The dynamical evolution of electrical discharge machining (EDM) has drawn immense research interest. Previous research on mechanism analysis has discussed the deterministic nonlinearity of gap states at pulse-on discharging duration, while describing the pulse-off deionization process separately as a stochastic evolutionary process. In this case, the precise model describing a complete machining process, as well as the optimum performance parameters of EDM, can hardly be determined. The main purpose of this paper is to clarify whether the EDM system can maintain consistency in dynamic characteristics within a discharge interval. A nonlinear self-maintained equivalent model is first established, and two threshold conditions are obtained by the Shilnikov theory. The theoretical results prove that the EDM system could lead to chaos without external excitation. The time series of the deionization process recorded in the EDM experiments are then analyzed to further validate this theoretical conclusion. Qualitative chaotic analyses verify that the autonomous EDM process has chaotic characteristics. Quantitative methods are used to estimate the chaotic feature of the autonomous EDM process. By comparing the quantitative results of the autonomous EDM process with the non-autonomous EDM process, a deduction is further made that the EDM system will evolve towards steady chaos under an autonomous state.
The Internet of Things (IoT) is an important component of the new digital infrastructure and is deeply integrated with the fifth-generation mobile communication (5G), big data, cloud computing, artificial intelligence (AI), blockchain, and digital twin. It is profoundly changing the technology system and promoting the digital economy, ushering in a new stage of smart IoT system in which everything is connected. This paper reviews the development status of IoT in China, proposes the concept of smart IoT system (IoT 2.0), and expounds on the implications, architecture, technical pedigree, and key enabling technologies. Practical cases of smart IoT system are explored considering the application scenarios of intelligent manufacturing, smart agriculture, smart grid, smart healthcare, intelligent transportation, and intelligent environmental protection, demonstrating the application values of the smart IoT system. Furthermore, we suggest that a technology integration innovation project that integrates IoT, AI, 5G, and new application field technologies should be implemented; focus should be placed on the research, development, and industrialization of intelligent products such as smart IoT systems / cloud native platforms / low-code (no-code) application development environments and toolsets, high-end sensors for smart IoT systems, and IoT chips / special components; and application demonstration of cloud-edge-end collaborative, autonomous controllable, safe, and credible smart IoT systems should be conducted.
So far, there exists no general, common method for representing multi-resolution models. These results in that the interoperability and composability of models with different resolutions can't be theoretically or formally asserted to be get. And so the goal of MRM can't be reached. To address this problem, three patterns for MRM based on Base Object Model (BOM) wear put forward. The first one is “Bridge Pattern” to develop various component implementations with different resolution for the same BOM. The second one is “Composite Pattern” based on BOM pattern aggregation. And the third one is “Flyweight Pattern” based on BOM instance aggregation. BOM was extended with an extension component — Attribute Relationship View template, to describe consistency mappings among BOMs at different levels. The mapping function was represented with MathML, which can be inserted into BOM document easily. In simulation the mapping functions in ARV can be modified dynamically till the consistency could meet requirements and goals of modeling. So, common, general patterns for MRM, which can asset the consistencies among models, were set up.
The pipeline network interfered by the stray currents has been verified to be a complex nonlinear dynamical system. This paper shows that the chaotic characteristic could be utilized in the stray currents control. To prove this, this paper establishes an equivalent chaotic control model. Then we analyze this model by using Melnikov method and find out the condition under which the chaotic phenomenon will be controlled. In order to get a sufficient proof for identifying the conclusion, simulation experiments are used to verify the validity of the control method.
To solve the fault-tolerant and migration problems of simulation grid applications, the art of related research works is introduced firstly. Then, by adopting reflective software analysis and modeling method, the independent and dynamic fault-tolerant and migration model for simulation resources is proposed. Further more, the research fruits on several related key technologies are presented in detail, which include: to inspect the resources, the loading model is defined and the independent error forecast is achieved; the consistency of the simulation time is ensured by the harmonious advance of the state management of federates and the distributed simulation time management; and the automatic storage and restoring of state is accomplished. The research fruits above have been applied to the development of fault-tolerant and migration service for COSIM-CSP1.0v, and gained well validation in some typical applications. The multidisciplinary, distributed and collaborative simulation application for undercarriage virtual prototype is introduced as an example. Finally the conclusion is given.
The combination of parallel/distributed discrete event simulation (PDES) and Grid technology is a new trend in simulation. QoS is important yet difficult in this process. With the special features of Gird architecture and PDES input, such as periodical and predictable inputs, we could enhance QoS with a PDES-specific prediction. A Grid-based framework is presented that is designed to help predict the performance of PDES. Based on this framework, a prediction algorithm using time series theory is presented in the context of large scale Grid simulations. Experiments are executed in the context of GridSim, which shows methods discussed before to help to improve QoS level.
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