Trajectory analysis of nonisothermal tubular reactor nonlinear models

The existence and uniqueness of the state trajectories (temperature and reactant concentration) are analyzed for nonisothermal plug flow and axial dispersion tubular reactor models. It is mainly shown that these trajectories exist on the whole (nonnegative real) time axis and the set of all physically feasible state values is invariant under the dynamics equations. The main nonlinearity in the model originates from the Arrhenius-type kinetics term in the model equations. The analysis essentially uses Lipschitz and dissipativity properties of the nonlinear operator involved in the dynamics and the concept of state trajectory positivity.     

Cell population models for bifurcation analysis and nonlinear control of continuous yeast bioreactors

Saccharomyces cerevisiae (baker's yeast) can exhibit sustained oscillations over a wide range of operating conditions when produced in a continuous bioreactor. In this paper the bifurcations leading to these periodic solutions are investigated using an unstructured, segregated model in which the population balance equation (PBE) for the cell mass distribution is coupled to the mass balance of the rate limiting substrate. The PBE model is shown to produce periodic solutions over a range of dilution rates due to the presence of two supercritical Hopf bifurcations. The problem of oscillation attenuation using nonlinear feedback control with four candidate input/output variable pairings is investigated. The controller designs are based on a low dimensional moment representation of the PBE model. The performance of the nonlinear controllers are compared and discussed.     

Optimal control of (bio-)chemical reactors: generic properties of time and space dependent optimization

This paper deals with the optimal control (OC) in the space domain of (bio-)chemical reactors. As an example, the determination of optimal heat exchanger temperature profiles of exothermic tubular reactors under the assumption of steady-state and plug flow characteristics is investigated. To enable a trade-off between process performance and energy loss a weighted cost criterion is defined. Application of the Minimum Principle of Pontryagin leads to extremal control profile structures which are very similar to those obtained during optimization (in the time domain) of well mixed bioreactors. The latter problem has been analyzed in detail over the last two decades. The analogy is reflected at various levels during analytical optimal control computations.     

(C, A, B)-pairs in infinite dimensions

We introduce the notion of (C, A, B)-pairs for infinite-dimensional systems and relate this to the concept of invariance under an extended operator. As an application we solve the disturbance decoupling problem with measurement feedback.     

Model order selection of nonlinear input–output models––a clustering based approach

Selecting the order of an input–output model of a dynamical system is a key step toward the goal of system identification. The false nearest neighbors algorithm (FNN) is a useful tool for the estimation of the order of linear and nonlinear systems. While advanced FNN uses nonlinear input–output data-based models for the model-based selection of the threshold constant that is used to compute the percentage of false neighbors, the computational effort of the method increases along with the number of data and the dimension of the model. To increase the efficiency of this method, in this paper we propose a clustering-based algorithm. Clustering is applied to the product space of the input and output variables. The model structure is then estimated on the basis of the cluster covariance matrix eigenvalues. The main advantage of the proposed solution is that it is model-free. This means that no particular model needs to be constructed in order to select the order of the model, while most other techniques are ‘wrapped' around a particular model construction method. This saves the computational effort and avoids a possible bias due to the particular construction method used. Three simulation examples are given to illustrate the proposed technique: estimation of the model structure for a linear system, a polymerization reactor and the van der Vusse reactor.     

Closed formulae for a parametric-mixed-sensitivity problem for Pritchard-Salamon systems

The parametric-mixed-sensitivity problem is posed for the class of irrational transfer matrices with a realization as a Pritchard-Salamon system. An exact solution is obtained in terms of two uncoupled, standard Riccati equations.     

A socio-technical analysis of functional properties in a joint cognitive system: a case study in an aircraft cockpit

Abstract

In a socio-technical work domain, humans, device interfaces and artefacts all affect transformations of information flow. Such transformations, which may involve a change of auditory to visual information & vice versa or alter semantic approximations into spatial proximities from instruments readings, are generally not restricted to solely human cognition. This paper applies a joint cognitive system approach to explore a socio-technical system. A systems ergonomics perspective is achieved by applying a multi-layered division to transformations of information between, and within, human and technical agents. The approach uses the Functional Resonance Analysis Method (FRAM), but abandons the traditional boundary between medium and agent in favour of accepting aircraft systems and artefacts as agents, with their own functional properties and relationships. The joint cognitive system perspective in developing the FRAM model allows an understanding of the effects of task and information propagation, and eventual distributed criticalities, taking advantage of the functional properties of the system, as described in a case study related to the cockpit environment of a DC-9 aircraft.

