Comparison of deterministic, stochastic and fuzzy logic uncertainty modelling for capacity extension projects of DI/WFI pharmaceutical plant utilities with variable/dynamic demand

The last 30 years have seen Fuzzy Logic (FL) emerging as a method either complementing or challenging stochastic methods as the traditional method of modelling uncertainty. But the circumstances under which FL or stochastic methods should be used are shrouded in disagreement, because the areas of application of statistical and FL methods are overlapping with differences in opinion as to when which method should be used. Lacking are practically relevant case studies comparing these two methods. This work compares stochastic and FL methods for the assessment of spare capacity on the example of pharmaceutical high purity water (HPW) utility systems. The goal of this study was to find the most appropriate method modelling uncertainty in industrial scale HPW systems. The results provide evidence which suggests that stochastic methods are superior to the methods of FL in simulating uncertainty in chemical plant utilities including HPW systems in typical cases whereby extreme events, for example peaks in demand, or day-to-day variation rather than average values are of interest. The average production output or other statistical measures may, for instance, be of interest in the assessment of workshops. Furthermore the results indicate that the stochastic model should be used only if found necessary by a deterministic simulation. Consequently, this thesis concludes that either deterministic or stochastic methods should be used to simulate uncertainty in chemical plant utility systems and by extension some process system because extreme events or the modelling of day-to-day variation are important in capacity extension projects. Other reasons supporting the suggestion that stochastic HPW models are preferred to FL HPW models include: 1. The computer code for stochastic models is typically less complex than a FL models, thus reducing code maintenance and validation issues. 2. In many respects FL models are similar to deterministic models. Thus the need for a FL model over a deterministic model is questionable in the case of industrial scale HPW systems as presented here (as well as other similar systems) since the latter requires simpler models. 3. A FL model may be difficult to "sell" to an end-user as its results represent "approximate reasoning" a definition of which is, however, lacking. 4. Stochastic models may be applied with some relatively minor modifications on other systems, whereas FL models may not. For instance, the stochastic HPW system could be used to model municipal drinking water systems, whereas the FL HPW model should or could not be used on such systems. This is because the FL and stochastic model philosophies of a HPW system are fundamentally different. The stochastic model sees schedule and volume uncertainties as random phenomena described by statistical distributions based on either estimated or historical data. The FL model, on the other hand, simulates schedule uncertainties based on estimated operator behaviour e.g. tiredness of the operators and their working schedule. But in a municipal drinking water distribution system the notion of "operator" breaks down. 5. Stochastic methods can account for uncertainties that are difficult to model with FL. The FL HPW system model does not account for dispensed volume uncertainty, as there appears to be no reasonable method to account for it with FL whereas the stochastic model includes volume uncertainty.

Typical delay determines waiting time on periodic-food schedules: Static and dynamic tests.

Pigeons and other animals soon learn to wait (pause) after food delivery on periodic-food schedules before resuming the food-rewarded response. Under most conditions the steady-state duration of the average waiting time, t, is a linear function of the typical interfood interval. We describe three experiments designed to explore the limits of this process. In all experiments, t was associated with one key color and the subsequent food delay, T, with another. In the first experiment, we compared the relation between t (waiting time) and T (food delay) under two conditions: when T was held constant, and when T was an inverse function of t. The pigeons could maximize the rate of food delivery under the first condition by setting t to a consistently short value; optimal behavior under the second condition required a linear relation with unit slope between t and T. Despite this difference in optimal policy, the pigeons in both cases showed the same linear relation, with slope less than one, between t and T. This result was confirmed in a second parametric experiment that added a third condition, in which T + t was held constant. Linear waiting appears to be an obligatory rule for pigeons. In a third experiment we arranged for a multiplicative relation between t and T (positive feedback), and produced either very short or very long waiting times as predicted by a quasi-dynamic model in which waiting time is strongly determined by the just-preceding food delay.

