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.     

Evaluation of advanced industrial control projects: a framework for determining economic benefits

A framework for the evaluation of advanced control projects is illustrated in this paper by way of a case study on level control in mineral flotation. The approach taken is to investigate how improvements brought about by advanced control can be measured to a required level of statistical significance, after the controller has been commissioned. Measured improvements are translated into increases in cash flow that result from implementing an advanced controller, which is then used for project evaluation. As an introduction to the case study, a framework for establishing advanced controllers for industrial processes, which culminates in determining economic benefits, is discussed. Ways of obtaining controller benefits through the reduction of downtime and product variations, are also described. The risk of implementing an advanced control project is discussed together with typical scenarios under which such projects are often undertaken.     

端边云协同的复杂工业过程运行控制智能系统

针对难以建立数学模型的复杂工业运行控制过程,利用可获得的过程控制系统设定值和运行指标以及相关变量的工业大数据和运行控制过程特性,将系统辨识与深度学习相结合,建立以实际运行指标以及相关变量为输入,以实际过程控制系统设定值为输出的运行控制过程数字孪生模型,提出云-边协同的过程控制系统设定值智能控制方法.所提出方法由云-运行控制过程数字孪生模型、边-过程控制系统设定值智能控制模型和自校正机制组成.将工业互联网与工业过程控制系统相结合,提出端边云协同的工业运行控制智能系统的架构和功能,采用所提出控制系统设定值智能控制方法,研制工业过程运行控制智能系统,并在选矿关键设备—–高压辊磨成功应用.所提出系统安全、可靠和优化运行,取得了显著的节能减排效果.     

Factors influencing effective use of big data: A research framework

Information systems (IS) research has explored “effective use” in a variety of contexts. However, it is yet to specifically consider it in the context of the unique characteristics of big data. Yet, organizations have a high appetite for big data, and there is growing evidence that investments in big data solutions do not always lead to the derivation of intended value. Accordingly, there is a need for rigorous academic guidance on what factors enable effective use of big data. With this paper, we aim to guide IS researchers such that the expansion of the body of knowledge on the effective use of big data can proceed in a structured and systematic manner and can subsequently lead to empirically driven guidance for organizations. Namely, with this paper, we cast a wide net to understand and consolidate from literature the potential factors that can influence the effective use of big data, so they may be further studied. To do so, we first conduct a systematic literature review. Our review identifies 41 factors, which we categorize into 7 themes, namely data quality; data privacy and security and governance; perceived organizational benefit; process management; people aspects; systems, tools, and technologies; and organizational aspects. To explore the existence of these themes in practice, we then analyze 45 published case studies that document insights into how specific companies use big data successfully. Finally, we propose a framework for the study of effective use of big data as a basis for future research. Our contributions aim to guide researchers in establishing the relevance and relationships within the identified themes and factors and are a step toward developing a deeper understanding of effective use of big data.     

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.     

A general analysis and control framework for process systems with inventory recycling

In this work, we generalize our previous results concerning the impact of material recycling and energy recovery on plant dynamics and control. We define a generic class of integrated process systems, in which an extensive quantity that obeys conservation laws is recovered from the process output and recycled to the process feed; the operation of the system is assumed to be subject to time‐varying, measurable disturbances. We establish, in this general case, that integration is conducive to the emergence of a two‐time‐scale dynamic behavior and derive reduced‐order models for the dynamics in each time scale. Subsequently, we postulate a hierarchical control framework that exploits these dynamics results in the design of coordinated fast and slow feedback/feedforward controllers and formulate a stability result for the closed‐loop system. We demonstrate these concepts on a case study concerning an energy‐integrated process. Copyright © 2013 John Wiley & Sons, Ltd.     

Modelling for control analysis and design in complex industrial separation and liquefaction processes

Dynamic simulation of complex industrial systems is discussed, and a summary is presented of over a decade of work in the modelling, simulation and control of cryogenic separation and liquefaction processes. The work includes not only successful applications but also the development of tools to facilitate the construction of the simulation flowsheets and their effective use in control system analysis and design. The use of these tools and of two commercial dynamic simulation packages is reviewed. The question of what is a required level of modelling detail in dynamic simulation applications is addressed.     

Semiconductor manufacturing process control and monitoring: A fab-wide framework

The semiconductor industry has started the technology transition from 200 mm to 300 mm wafers to improve manufacturing efficiency and reduce manufacturing cost. These technological changes present a unique opportunity to optimally design the process control systems for the next generation fabs. In this paper we first propose a hierarchical fab-wide control framework with the integration of 300 mm equipment and metrology tools and highly automated material handling system. Relevant existing run-to-run technology is reviewed and analyzed in the fab-wide control context. Process and metrology data monitoring are discussed with an example. Missing components are pointed out as opportunities for future research and development. Concluding remarks are given at the end of the paper.     

Timing of cueing in complex problem-solving tasks: Learner versus system control

Task-specific cueing formats that promote the automation and construction of problem-solving schemas should ideally be presented just in time to students learning to solve complex problems. This article reports experimental work comparing learner-controlled cueing, system-controlled cueing, and no cueing among 34 sophomore law students in a multimedia practical aimed at learning to prepare and hold a plea in court. The cueing consisted of a combination of process worksheets (PW) and worked out examples (WOE). Our main hypotheses that participants with cueing would outperform those without cueing and that participants with learner-controlled cueing would outperform those with system-controlled cueing were partly confirmed by the learning and transfer outcomes on a training and transfer task. Theoretical and practical implications of these findings are discussed.     

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.     

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.     

Information problem solving by experts and novices: analysis of a complex cognitive skill

In (higher) education students are often faced with information problems: tasks or assignments that require them to identify information needs, locate corresponding information sources, extract and organize relevant information from each source, and synthesize information from a variety of sources. It is often assumed that students master this complex cognitive skill of information problem solving all by themselves. In our point of view, however, explicit and intensive instruction is necessary. A skill decomposition is needed in order to design instruction that fosters the development of information problem solving. This research analyzes the information problem solving process of novices and experts in order to reach a detailed skill decomposition. Results reveal that experts spend more time on the main skill ‘define problem’ and more often activate their prior knowledge, elaborate on the content, and regulate their process. Furthermore, experts and novices show little differences in the way they search the Internet. These findings formed the basis for formulating instructional guidelines.