Practitioner Summary: This research presents the application of one systemic method to understand work systems and performance variability in relation to the transformation of information within a flight deck for a specific phase of flight. By using a joint cognitive systems approach both retrospective and prospective investigation of cockpit challenges will be better understood.

Abbreviations: ATC: air traffic control; ATCO: air traffic controller; ATM: air traffic management; CSE: cognitive systems engineering; DSA: distributed situation awareness; FMS: flight management system; FMV: FRAM model visualize; FRAM: functional resonance analysis method; GF: generalised function; GW: gross weight; HFACS: human factors analysis and classification system; JCS: joint cognitive systems; PF: pilot flying; PNF: pilot not flying; SA: situation awareness; SME: subject matter expert; STAMP: systems theoretic accident model and processes; VBA: visual basic for applications; WAD: work-as-done; WAI: work-as-imagined; ZFW: zero fuel weight     

A method for exact reliability of consecutive2-out-of-(r, r)-from-(n, n):F systems

A new reliability model, consecutive 2‐out‐of‐(r, r)‐from‐(n, n):F model, is proposed. The consecutive 2‐out‐of‐(r, r)‐from‐(n, n):F system consists of a square grid of side n (containing n² components) such that the system fails if and only if there is at least one square of side r which includes among them at least two failed components. For i.i.d. case an algorithm is given for computing the reliability of the system. The reliability function can be expressed by the number of 0–1 matrices having no two or more 0s at any square of side r.     

LMI-based LSVF control of a class of nonlinear systems with parametric uncertainty: an application to an inverted pendulum system

This work centres around the stabilisation of a nonlinear system containing parametric uncertainty using a new Control Lyapunov Function (using Lie derivatives) which comes up with a linear matrix inequality-based design. The paper has three major contributions. The first one is an extension of a theorem proposed to find the convex-concave bounds of nonlinear function towards robustness. With some restrictions in the structure of the uncertainty, the theory developed here may be applied to find out the bounds of any nonlinear function with uncertainty. The next one is the main contribution of this paper in which the form of the control law obtained is linear and has several advantages from a practical point of view over almost all other nonlinear control techniques. The third one is the expansion of the proposed control scheme towards underactuated systems. To show the effectiveness of the proposed theory the controller design is attempted for both the traditional cart inverted pendulum and the more complex mobile wheeled inverted pendulum model.     

多维滑动和（MA）模型参数的自适应估计

本文利用AR模型LS梯格滤波的有关公式给出了实时辨识多维MA模型参数的递推算法,该算法建立在对模型噪声和观测的自协方差阵和互协方差阵的矩估计基础上,由一个N阶反馈形式的梯格滤波器构成,可关于时间和阶次双重递推,该算法计算量为O(N)的量级,并具备梯格滤波固有的良好数值及结构特性。     

Development of a distributed representative human model generation and analysis system (DRHM-GAS): Application to optimization of flight suit and pilot oxygen mask sizing systems

A computerized system is needed for effective determination of the sizing system of a multiple-size product. The present study developed a distributed representative human model generation and analysis system (DRHM-GAS) which can generate various sizing system candidates for a target population using different DRHM generation methods and evaluate the sizing system candidates. A five-step DRHM generation procedure (target population characterization, anthropometric variable selection, key dimension selection, grid formation, and DRHM size determination) was developed and implemented into the DRHM-GAS. A generated sizing system candidate is evaluated in the DRHM-GAS by a set of metrics including multivariate accommodation percentage and the number of sizing categories. The effectiveness of the DRHM-GAS was examined with two case studies of the development of optimal sizing systems for men's flight suit design based on the anthropometric data of US Army pilots and pilot oxygen mask design based on the facial anthropometric data of ROKAF pilots. The DRHM-GAS would be of use for product designers to establish an appropriate sizing system with efficiency.     