Parameterisation of bivalve functional traits for mechanistic eco-physiological dynamic energy budget (DEB) models

Mechanistic models such as those based on dynamic energy budget (DEB) theory are emergent ecomechanics tools to investigate the extent of fitness in organisms through changes in life history traits as explained by bioenergetic principles. The rapid growth in interest around this approach originates from the mechanistic characteristics of DEB, which are based on a number of rules dictating the use of mass and energy flow through organisms. One apparent bottleneck in DEB applications comes from the estimations of DEB parameters which are based on mathematical and statistical methods (covariation method). The parameterisation process begins with the knowledge of some functional traits of a target organism (e. g. embryo, sexual maturity and ultimate body size, feeding and assimilation rates, maintenance costs), identified from the literature or laboratory experiments. However, considering the prominent role of the mechanistic approach in ecology, the reduction of possible uncertainties is an important objective. We propose a revaluation of the laboratory procedures commonly used in ecological studies to estimate DEB parameters in marine bivalves. Our experimental organism was Brachidontes pharaonis. We supported our proposal with a validation exercise which compared life history traits as obtained by DEBs (implemented with parameters obtained using classical laboratory methods) with the actual set of species traits obtained in the field. Correspondence between the 2 approaches was very high (>95%) with respect to estimating both size and fitness. Our results demonstrate a good agreement between field data and model output for the effect of temperature and food density on age-size curve, maximum body size and total gamete production per life span. The mechanistic approach is a promising method of providing accurate predictions in a world that is under in creasing anthropogenic pressure.

Simulation of Dynamic Operation of a Single-Cylinder Two-Stroke Engine

Presented is a study that expands the body of knowledge on the effect of in-cycle speed fluctuations on performance of small engines. It uses the engine and drivetrain models developed previously by Callahan, et al. (1) to examine a variety of engines. The predicted performance changes due to drivetrain effects are shown in each case, and conclusions are drawn from those results. The single-cylinder, high performance four-stroke engine showed significant changes in predicted performance compared to the prediction with zero speed fluctuation in the model. Measured speed fluctuations from a firing Yamaha YZ426 engine were applied to the simulation in addition to data from a simple free mass model. Both methods predicted similar changes in performance. The multiple-cylinder, high performance two-stroke engine also showed significant changes in performance depending on the firing configuration. With both engines, the change in performance diminished with increasing mean engine speed. The low output, single-cylinder two-stroke engine simulation showed only a negligible change in performance, even with high amplitude speed fluctuations. Because the torque versus engine speed characteristic for the engine was so flat, this was expected. The cross-charged, multi-cylinder two-stroke engine also showed only a negligible change in performance. In this case, the combination of a relatively high inertia rotating assembly and the multiple cylinder firing events within the revolution smoothing the torque pulsations reduced the speed fluctuation amplitude itself.

Identification of dynamic systems under closed-loop control

This paper introduces a novel modelling framework for identifying dynamic models of systems that are under feedback control. These models are identified under closed-loop conditions and produce a joint representation that includes both the plant and controller models in state space form. The joint plant/controller model is identified using subspace model identification (SMI), which is followed by the separation of the plant model from the identified one. Compared to previous research, this work (i) proposes a new modelling framework for identifying closed-loop systems, (ii) introduces a generic structure to represent the controller and (iii) explains how that the new framework gives rise to a simplified determination of the plant models. In contrast, the use of the conventional modelling approach renders the separation of the plant model a difficult task. The benefits of using the new model method are demonstrated using a number of application studies.

Dynamic Linear Precoding for the Exploitation of Known Interference in MIMO Broadcast Systems

This paper introduces a novel channel inversion (CI) precoding scheme for the downlink of phase shift keying (PSK)-based multiple input multiple output (MIMO) systems. In contrast to common practice where knowledge of the interference is used to eliminate it, the main idea proposed here is to use this knowledge to glean benefit from the interference. It will be shown that the system performance can be enhanced by exploiting some of the existent inter-channel interference (ICI). This is achieved by applying partial channel inversion such that the constructive part of ICI is preserved and exploited while the destructive part is eliminated by means of CI precoding. By doing so, the effective signal to interference-plus-noise ratio (SINR) delivered to the mobile unit (MU) receivers is enhanced without the need to invest additional transmitted signal power at the MIMO base station (BS). It is shown that the trade-off to this benefit is a minor increase in the complexity of the BS processing. The presented theoretical analysis and simulations demonstrate that due to the SINR enhancement, significant performance and throughput gains are offered by the proposed MIMO precoding technique compared to its conventional counterparts.

