Individual and group-based learning from complex cognitive tasks: Effects on retention and transfer efficiency

The effects of individual versus group learning (in triads) on efficiency of retention and transfer test performance in the domain of biology (heredity) among 70 high-school students were investigated. Applying cognitive load theory, the limitations of the working memory capacity at the individual level were considered an important reason to assign complex learning tasks to groups rather than to individuals. It was hypothesized that groups will have more processing capacity available for relating the information elements to each other and by doing so for constructing higher quality cognitive schemata than individuals if the high cognitive load imposed by complex learning tasks could be shared among group members. In contrast, it was expected that individuals who learn from carrying out the same complex tasks would need all available processing capacity for remembering the interrelated information elements, and, consequently, would not be able to allocate resources to working with them. This interaction hypothesis was confirmed by the data on efficiency of retention and transfer test performance; there was a favorable relationship between mental effort and retention test performance for the individual learners as opposed to a favorable relationship between transfer test performance and mental effort for the students who learned in groups.     

Hybrid control of a two-arm robot for complex tasks

For many coordinated tasks, a two-arm robot cannot be properly controlled by using a simple position control scheme and therefore requires a certain form hybrid control. Uchiyama and Dauchez recently proposed a symmetric hybrid position/force scheme for the manipulation of rigid objects rigidly held. The main results of this theory are summarized in this paper, and the limitations are pointed out. Several examples in which the relative motion of the end effectors cannot be neglected are presented: manipulation of rigid objects non-rigidly held, deformation of a flexible object, and assemblies of two objects “in space”. These tasks are analyzed and attempted control schemes are given for each of them. The dynamic effects are always neglected in this preliminary theoretical approach. An experimental setup built around two six axis PUMA arms and a parallel processing controller has been installed in order to validate our theoretical results. The hardware and software of this setup are also briefly described in this paper.     

Nonlinear control of a simulated industrial evaporation process

Three differential geometry based nonlinear control strategies, including input-output linearization, generic model control and Su-Hunt-Meyer transformation were studied on a simulation of the evaporation stage of the liquor burning process associated with the Bayer process for the production of alumina. The simulation was implemented using a symbolic algebraic software, Maple V.3. The overall results show that all three nonlinear strategies, especially the input-output linearization, provide better control performance than the classical linear control theory. A simplified solution algorithm for the Su-Hunt-Meyer transformation is proposed. A brief discussion on the industrial implementation of the nonlinear technology is also given.     

Spice: a cognitive agent framework for computational crowd simulations in complex environments

Pedestrian behavior is an omnipresent topic, but the underlying cognitive processes and the various influences on movement behavior are still not fully understood. Nonetheless, computational simulations that predict crowd behavior are essential for safety, economics, and transport. Contemporary approaches of pedestrian behavior modeling focus strongly on the movement aspects and seldom address the rich body of research from cognitive science. Similarly, general purpose cognitive architectures are not suitable for agents that can move in spatial domains because they do not consider the profound findings of pedestrian dynamics research. Thus, multi-agent simulations of crowd behavior that strongly incorporate both research domains have not yet been fully realized. Here, we propose the cognitive agent framework Spice. The framework provides an approach to structure pedestrian agent models by integrating concepts of pedestrian dynamics and cognition. Further, we provide a model that implements the framework. The model solves spatial sequential choice problems in sufficient detail, including movement and cognition aspects. We apply the model in a computer simulation and validate the Spice approach by means of data from an uncontrolled field study. The Spice framework is an important starting point for further research, as we believe that fostering interdisciplinary modeling approaches will be highly beneficial to the field of pedestrian dynamics.     

Exact control of genetic networks in a qualitative framework: The bistable switch example

A qualitative method to control piecewise affine differential systems is proposed and explored for application to genetic regulatory networks. This study considers systems whose outputs and inputs are of a qualitative form, well suited to experimental devices: the measurements indicate whether the variables are “strongly” or “weakly” expressed, that is, only the region of the state space where trajectories evolve at each instant can be known. The control laws are piecewise constant functions in each region and in time, and are only allowed to take three qualitative values corresponding to no control (u=1), high synthesis rates () or low synthesis rates (). The problems of controlling the bistable switch to each of its steady states is considered. Exact solutions are given to asymptotically control the system to either of its two stable steady states. Two approximate solutions are suggested to the problem of controlling the system to the unstable steady state: either control to a neighborhood of the state, or in the form of a periodic cycle that passes through the state.     