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. An efficient common model structure selection (CMSS) algorithm is proposed to select a common model structure, with application to EEG data modelling. The time-varying parameters for the identified common-structured model are then estimated using a sliding-window recursive least squares (SWRLS) approach. The new method can effectively detect and adaptively track and rapidly capture the transient variation of nonstationary signals, and can also produce robust models with better generalisation properties. Two examples are presented to demonstrate the effectiveness and applicability of the new approach including an application to EEG data.     

Design and analysis of linear precoders under a mean square error criterion, Part I: foundations and worst case designs

Part I of this two-part paper formulated the precoder design problem as an optimisation problem and solved it with respect to a worst case criterion. Part II studies a similar optimisation problem but with respect to a criterion measuring average performance. A stochastic optimisation algorithm is proposed for solving this problem. For special cases, closed form solutions are also given. These results indicate linear precoders reduce the effects of frequency distortion caused by multipath channels but are powerless to counteract additive white Gaussian noise. The conclusion is linear precoders should introduce only a small amount of redundancy and be used in conjunction with an error correcting code capable of combatting additive noise.     

An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices

The close price prediction model of the Zagreb Stock Exchange Crobex® index is presented in this paper. For the input/output data plan modeling the Crobex® index close price historical data are retrieved from the Zagreb Stock Exchange official internet pages. The prediction model is created in the way that for each of 5 days in advance it predicts the Crobex® close price. The prediction model is generated based on the input/output data plan by means of the adaptive neuro-fuzzy inference system method, representing the fuzzy inference system. It is of the essence to point out that for each day a separate fuzzy inference system is created by means of the adaptive neuro-fuzzy inference system method based on the same set of input/output data, the only difference being that for every separate fuzzy inference system different subsets for training and checking are used so that input variables are differently created. The input/output data set represents the historical data of the Crobex® index close price from 4 November 2010 to 24 January 2012 and the Crobex® index close price is predicted for the subsequent 5 days, the first day of prediction being 25 January 2012. After that the above mentioned input/output data set is shifted 5 days in advance and the Crobex® index close price is predicted in advance for the next 5 days starting with the last day of the input/output data set. In that way the Crobex® index close prices are predicted until 19 October 2012 based on the Crobex® index close price historical data. At the end of the paper qualitative and quantitative estimates are presented for the given approach of predicting the Crobex® index close price showing that the approach is useful for predicting within its limits.     

From 3D models to 3D prints: an overview of the processing pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step along the processing pipeline that prepares the 3D model for fabrication can now count on dozens of methods, that analyse and optimize geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during model preparation – a process globally refereed to as Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.     

Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework: A case study of the Southwest Regional Gap Analysis Project (SWReGAP)

Land-cover mapping efforts within the USGS Gap Analysis Program have traditionally been state-centered; each state having the responsibility of implementing a project design for the geographic area within their state boundaries. The Southwest Regional Gap Analysis Project (SWReGAP) was the first formal GAP project designed at a regional, multi-state scale. The project area comprises the southwestern states of Arizona, Colorado, Nevada, New Mexico, and Utah. The land-cover map/dataset was generated using regionally consistent geospatial data (Landsat ETM+ imagery (1999-2001) and DEM derivatives), similar field data collection protocols, a standardized land-cover legend, and a common modeling approach (decision tree classifier). Partitioning of mapping responsibilities amongst the five collaborating states was organized around ecoregion-based “mapping zones”. Over the course of 2¹/₂ field seasons approximately 93,000 reference samples were collected directly, or obtained from other contemporary projects, for the land-cover modeling effort. The final map was made public in 2004 and contains 125 land-cover classes. An internal validation of 85 of the classes, representing 91% of the land area was performed. Agreement between withheld samples and the validated dataset was 61% (KHAT = .60, n = 17,030). This paper presents an overview of the methodologies used to create the regional land-cover dataset and highlights issues associated with large-area mapping within a coordinated, multi-institutional management framework.     