Dynamic global security-aware synthesis using System-C

A dynamic global security-aware synthesis flow using the SystemC language is presented. SystemC security models are first specified at the system or behavioural level using a library of SystemC behavioural descriptions which provide for the reuse and extension of security modules. At the core of the system is incorporated a global security-aware scheduling algorithm which allows for scheduling to a mixture of components of varying security level. The output from the scheduler is translated into annotated nets which are subsequently passed to allocation, optimisation and mapping tools for mapping into circuits. The synthesised circuits incorporate asynchronous secure power-balanced and fault-protected components. Results show that the approach offers robust implementations and efficient security/area trade-offs leading to significant improvements in turnover.

A Question of Legitimacy? A Dynamic Perspective on Multinational Firm Control

The control and coordination of a network of geographically and culturally dispersed subsidiaries is one of the most prominent challenges in international management. However, many empirical findings on the effectiveness of various control mechanisms and combinations thereof are still counterintuitive. This study uses longitudinal case studies and cross-sectional interview data to extend control theory by examining why, how, and in what sequence large multinational firms (MNCs) implement controls in their networks of foreign subsidiaries. Our analysis draws from literature on institutional theory, embeddedness, and organizational power to demonstrate that MNC headquarters need to overcome institutional duality when implementing their controls abroad. We find that headquarters do so by using social controls, primarily as a way of legitimizing and institutionalizing their process and output controls that are implemented subsequently.

Tree-Based Adaptive Spatial Detection for Adaptive Modulated MIMO Systems

Adaptive Multiple-Input Multiple-Output (MIMO) systems achieve a much higher information rate than conventional fixed schemes due to their ability to adapt their configurations according to the wireless communications environment. However, current adaptive MIMO detection schemes exhibit either low performance (and hence low spectral efficiency) or huge computational
complexity. In particular, whilst deterministic Sphere Decoder (SD) detection schemes are well established for static MIMO systems, exhibiting deterministic parallel structure, low computational complexity and quasi-ML detection performance, there are no corresponding adaptive schemes. This paper solves
this problem, describing a hybrid tree based adaptive modulation detection scheme. Fixed Complexity Sphere Decoding (FSD) and Real-Values FSD (RFSD) are modified and combined into a hybrid scheme exploited at low and medium SNR to provide the highest possible information rate with quasi-ML Bit Error
Rate (BER) performance, while Reduced Complexity RFSD, BChase and Decision Feedback (DFE) schemes are exploited in the high SNR regions. This algorithm provides the facility to balance the detection complexity with BER performance with compatible information rate in dynamic, adaptive MIMO communications
environments.

Implementation of phantom-less IMRT delivery verification using Varian DynaLog files and R/V output

