Knowledge networks in new product development projects: A transactive memory perspective

Even though an individual's knowledge network is known to contribute to the effectiveness and efficiency of his or her work in groups, the way that network building occurs has not been carefully investigated. In our study, activities of new product development teams were analyzed to determine the antecedents and consequences on the transactive memory systems, the moderating affect of task complexity was also considered. We examined 69 new product development projects and found that team stability, team member familiarity, and interpersonal trust had a positive impact on the transactive memory system and also had a positive influence on team learning, speed-to-market, and new product success. Further, we found that the impact of the transactive memory system on team learning, speed-to-market, and new product success was higher when there was a higher task complexity. Theoretical and managerial implications of the study findings are discussed.     

过程计算机通讯接口的开发

介绍了过程控制系统的组成和功能,并详细分析了模块之间数据传递方式.提出了系统软件的设计规划,建立了数据通讯与模型相分离的软件体系结构,简化了系统的调试和维护,易于扩展的设计使过程控制系结的适用性更强.     

实现人工神经网络知识增殖能力的一种方法

具有知识增殖能力的神经学习系统是人工神经网络发展的一个重要方向，备受研究人员的关注．传统上对神经学习系统知识的增殖或重用研究偏重于对个体网络的改造，根据知识积累和继承的思想，引入自治神经网络(autonomous artificial neural network，AANN)的理念，以此作为构造知识可增殖神经学习系统的基础，利用群体网络的方法成功解决了神经学习系统的拓展和知识增殖问题．AANN和一般神经网络的区别在于其自治能力，采用AANN模块构造的神经学习系统，具有知识增殖能力，其可靠性、可拓展性和灵活性都得到提高．实验结果表明，该方法构造的群体网络系统可有效继承其模块所学习的知识．     

Modeling and projection in software development

Modeling is a fundamental technique for coping with undesirable complexity in constructing and reasoning about software systems. The concept of modeling can be applied to software design and implementation in two ways: a design can be viewed as an abstract model of the system it represents, and designs and implementations may be represented by even more abstract, simplified models for purposes of analysis. A discussion of these concepts and their applications is presented, including a case study showing the use of modeling in the debugging of an actual software system and remarks on research in progress.     

Reinforcement learning of goal-directed obstacle-avoiding reaction strategies in an autonomous mobile robot

In this paper we argue for building reactive autonomous mobile robots through reinforcement connectionist learning. Nevertheless, basic reinforcement learning is a slow process. This paper describes an architecture which deals with complex— high-dimensional and/or continuous—situation and action spaces effectively. This architecture is based on two main ideas. The first is to organize the reactive component into a set of modules in such a way that, roughly, each one of them codifies the prototypical action for a given cluster of situations. The second idea is to use a particular kind of planning for figuring out what part of the action space deserves attention for each cluster of situations. Salient features of the planning process are that it is grounded and that it is invoked only when the reactive component does not generalize correctly its previous experience to the new situation. We also report our experience in solving a basic task that most autonomous mobile robots must face, namely path finding.     

基于指示信号方式实现跨时钟域数据传输的方法

随着片上系统(SoC)技术的发展,芯片内各个模块交流频繁。异步系统因功耗低、速度提升潜力大和抗干扰能力强而备受青睐,但是异步电路设计复杂,数据的跨时钟域传输是亟需解决的问题。国际上目前最流行的方式是FIFO,但随着SoC复杂度的提升,一个系统上集成上百个模块,利用FIFO将会占用大量的资源,产生很大的功耗。通过分析异步传输的特点,提出一种使用指示信号来实现跨时钟域数据传输的方法,该方法与FIFO相比,在性能不减的情况下大大降低了功耗及其复杂度。利用Verilog对两个模块(CPU和FPGA)的跨时钟域数据传输进行设计仿真,通过Xilinx公司的Vivado硬件验证了其可行性。最后通过与FIFO方式的设计进行对比,说明该方法比FIFO具有更好的应用价值。     

Crystalline Robots: Self-Reconfiguration with Compressible Unit Modules

We discuss a robotic system composed of Crystalline modules. Crystalline modules can aggregate together to form distributed robot systems. Crystalline modules can move relative to each other by expanding and contracting. This actuation mechanism permits automated shape metamorphosis. We describe the Crystalline module concept and show the basic motions that enable a Crystalline robot system to self-reconfigure. We present an algorithm for general self-reconfiguration and describe simulation experiments.     

Online probabilistic learning for fuzzy inference system

Online learning is a key methodology for expert systems to gracefully cope with dynamic environments. In the context of neuro-fuzzy systems, research efforts have been directed toward developing online learning methods that can update both system structure and parameters on the fly. However, the current online learning approaches often rely on heuristic methods that lack a formal statistical basis and exhibit limited scalability in the face of large data stream. In light of these issues, we develop a new Sequential Probabilistic Learning for Adaptive Fuzzy Inference System (SPLAFIS) that synergizes the Bayesian Adaptive Resonance Theory (BART) and Rule-Wise Decoupled Extended Kalman Filter (RDEKF) to generate the rule base structure and refine its parameters, respectively. The marriage of the BART and RDEKF methods, both of which are built upon the maximum a posteriori (MAP) principle rooted in the Bayes’ rule, offers a comprehensive probabilistic treatment and an efficient way for online structural and parameter learning suitable for large, dynamic data stream. To manage the model complexity without sacrificing its predictive accuracy, SPLAFIS also includes a simple procedure to prune inconsequential rules that have little contribution over time. The predictive accuracy, structural simplicity, and scalability of the proposed model have been exemplified in empirical studies using chaotic time series, stock index, and large nonlinear regression datasets.     

Active Learning for Vision-Based Robot Grasping

Reliable vision-based grasping has proved elusive outside of controlled environments. One approach towards building more flexible and domain-independent robot grasping systems is to employ learning to adapt the robot's perceptual and motor system to the task. However, one pitfall in robot perceptual and motor learning is that the cost of gathering the learning set may be unacceptably high. Active learning algorithms address this shortcoming by intelligently selecting actions so as to decrease the number of examples necessary to achieve good performance and also avoid separate training and execution phases, leading to higher autonomy. We describe the IE-ID3 algorithm, which extends the Interval Estimation (IE) active learning approach from discrete to real-valued learning domains by combining IE with a classification tree learning algorithm (ID-3). We present a robot system which rapidly learns to select the grasp approach directions using IE-ID3 given simplified superquadric shape approximations of objects. Initial results on a small set of objects show that a robot with a laser scanner system can rapidly learn to pick up new objects, and simulation studies show the superiority of the active learning approach for a simulated grasping task using larger sets of objects. Extensions of the approach and future areas of research incorporating more sophisticated perceptual and action representation are discussed