ManufAg: a multi-agent-system framework for production control of complex manufacturing systems

In this paper, we present a framework for the implementation of multi-agent-systems for production control of complex manufacturing systems. We present the results of a requirement analysis for production control systems for complex manufacturing systems; then we describe the framework design criteria. Our framework supports the inclusion of distributed hierarchical decision-making schemes into the production control. Furthermore, in order to increase the coordination abilities of multi-agent-systems, we follow the decision-making and staff agent architecture suggested in the PROSA reference architecture. We indicate the usage of the framework for designing and implementing an agent-based production control system for semiconductor manufacturing processes in a case study.     

On industrial learning and training for the factories of the future: a conceptual, cognitive and technology framework

The manufacturing education addresses significant challenges in view of paving the way for the human capital of the Factories of the Future. This paper introduces a specification framework for the delivery of industrial learning and training, addressing the needs for the “knowledge” workers of the factories of the future. A review of the relevant background, including the activities associated with the industrial learning and its basic methods as well as some emerging paradigms, is first provided. A Teaching Factory based paradigm is then suggested as the conceptual foundation of this framework. A cognitive framework, addressing the major building blocks of the industrial learning process, namely the attitude the knowledge the skills and the competences is being defined. An appropriate technology framework, dealing with the needs of engineers and blue-collar workers, for practising the manufacturing knowledge content within digital environments, is suggested at this point. Finally, the approach of the future implementation of the specific framework is being discussed.     

先进控制技术在气相低密度聚乙烯装置中的应用

线型低密度聚乙烯(LLDPE)作为工业生产的重要原料,对其控制有着较高的要求,但是由于生产过程工况复杂,非线性特性明显、具有混沌特性、多变量耦合、缺乏质量指标的在线反馈、大时间滞后等特性,基于常规的控制策略已经不能满足对精细控制的要求.由此,开发了LLDPE装置的先进控制技术,对其质量指标及操作约束进行实时的在线估计及质量指标的闭环优化控制.实施结果表明,先进控制提高丁,装置的平稳性,实现了质量指标的在线闭环控制,提高了优级品率,降低了物耗.     

Decomposition methodology for classification tasks: a meta decomposer framework

The idea of decomposition methodology for classification tasks is to break down a complex classification task into several simpler and more manageable sub-tasks that are solvable by using existing induction methods, then joining their solutions together in order to solve the original problem. In this paper we provide an overview of very popular but diverse decomposition methods and introduce a related taxonomy to categorize them. Subsequently, we suggest using this taxonomy to create a novel meta-decomposer framework to automatically select the appropriate decomposition method for a given problem. The experimental study validates the effectiveness of the proposed meta-decomposer on a set of benchmark datasets.     

复杂工业过程的可变时域分散协调优化方法

针对大型工业生产过程,提出了一种基于约减状态空间分解的可变预测窗口长度预测控制算法。采用预测控制滚动优化的方法,在每个滚动窗口内,通过内外部状态分解减小优化问题求解规模,在此基础上,采用可变时间窗口长度的方式巧妙地解决了多时间尺度子系统之间的优化协调问题。以某炼铁厂的实际生产数据为基础进行了仿真实验,并与现有的三种预测控制算法进行了比较。结果表明,该方法很好地处理了计算实时性和优化性能之间的矛盾,综合性能最优。     

工业过程中液位的区间控制

液位的定位控制是造成物流频繁变化的主要因素之一。在工业过程中,充分利用液位所具有的容量特性对物流流量的缓冲作用,尽可能地对设备或容器的液位实行区间控制,能够大大提高生产装置的平稳运行水平和产品质量。模型预测控制是一种行之有效的方法,在吸收稳定装置的实际应用中取得了非常好的效果。     

Software process representation and analysis for framework instantiation

Object-oriented frameworks are currently regarded as a promising technology for reusing designs and implementations. However, developers find there is still a steep learning curve when extracting the design rationale and understanding the framework documentation during framework instantiation. Thus, instantiation is a costly process in terms of time, people, and other resources. These problems raise a number of questions including: "How can we raise the level of abstraction in which the framework instantiation is expressed, reasoned about and implemented?" "How can the same high-level design abstractions that were used to develop the framework be used during framework instantiation instead of using source code as is done currently?" "How can we define extended design abstractions that can allow framework instantiation to be explicitly represented and validated?" We present an approach to framework instantiation based on software processes that addresses these issues. Our main goal is to represent the framework design models in an explicit and declarative way, and support changes to this design based on explicit instantiation tasks based on software processes while maintaining system integrity, invariants, and general constraints. In this way, the framework instantiation can be performed in a valid and controlled way.     