This study aims to evaluate the use of Varian radiotherapy dynamic treatment log (DynaLog) files to verify IMRT plan delivery as part of a routine quality assurance procedure. Delivery accuracy in terms of machine performance was quantified by multileaf collimator (MLC) position errors and fluence delivery accuracy for patients receiving intensity modulated radiation therapy (IMRT) treatment. The relationship between machine performance and plan complexity, quantified by the modulation complexity score (MCS) was also investigated. Actual MLC positions and delivered fraction of monitor units (MU), recorded every 50 ms during IMRT delivery, were extracted from the DynaLog files. The planned MLC positions and fractional MU were taken from the record and verify system MLC control file. Planned and delivered beam data were compared to determine leaf position errors with and without the overshoot effect. Analysis was also performed on planned and actual fluence maps reconstructed from the MLC control file and delivered treatment log files respectively. This analysis was performed for all treatment fractions for 5 prostate, 5 prostate and pelvic node (PPN) and 5 head and neck (H&N) IMRT plans, totalling 82 IMRT fields in ∼5500 DynaLog files. The root mean square (RMS) leaf position errors without the overshoot effect were 0.09, 0.26, 0.19 mm for the prostate, PPN and H&N plans respectively, which increased to 0.30, 0.39 and 0.30 mm when the overshoot effect was considered. Average errors were not affected by the overshoot effect and were 0.05, 0.13 and 0.17 mm for prostate, PPN and H&N plans respectively. The percentage of pixels passing fluence map gamma analysis at 3%/3 mm was 99.94 ± 0.25%, which reduced to 91.62 ± 11.39% at 1%/1 mm criterion. Leaf position errors, but not gamma passing rate, were directly related to plan complexity as determined by the MCS. Site specific confidence intervals for average leaf position errors were set at -0.03-0.12 mm for prostate and -0.02-0.28 mm for more complex PPN and H&N plans. For all treatment sites confidence intervals for RMS errors with the overshoot was set at 0-0.50 mm and for the percentage of pixels passing a gamma analysis at 1%/1 mm a confidence interval of 68.83% was set also for all treatment sites. This work demonstrates the successful implementation of treatment log files to validate IMRT deliveries and how dynamic log files can diagnose delivery errors not possible with phantom based QC. Machine performance was found to be directly related to plan complexity but this is not the dominant determinant of delivery accuracy.

The VLT-FLAMES Tarantula Survey XI. A census of the hot luminous stars and their feedback in 30 Doradus

Context. The VLT-FLAMES Tarantula Survey has an extensive view of the copious number of massive stars in the 30 Doradus (30 Dor) star forming region of the Large Magellanic Cloud. These stars play a crucial role in our understanding of the stellar feedback in more distant, unresolved star forming regions. Aims. The first comprehensive census of hot luminous stars in 30 Dor is compiled within a 10 arcmin (150 pc) radius of its central cluster, R136. We investigate the stellar content and spectroscopic completeness of the early type stars. Estimates were made for both the integrated ionising luminosity and stellar wind luminosity. These values were used to re-assess the star formation rate (SFR) of the region and determine the ionising photon escape fraction. Methods. Stars were selected photometrically and combined with the latest spectral classifications. Spectral types were estimated for stars lacking spectroscopy and corrections were made for binary systems, where possible. Stellar calibrations were applied to obtain their physical parameters and wind properties. Their integrated properties were then compared to global observations from ultraviolet (UV) to far-infrared (FIR) imaging as well as the population synthesis code, Starburst99. Results. Our census identified 1145 candidate hot luminous stars within 150 pc of R136 of which >700 were considered to be genuine early type stars and contribute to feedback. We assess the survey to be spectroscopically complete to 85% in the outer regions (>5 pc) but only 35% complete in the region of the R136 cluster, giving a total of 500 hot luminous stars in the census which had spectroscopy. Only 31 were found to be Wolf-Rayet (W-R) or Of/WN stars, but their contribution to the integrated ionising luminosity and wind luminosity was ~ 40% and ~ 50%, respectively. Similarly, stars with M > 100 M (mostly H-rich WN stars) also showed high contributions to the global feedback, ~ 25% in both cases. Such massive stars are not accounted for by the current Starburst99 code, which was found to underestimate the integrated ionising luminosity of R136 by a factor ~ 2 and the wind luminosity by a factor ~ 9. The census inferred a SFR for 30 Dor of 0.073 ± 0.04 M yr . This was generally higher than that obtained from some popular SFR calibrations but still showed good consistency with the far-UV luminosity tracer as well as the combined Hα and mid-infrared tracer, but only after correcting for Hα extinction. The global ionising output was also found to exceed that measured from the associated gas and dust, suggesting that ~6 % of the ionising photons escape the region. Conclusions. When studying the most luminous star forming regions, it is essential to include their most massive stars if one is to determine a reliable energy budget. Photon leakage becomes more likely after including their large contributions to the ionising output. If 30 Dor is typical of other massive star forming regions, estimates of the SFR will be underpredicted if this escape fraction is not accounted for.

