Primary Control Variables Research Articles

Ethnicity/culture modulates the relationships of the haptoglobin (Hp) 1-1 phenotype with cognitive function in older individuals with type 2 diabetes.

The haptoglobin (Hp) genotype has been associated with cognitive function in type 2 diabetes. Because ethnicity/culture has been associated with both cognitive function and Hp genotype frequencies, we examined whether it modulates the association of Hp with cognitive function. This cross-sectional study evaluated 787 cognitively normal older individuals (>65 years of age) with type 2 diabetes participating in the Israel Diabetes and Cognitive Decline study. Interactions in two-way analyses of covariance compared Group (Non-Ashkenazi versus Ashkenazi Jews) on the associations of Hp phenotype (Hp 1-1 versus non- Hp 1-1) with five cognitive outcome measures. The primary control variables were age, gender, and education. Compared with Ashkenazi Jews, non-Ashkenazi Jews with the Hp 1-1 phenotype had significantly poorer cognitive function than non-Hp 1-1 in the domains of Attention/Working Memory (p = 0.035) and Executive Function (p = 0.023), but not in Language/Semantic Categorization (p = 0.432), Episodic Memory (p = 0.268), or Overall Cognition (p = 0.082). After controlling for additional covariates (type 2 diabetes-related characteristics, cardiovascular risk factors, Mini-mental State Examination, and extent of depressive symptoms), Attention/Working Memory (p = 0.038) and Executive Function (p = 0.013) remained significant. Older individuals from specific ethnic/cultural backgrounds with the Hp 1-1 phenotype may benefit more from treatment targeted at decreasing or halting the detrimental effects of Hp 1-1 on the brain. Future studies should examine differential associations of Hp 1-1 and cognitive impairment, especially for groups with high prevalence of both, such as African-Americans and Hispanics.

A Novel Universal Control Scheme for Transcutaneous Energy Transfer (TET) Applications

A key feature of future active implants will include wireless transcutaneous energy transfer (TET). A novel closed-loop control scheme is presented here for coupled resonant tank circuits that is capable of providing stable and maximally efficient power transfer under various load and disturbance conditions as well as for varying implants. The control algorithm consists of four cascaded control loops using capacitance, frequency, and voltage as the primary control variables. The innermost, D, control loop uses the primary side voltage to control the secondary side current. The C-loop uses the excitation frequency to set the optimal working point for the TET system and the secondary side tank behavior. The secondary side tank may either act as voltage or current source dependent upon the frequency setting of the primary side. The TETs transfer function is shaped by setting the primary capacitance in the B-control loop, determining the performance and transmission efficiency. The exact positioning of primary and secondary coils is determined by an online measurement of the mutual inductance M. In this paper, the D-control loop has been both simulated and realized, for the A-C loops a proof of concept is shown.

Plant-wide control system design: Secondary controlled variable selection

This work is focused on the development of a rigorous, model-based approach for the selection of secondary controlled variables as part of a plant-wide control system design methodology. Secondary controlled variables should be easy to measure, easy to control, fast to respond to changes in the input variables, and lead to automatic, indirect control of the primary controlled variables. While much of the work on this subject has been based upon ad hoc approaches, here a systematic three-stage approach is proposed that addresses issues of controllability and economic performance of the control system. The first stage involves the generation of an initial set of candidate secondary controlled variables and the generation of selection constraints that are used to determine if manipulated variables can be used for control of candidate controlled variables. During the second stage, secondary controlled variables are selected to minimize integral absolute errors (IAEs) of the primary controlled variables subject to minimal loop interactions as determined by a relative gain array analysis. Finally, during the third stage, control performance of the secondary controlled variables is evaluated at off-design operations using a nonlinear process model. The proposed approach is then applied, as ongoing work in the application of plant-wide control, to an acid gas removal unit as part of an integrated gasification combined cycle power plant with CO2 capture.

