analysis,cluster behaviors analysis under cluster security requirements remains an open issue,which is a joint analysis of clus-tering,intent reasoning and activity regions.To address this issue,a framework for cluster behav-iors analysis is proposed by incorporating expert knowledge and domain knowledge,and a knowledge-assisted score function with is designed to improve the accuracy of intent reasoning net-work,overcoming the effects of possible knowledge errors.The framework consists of three mod-ules for cluster analysis,intent reasoning and activity region analysis for typical tasks,in which an intent reasoning network is constructed to obtain cluster intents by using a hybrid knowledge and data driven approach.Furthermore,considering the complexity of the battlefield environment,dif-ferent tasks and corresponding activity region optimization functions are designed for cluster activ-ity region analysis,which are vital elements of cluster behaviors analysis.Simulations demonstrate the effectiveness of the proposed cluster behaviors analysis framework.