Einfluß unterschiedlicher Störsignale auf Mensch-maschine-Regelkreise

Dynamic behaviour of man has been investigated in a simple closed loop with a human controller. Either stochastic or deterministic disturbances act upon the output of the controlled element. A choice of the nearly 200 tests will be discussed concerning the learning ability, the variability of the results and the stationarity of the human controller. It will be shown that the quasi-linear frequency response of the open loop is independent of the type of disturbances provided the controlled elements do not contain dead-time. Conversely, dead-time within the controlled element affects the adaptibility of the human controller. Hence, in this case, the quasi-linear frequency response depends also on the type of the disturbance signal.     

The photon counting histogram in fluorescence fluctuation spectroscopy with non-ideal photodetectors

Fluorescence fluctuation spectroscopy utilizes the signal fluctuations of single molecules for studying biological processes. Information about the biological system is extracted from the raw data by statistical methods such as used in fluctuation correlation spectroscopy or photon counting histogram (PCH) analysis. Since detectors are never ideal, it is crucial to understand the influence of photodetectors on signal statistics to correctly interpret the experimental data. Here we focus on the effects of afterpulsing and detector dead-time on PCH statistics. We determine the dead-time and afterpulse probability for our detectors experimentally and show that afterpulsing can be neglected for most experiments. Dead-time effects on the PCH are concentration-dependent and become significant when more than one molecule is present in the excitation volume. We develop a new PCH theory that includes dead-time effects and verify it experimentally. Additionally, we derive a simple analytical expression that accurately predicts the effect of dead-time on the molecular brightness. Corrections for non-ideal detector effects extend the useful concentration range of PCH experiments and are crucial for the interpretation of titration and dilution experiments.     

LFT models of continuous biotechnological processes

The aim of the paper is to obtain suitable state-space models of continuous biotechnological processes (CBTP) in the framework of Linear Fractional Transformation (LFT). The LFT models are starting point in most of the advanced robust control design and analysis methods. Therefore, a linearized process model in the state-space is used whose elements are supposed to vary within certain bounds to represent the nonlinear behaviour of the real plant. The performance specifications are defined in the frequency domain through weighting functions. Two LFT models of CBTP are obtained ready for controller design aimed to optimize robust stability margins and robust performance, respectively.     

Dynamic fuzzy model based predictive controller for a biochemical reactor

The kinetics of bioreactions often involve some uncertainties and the dynamics of the process vary during the course of fermentation. For such processes, conventional control schemes may not provide satisfactory control performance and demands extra effort to design advanced control schemes. In this study, a dynamic fuzzy model based predictive controller (DFMBPC) is presented for the control of a biochemical reactor. The DFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. The DFMBPC has the flexibility to opt with various types of fuzzy models whose choice also lead to improve the control performance. The performance of DFMBPC is evaluated by comparing with a fuzzy model based predictive controller (FMBPC) with no model adaptation and a conventional PI controller. The results show that DFMBPC provides better performance for tracking setpoint changes and rejecting unmeasured disturbances in the biochemical reactor.     

Risk-Sensitive Optimal Feedback Control Accounts for Sensorimotor Behavior under Uncertainty

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.     

Stimulus design for model selection and validation in cell signaling

Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus–response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody–ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models.     

Intermittent control: a computational theory of human control

The paradigm of continuous control using internal models has advanced understanding of human motor control. However, this paradigm ignores some aspects of human control, including intermittent feedback, serial ballistic control, triggered responses and refractory periods. It is shown that event-driven intermittent control provides a framework to explain the behaviour of the human operator under a wider range of conditions than continuous control. Continuous control is included as a special case, but sampling, system matched hold, an intermittent predictor and an event trigger allow serial open-loop trajectories using intermittent feedback. The implementation here may be described as ??continuous observation, intermittent action??. Beyond explaining unimodal regulation distributions in common with continuous control, these features naturally explain refractoriness and bimodal stabilisation distributions observed in double stimulus tracking experiments and quiet standing, respectively. Moreover, given that human control systems contain significant time delays, a biological-cybernetic rationale favours intermittent over continuous control: intermittent predictive control is computationally less demanding than continuous predictive control. A standard continuous-time predictive control model of the human operator is used as the underlying design method for an event-driven intermittent controller. It is shown that when event thresholds are small and sampling is regular, the intermittent controller can masquerade as the underlying continuous-time controller and thus, under these conditions, the continuous-time and intermittent controller cannot be distinguished. This explains why the intermittent control hypothesis is consistent with the continuous control hypothesis for certain experimental conditions.     