The process of team communication in multi-cultural contexts: An empirical study using Bales’ interaction process analysis (IPA)

The main goal of the present study was to empirically investigate the manner in which cultural diversity and communication mode affect team communication process. Two independent variables were manipulated in the study: (1) cultural diversity (homogeneous versus heterogeneous); and (2) communication mode (face-to-face versus computer-mediated communication). Team members’ communication exchanges were measured by using Bales’ interaction process analysis (IPA) that divides the function (as opposed to the topical content) of communication during group discussion into either task-related or socio-emotional contributions at two-levels: communication profile and functional area. Results of this study indicated homogeneous teams used more showing agreement, giving opinions, and showing tension communication patterns compared to heterogeneous teams. Additionally, when considering the higher-level functional areas, teams using face-to-face (FTF) communication strategies were characterized by greater positive socio-emotional communication, more attempted answers, more questions, and more frequent negative socio-emotional communications. The results of the present study also suggested that the impact of communication mode on team communication varies by the level of diversity within the team. The outcomes of this research should have a broad impact on the management of a multi-cultural team, a task-oriented team consisting of people with different national cultures. Relevance to industry: Results of the present study should provide valuable insights into the ways in which cultural diversity and communication mode influence the communication mechanisms which take place among members during team interaction.     

The effects of computer simulation and animation (CSA) on students' cognitive processes: A comparative case study in an undergraduate engineering course

This study focuses on the investigation of the effects of computer simulation and animation (CSA) on students' cognitive processes in an undergraduate engineering course. The revised Bloom's taxonomy, which consists of six categories in the cognitive process domain, was employed in this study. Five of the six categories were investigated, including remember, understand, apply, analyze, and evaluate. Data were collected via a think‐aloud protocol involving two groups of student participants: One group learned a worked example problem with a CSA module, and the other group learned the same problem with traditional textbook‐style instruction. A new concept called frequency index was proposed for use in qualitative research that involves the quantitative comparison of the overall popularity of a particular mental activity performed by two groups of students. The results show that as compared to traditional textbook‐style instruction, CSA significantly increases students' activities in the understand category of the revised Bloom's taxonomy during learning and significantly increases students' activities in the understand, apply, analyse, and evaluate categories during subsequent problem‐solving. That learning via CSA has a profound impact on subsequent problem‐solving is attributed to intensive human–computer interactions built in the CSA learning module.     

Subtractive clustering attribute weighting (SCAW) to discriminate the traffic accidents on Konya-Afyonkarahisar highway in Turkey with the help of GIS: A case study

A case study including the discrimination of traffic accidents as accident free and accident cases on Konya-Afyonkarahisar highway in Turkey using the proposed hybrid method based on combining of a new data preprocessing method called subtractive clustering attribute weighting (SCAW) and classifier algorithms with the help of Geographical Information System (GIS) technology has been conducted. In order to improve the discrimination of classifier algorithms including artificial neural network (ANN), adaptive network based fuzzy inference system (ANFIS), support vector machine, and decision tree, using data preprocessing need in solution of these kinds of problems (traffic accident case study). So, we have proposed a novel data preprocessing method called subtractive clustering attribute weighting (SCAW) and combined with classifier algorithms. In this study, the experimental data has been obtained by means of using GIS. The obtained GIS attributes are day, temperature, humidity, weather conditions, and month of occurred accident. To evaluate the performance of the proposed hybrid method, the classification accuracy, sensitivity and specificity values have been used. The experimental obtained results are 53.93%, 52.25%, and 38.76% classification successes using alone ANN, ANFIS, and SVM with RBF kernel type, respectively. As for the proposed hybrid method, the classification accuracies of 67.98%, 70.22%, and 61.24% have been obtained using the combination of SCAW with ANN, the combination of SCAW with SVM (radial basis function (RBF) kernel type), and the combination of SCAW with ANFIS, respectively. The proposed SCAW method with the combination of classifier algorithms has been achieved the very promising results in the discrimination of traffic accidents.