Multiuser Scheduling for Cognitive MIMO with Channel Estimation Errors and Feedback Delay

A multiuser scheduling multiple-input multiple-output (MIMO) cognitive radio network (CRN) with space-time block coding (STBC) is considered in this paper, where one secondary base station (BS) communicates with one secondary user (SU) selected from K candidates. The joint impact of imperfect channel state information (CSI) in BS → SUs and BS → PU due to channel estimation errors and feedback delay on the outage performance is firstly investigated. We obtain the exact outage probability expressions for the considered network under the peak interference power IP at PU and maximum transmit power Pm at BS which cover perfect/imperfect CSI scenarios in BS → SUs and BS → PU. In addition, asymptotic expressions of outage probability in high SNR region are also derived from which we obtain several important insights into the system design. For example, only with perfect CSIs in BS → SUs, i.e., without channel estimation errors and feedback delay, the multiuser diversity can be exploited. Finally, simulation results confirm the correctness of our analysis.

Blind Characterisation of Sensors with Second-Order Dynamic Response

Compensation for the dynamic response of a temperature sensor usually involves the estimation of its input on the basis of the measured output and model parameters. In the case of temperature measurement, the sensor dynamic response is strongly dependent on the measurement environment and fluid velocity. Estimation of time-varying sensor model parameters therefore requires continuous textit{in situ} identification. This can be achieved by employing two sensors with different dynamic properties, and exploiting structural redundancy to deduce the sensor models from the resulting data streams. Most existing approaches to this problem assume first-order sensor dynamics. In practice, however second-order models are more reflective of the dynamics of real temperature sensors, particularly when they are encased in a protective sheath. As such, this paper presents a novel difference equation approach to solving the blind identification problem for sensors with second-order models. The approach is based on estimating an auxiliary ARX model whose parameters are related to the desired sensor model parameters through a set of coupled non-linear algebraic equations. The ARX model can be estimated using conventional system identification techniques and the non-linear equations can be solved analytically to yield estimates of the sensor models. Simulation results are presented to demonstrate the efficiency of the proposed approach under various input and parameter conditions.

MIMO cooperative communication network design with relay selection and CSI feedback

In this paper, we investigate an amplify-and-forward (AF) multiple-input multiple-output - spatial division multiplexing (MIMO-SDM) cooperative wireless networks, where each network node is equipped with multiple antennas. In order to deal with the problems of signal combining at the destination and cooperative relay selection, we propose an improved minimum mean square error (MMSE) signal combining scheme for signal recovery at the destination. Additionally, we propose two distributed relay selection algorithms based on the minimum mean squared error (MSE) of the signal estimation for the cases where channel state information (CSI) from the source to the destination is available and unavailable at the candidate nodes. Simulation results demonstrate that the proposed combiner together with the proposed relay selection algorithms achieve higher diversity gain than previous approaches in both flat and frequency-selective fading channels.

Approximate Computing with Unreliable Dynamic Memories

Embedded memories account for a large fraction of the overall silicon area and power consumption in modern SoC(s). While embedded memories are typically realized with SRAM, alternative solutions, such as embedded dynamic memories (eDRAM), can provide higher density and/or reduced power consumption. One major challenge that impedes the widespread adoption of eDRAM is that they require frequent refreshes potentially reducing the availability of the memory in periods of high activity and also consuming significant amount of power due to such frequent refreshes. Reducing the refresh rate while on one hand can reduce the power overhead, if not performed in a timely manner, can cause some cells to lose their content potentially resulting in memory errors. In this paper, we consider extending the refresh period of gain-cell based dynamic memories beyond the worst-case point of failure, assuming that the resulting errors can be tolerated when the use-cases are in the domain of inherently error-resilient applications. For example, we observe that for various data mining applications, a large number of memory failures can be accepted with tolerable imprecision in output quality. In particular, our results indicate that by allowing as many as 177 errors in a 16 kB memory, the maximum loss in output quality is 11%. We use this failure limit to study the impact of relaxing reliability constraints on memory availability and retention power for different technologies.