Automatic control of biomass gasifiers using fuzzy inference systems

A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated.     

Frequency-domain identification of the human controller

System identification techniques applied to experimental human-in-the-loop data provide an objective test of three alternative control-theoretical models of the human control system: non-predictive control, predictive control, and intermittent predictive control. A two-stage approach to the identification of a single-input single-output control system is used: first, the closed-loop frequency response is derived using the periodic property of the experimental data, followed by the fitting of a parametric model. While this approach is well-established for non-predictive and predictive control, it is here used for the first time with intermittent predictive control. This technique is applied to data from experiments with human volunteers who use one of two control strategies, focusing either on position or on velocity, to manually control a virtual, unstable load which requires sustained feedback to maintain position or low velocity. The results show firstly that the non-predictive controller does not fit the data as well as the other two models, and secondly that the predictive and intermittent predictive controllers provide equally good models which cannot be distinguished using this approach. Importantly, the second observation implies that sustained visual manual control is compatible with intermittent control, and that previous results suggesting a continuous control model for the human control system do not rule out intermittent control as an alternative hypothesis. Thirdly, the parameters identified reflect the control strategy adopted by the human controller.     

Generation of a Three-dimensional Full Thickness Skin Equivalent and Automated Wounding

In vitro models are a cost effective and ethical alternative to study cutaneous wound healing processes. Moreover, by using human cells, these models reflect the human wound situation better than animal models. Although two-dimensional models are widely used to investigate processes such as cellular migration and proliferation, models that are more complex are required to gain a deeper knowledge about wound healing. Besides a suitable model system, the generation of precise and reproducible wounds is crucial to ensure comparable results between different test runs. In this study, the generation of a three-dimensional full thickness skin equivalent to study wound healing is shown. The dermal part of the models is comprised of human dermal fibroblast embedded in a rat-tail collagen type I hydrogel. Following the inoculation with human epidermal keratinocytes and consequent culture at the air-liquid interface, a multilayered epidermis is formed on top of the models. To study the wound healing process, we additionally developed an automated wounding device, which generates standardized wounds in a sterile atmosphere.     

Machine learning-based model predictive controller design for cell culture processes

The biopharmaceutical industry continuously seeks to optimize the critical quality attributes to maintain the reliability and cost-effectiveness of its products. Such optimization demands a scalable and optimal control strategy to meet the process constraints and objectives. This work uses a model predictive controller (MPC) to compute an optimal feeding strategy leading to maximized cell growth and metabolite production in fed-batch cell culture processes. The lack of high-fidelity physics-based models and the high complexity of cell culture processes motivated us to use machine learning algorithms in the forecast model to aid our development. We took advantage of linear regression, the Gaussian process and neural network models in the MPC design to maximize the daily protein production for each batch. The control scheme of the cell culture process solves an optimization problem while maintaining all metabolites and cell culture process variables within the specification. The linear and nonlinear models are developed based on real cell culture process data, and the performance of the designed controllers is evaluated by running several real-time experiments.     

Human internal mammary artery (IMA) transplantation and stenting: a human model to study the development of in-stent restenosis

Preclinical in vivo research models to investigate pathobiological and pathophysiological processes in the development of intimal hyperplasia after vessel stenting are crucial for translational approaches (1,2). The commonly used animal models include mice, rats, rabbits, and pigs (3-5). However, the translation of these models into clinical settings remains difficult, since those biological processes are already studied in animal vessels but never performed before in human research models (6,7). In this video we demonstrate a new humanized model to overcome this translational gap. The shown procedure is reproducible, easy, and fast to perform and is suitable to study the development of intimal hyperplasia and the applicability of diverse stents. This video shows how to perform the stent technique in human vessels followed by transplantation into immunodeficient rats, and identifies the origin of proliferating cells as human.     

