A Task-oriented Modular and Agent-based Collaborative Design Mechanism for Distributed Product Development

The rapid expansion of enterprises makes product collaborative design （PCD） a critical issue under the distributed heterogeneous environment, but as the collaborative task of large-scale network becomes more complicated, neither unified task decomposition and allocation methodology nor Agent-based network management platform can satisfy the increasing demands. In this paper, to meet requirements of PCD for distributed product development, a collaborative design mechanism based on the thought of modularity and the Agent technology is presented. First, the top-down 4-tier process model based on task-oriented modular and Agent is constructed for PCD after analyzing the mapping relationships between requirements and functions in the collaborative design. Second, on basis of sub-task decomposition for PCD based on a mixed method, the mathematic model of task-oriented modular based on multi-objective optimization is established to maximize the module cohesion degree and minimize the module coupling degree, while considering the module executable degree as a restriction. The mathematic model is optimized and simulated by the modified PSO, and the decomposed modules are obtained. Finally, the Agent structure model for collaborative design is put forward, and the optimism matching Agents are selected by using similarity algorithm to implement different task-modules by the integrated reasoning and decision-making mechanism with the behavioral model of collaborative design Agents. With the results of experimental studies for automobile collaborative design, the feasibility and efficiency of this methodology of task-oriented modular and Agent-based collaborative design in the distributed heterogeneous environment are verified. On this basis, an integrative automobile collaborative R＆D platform is developed. This research provides an effective platform for automobile manufacturing enterprises to achieve PCD, and helps to promote product numeralization collaborative R＆D and management development.

Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible.In an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user experience.However,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of users.Recently,techniques for caching content at the edge are becoming popular for addressing the above challenges.It is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud servers.However,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity data.To address this challenge,we introduce a novel method for content caching over MEC,i.e.,PRIME.It synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching schedules.Experimental results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

Design of hydraulic shift mechanism for AMT vehicles based on the collaborative optimization method

Based on multidisciplinary design optimization（MDO）,a new design method is put forward for hydraulic shift mechanism of heavy-duty vehicle automated manual transmission（AMT）.Taking a shift cylinder for example,the collaborative optimization（CO）method for the design problem of a cylinder is devided into one system level design optimization problem and three subsystem level design optimization problems.The system level is an economic model and the subsystem level is mechanics,kinetics,and a reliability model.Application of the multidisciplinary design optimization software iSIGHT modeling and solving,optimal solution of the shifting cylinder CO model is obtained.According to the optimal solution,oil cylinders are machined out and installed on the gearbox of an AMT system for the bench cycle shift test.The results show that the output force and action speed of the optimized mechanism can meet requirements very well.In addition,the optimized mechanism has a better performance compared to the structure of the traditional design method,which indicates that the CO method can optimize the design of hydraulic transmission.

中医药适宜技术推广协同治理机制构建研究

目的通过构建中医药适宜技术推广协同治理机制,增强群众对中医药适宜技术的接受度,为进一步提升中医药适宜技术推广应用的力度与深度提供参考。方法梳理中医药适宜技术相关政策,运用协同治理理论,从协同驱动主体、制度保障、服务资源3个维度思考中医药适宜技术推广协同运转机制。结果结合协同驱动主体、制度保障、服务资源3个维度的要素,构建横向联动、纵向同质化执行的中医药适宜技术推广协同治理机制。结论基于协同驱动主体、制度保障、服务资源3个维度构建的中医药适宜技术推广协同治理机制,对于提高中医药适宜技术的普及性、满足基层群众多元化多层次健康需求具有现实意义。

Frontiers of collaborative intelligence systems

The development of information technology has propelled technological reform in artificial intelligence(AI).To address the needs of diversified and complex applications,AI has been increasingly trending towards intelligent,collaborative,and systematized development across different levels and tasks.Research on intelligent,collaborative and systematized AI can be divided into three levels:micro,meso,and macro.Firstly,the micro-level collaboration is illustrated through the introduction of swarm intelligence collaborative methods related to individuals collaboration and decision variables collaboration.Secondly,the meso-level collaboration is discussed in terms of multi-task collaboration and multi-party collaboration.Thirdly,the macro-level collaboration is primarily in the context of intelligent collaborative systems,such as terrestrial-satellite collaboration,space-air-ground collaboration,space-air-ground-air collaboration,vehicle-road-cloud collaboration and end-edge-cloud collaboration.Finally,this paper provides prospects on the future development of relevant fields from the perspectives of the micro,meso,and macro levels.