On-line identification and nonlinear control of an industrial pH process

The control of pH for industrial processes is a highly nonlinear and challenging problem, especially when the nonlinearity is unknown and time-varying. In this work, a controller is developed and implemented for an industrial pH process with unknown chemical composition. The method used is an application of a general algorithm for pH processes, which is based on a representation of the nonlinearity that leads to on-line identification of a small number of parameters. The results show good performance of the pH control algorithm under normal operating conditions and satisfactory performance during several unusual hardware or process problems.     

Interaction between process design and process control: economic analysis of process dynamics

A method is presented to assess the impact of disturbances on plant economic performance. The method is suitable for use during process design, and can be used to help evaluate alternative process structures or alternative control schemes for a given process. Non-linear steady-state optimization is performed to determine the best operating point in the absence of disturbances. Frequency response analysis of a linearized plant dynamic model is used to estimate the effects of plant disturbances on this ideal performance under a variety of control strategies. The method is illustrated on a case study involving minerals flotation circuit design.     

Plant-wide hierarchical optimization and control of an industrial hydrocracking process

Hydrocracking is a crucial refinery process in which heavy hydrocarbons are converted to more valuable, low-molecular weight products. Hydrocracking plants operate with large throughputs and varying feedstocks. In addition the product specifications change due to varying economic and market conditions. In such a dynamic operating environment, the potential gains of real-time optimization (RTO) and control are quite high. At the same time, real-time optimization of hydrocracking plants is a challenging task. A complex network of reactions, which are difficult to characterize, takes place in the hydrocracker. The reactor effluent affects the operation of the fractionator downstream and the properties of the final products. In this paper, a lumped first-principles reactor model and an empirical fractionation model are used to predict the product distribution and properties on-line. Both models have been built and validated using industrial data. A cascaded model predictive control (MPC) structure is developed in order to operate both the reactor and fractionation column at maximum profit. In this cascade structure, reactor and fractionation units are controlled by local decentralized MPC controllers whose set-points are manipulated by a supervisory MPC controller. The coordinating action of the supervisory MPC controller accomplishes the transition between different optimum operating conditions and helps to reject disturbances without violating any constraints. Simulations illustrate the applicability of the proposed method on the industrial process.     

Bridging the gap between analysis and design: improving existing driver interfaces with tools from the framework of cognitive work analysis

Two studies of train driving are presented, both within the framework of cognitive work analysis. In the first study, the modelling tool abstraction–decomposition space is adapted to routine conditions, making the analysis more representative for normal procedures. A major contribution to these analyses was the use of the method for ‘collegial verbalisation’. One particular advantage with this method over other verbalisation methods is that it supplies the analysts with data that contain much more information, but not at the expense of being more subjective. On the contrary, this method produces think-aloud protocols from video-recordings that do not have to be interpreted by the researcher. From these analyses, it was possible to distinguish information that is an intrinsic part of the train driver task from information that is dependent on the configuration and design of the current support system. The analyses show that the driver works in three rather separate time intervals with a long-range, a short-term and an immediate sense of perspective. The driver switches between these while travelling between two stations. Based on these behaviour-shaping constraints, a prototype of a planning area of a driver interface was developed, making feed-forward planning possible for the driver. Four design iterations were completed, using a user-centred system design (UCSD) approach. Early tests show that the planning area of the interface supports the feed-forward decision strategy used by drivers who prefer an active driving style. However, the driver group also made substantial changes in the design, indicating that UCSD is an efficient tool in order to capture user competencies, and to bridge the gap between analysis and design.     

Multivariable linearizing control of an industrial sugar crystallization process

This paper illustrates the benefits of a multivariable linearizing control approach applied to an industrial crystallization process. This relevant approach proposes a setpoint tracking for the crystal mass/concentration couple. In this purpose, a model dedicated to last stage crystallization is designed, without consideration of crystal size distribution. The controlled variables, unavailable, are obtained using an extended Luenberger observer. The observer is validated on industrial data and shows good performance in both convergence rate and accuracy. The performance of the proposed linearizing strategy, which application to cane sugar crystallization constitutes a real novelty, is tested via simulation. The good performance in setpoint tracking, even in presence of noise, disturbances and modeling error, allows to consider a significant improvement of the global productivity.