A model-based monitor of human sleep stages

Stochastic models are proposed for sleep and for the sleep related electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). The evolution of sleep through its various stages is described as a Markov chain. The EEG is modelled using Wiener processes. The EOG and EMG are modelled as combinations of Poisson point processes and Gaussian processes, respectively. The EEG models contain a feedback structure that is based on physiological data. The maximum likelihood sleep stage monitor, that uses the sleep-related observations, has been derived and implemented. The agreement between automatic and human stage classifications of six sleep recordings was 70.6%, which was 4.5% worse than the average agreement between six human classifiers. Monitoring of simulated sleep suggests that the difficulty in separating wakefulness from stage 1 is due to poor modelling. If one ignores this difference, which, from a diagnostic point of view is fairly unimportant, the above mentioned agreement reaches 81.8%, which is 0.5% better than the corresponding average human vs human agreement.     

Multiple Stable States of Disease Occurrence: A Note on the Implications for the Anthropological Study of Human Disease

Ecologists model human disease using predator-prey models, with humans as the prey and the pathogen as the predator. Use of these models permits the identification of stable equilibria and stable states of disease prevalence for human host-parasite systems. May (1977) has described the possibility of the presence of multiple stable states of disease prevalence for human disease systems. The model of multiple stable states implies that historical circumstances are important in determining the occurrence of disease. This model offers a theoretical framework in which to examine the occurrence of epidemics in human populations and to develop a more complete understanding of disease processes.     

Stabilization of human standing posture using functional neuromuscular stimulation

Functional neuromuscular stimulation (FNS)/functional electrical stimulation (FES) is a potential way to restore some functionality to the limbs of patients with spinal cord injury through direct/indirect stimulation of the motoneuron. One of the constraints for wider use of FNS on paraplegic patients is the lack of efficient control algorithm. Most of the published works on FNS/FES control are based on oversimplified models of human body dynamics. An innovative control strategy for stabilizing the standing posture of paraplegic patients is proposed here which is a combination of a proportional-plus-derivative controller for motions of the skeletal system and a control action prediction mechanism to produce musculotendon activation. The goal is to produce musculotendon torque which can approximate those demanded by the controller for the skeletal system. In computer simulations, using a detailed skeletal–musculotendon–muscle activation dynamics model of human body, this FNS/FES control approach can stabilize a paraplegic patient's standing posture with the minimum number of musculotendon groups. Also, it is found that this control strategy can maintain stability even in the presence of reasonable variations in the controller's musculotendon parameters.     

Perspectives in stochastic models of human reproduction: A review and analysis

The purpose of this paper is to review, to analyze, and to take steps toward synthesizing, two research areas in stochastic models of population dynamics. The first of these areas consists of stochastic models of human reproduction associated with the late Mindel C. Sheps and the second area is a class of stochastic models of population growth called generalized age-dependent branching processes, a class of models that shows promise of throwing more light on the classical mathematical demography of Lotka. The substantive material of the paper is arranged in eight sections ranging in content from a comparison of classical mathematical demography with generalized age-dependent branching processes, to suggestions for restructuring models of the Sheps school in quest of greater realism. The paper ends with a section on numerical examples illustrating applications in family planning evaluation and an appendix suggesting ways in which algebraic concepts are useful in short cutting computations.     

Integral control in accommodation.

Models of the accommodation system available inthe literature may be classified as proportional controller, optimal bang-bang controller and pure integral controller types. Each of these models explains only some aspects of the experimental behavior of the system and conflicts with other experimental observations. Based on two different experiments in which the built-in phyiological feedback loop has been opened, a leaky-integrator element is proposed for the accommodation controller. This element appears to resolve the conflict between the proportional and integral controller models and also explains the experimental observations better than any of the existing models.