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Various application domains require the integration of distributed real-time or near-real-time systems with non-real-time systems. Smart cities, smart homes, ambient intelligent systems, or network-centric defense systems are among these application domains. Data Distribution Service (DDS) is a communication mechanism based on Data-Centric Publish-Subscribe (DCPS) model. It is used for distributed systems with real-time operational constraints. Java Message Service (JMS) is a messaging standard for enterprise systems using Service Oriented Architecture (SOA) for non-real-time operations. JMS allows Java programs to exchange messages in a loosely coupled fashion. JMS also supports sending and receiving messages using a messaging queue and a publish-subscribe interface. In this article, we propose an architecture enabling the automated integration of distributed real-time and non-real-time systems. We test our proposed architecture using a distributed Command, Control, Communications, Computers, and Intelligence (C4I) system. The system has DDS-based real-time Combat Management System components deployed to naval warships, and SOA-based non-real-time Command and Control components used at headquarters. The proposed solution enables the exchange of data between these two systems efficiently. We compare the proposed solution with a similar study. Our solution is superior in terms of automation support, ease of implementation, scalability, and performance.

Particle swarm optimization (PSO) is a popular method to solve the optimization problems. However, calculations for each particle will be excessive when the number of particles and complexity of the problem increases. As a result, the execution speed will be too slow to achieve the optimized solution. Thus, this paper proposes an automated design and optimization method for rotary MRF brakes and similar multi-physics problems. A modified PSO algorithm is developed for solving multi-physics engineering optimization problems. The difference between the proposed method and the conventional PSO is to split up the original single population into several subpopulations according to the division of labor. The distribution of tasks and the transfer of information to the next party have been inspired by behaviors of a hunting party. Simulation results show that the proposed modified PSO algorithm can overcome the problem of heavy computational burden of multi-physics problems while improving the accuracy. Wire type, MR fluid type, magnetic core material, and ideal current inputs have been determined by the optimization process. To the best of the authors’ knowledge, this multi-physics approach is novel for optimizing rotary MRF brakes and the developed PSO algorithm is capable of solving other multi-physics engineering optimization problems. The proposed method has showed both better performance compared to the conventional PSO and also has provided small, lightweight, high impedance rotary MRF brake designs. Keywords: Particle swarm optimization (PSO), Multi-physics optimization problem, Rotary MRF brake, Heat transfer, Finite element