Plant-wide control system design: Primary controlled variable selection

This work is focused on the development of a rigorous, model-based approach for the selection of primary controlled variables as part of a plant-wide control system design methodology. Controlled variables should be selected for their self-optimizing control performance and controllability while ensuring satisfactory performance in terms of dead-time and closed loop interactions. This work has considered both self-optimizing and control performance as well as has addressed issues related to loop-interactions and superstructure constraints. The new three-stage approach developed in this work results in a large-scale, constrained, mixed-integer multi-objective optimization problem. For solving this problem, a parallelized, bi-directional branch and bound algorithm with dynamic search strategies has been developed to solve the problem on large computer clusters. The proposed approach is then applied to an acid gas removal unit as part of an integrated gasification combined cycle power plant with CO2 capture.

Evaluating the Impact of Cybersecurity Information Sharing on Cyber Incidents and Their Consequences

The Department of Homeland Security (DHS) facilitates cybersecurity information sharing among federal government departments and agencies and critical infrastructure owners and operators to promote their security. Information sharing is deemed of critical importance to accomplish the department’s cybersecurity mission; indeed, information sharing is one of the central planks of Executive Order 13636: Improving Critical Infrastructure Cybersecurity, which calls for greater cybersecurity information sharing between the government — not least DHS — and the private sector. But while the importance of information sharing in cybersecurity is intuitive — information that is relevant, timely, and accurate should help cyber defenders reduce vulnerabilities and mitigate threats — the impact of information sharing has not been empirically assessed. The lack of empirical support for information sharing raises two notable issues. First, information-sharing partners, particularly those in the private sector, are sometimes reluctant to participate in government-sponsored initiatives because of concerns about liability, resource costs, and return on investment. Absent empirical demonstration of the value of cybersecurity information-sharing efforts, DHS may be unable to better incentivize participation. Second, information-sharing efforts may, for a variety of reasons, be ineffective (not least due to a lack of participation or the dissemination of irrelevant information). Without assessing the relationship between information sharing and the number and severity (i.e., consequences) of cyber incidents, DHS may be unable to identify and improve poorly performing information sharing efforts. A previous Homeland Security Studies and Analysis Institute (HSSAI) study recommended a suite of metrics to measure various relevant inputs, processes, outputs, and outcomes for cyber information-sharing efforts (Fleming and Goldstein 2012). It did not, however, seek to suggest ways to empirically test the hypothesis that information sharing reduces the number or severity of cyber incidents (it was assumed to do so, per DHS guidance). Accordingly, building on the previous HSSAI research, the present paper sets forth views on use of the dependent variable (some measure of cyber incidents), primary independent variable (some measure of information sharing), control variables, and model specifications.

Impact of Homophily on Diffusion Dynamics Over Social Networks

The purpose of this study is to analyze the impact of homophily on diffusion over social networks. An agent-based simulation model is developed to serve as the experimental ground for this analysis. Diffusion dynamics of a nonsticky innovation is investigated by varying homophily levels in the social network depicted in the model as the primary control variable. First of all, the results show that homophily is self-reinforcing. Second, starting from a nonhomophilous network, early increases in the level of homophily have a positive effect on the extent of diffusion, whereas further increases have a negative impact. Finally, several local minima and maxima are observed in the relation between the homophily level and the extent of diffusion. Our analysis focuses on node properties such as connectedness and average degrees in order to explain the observed regular relationship between homophily and diffusion. We argue that (i) homophily increases the connectedness of different status groups separately and (ii) increasing levels of homophily decreases the marginal importance of a single homophilous tie by increasing the sources of valuable information. Future research involves investigating the coevolution of social behavior and networks by allowing the adopted innovation to lead to value homophily, exploration of different diffusion initiation types, and different adoption heuristics.

Improvement of Fuel Efficiency and Drivability Using Simple Prediction for Gear Changing

Decreasing fuel consumption and emissions in automobiles has been an active research topic in recent years. A promising technology is the hybridization of powertrain. The main focus in this area is usually on the development of optimal power management control methods. For parallel HEVs (hybrid electric vehicle), the primary control variable is the torque split between the internal combustion engine and the electric motor but gear number can also be considered as a control parameter. ECMS (equivalent consumption minimization strategy) is one of the well-known real time power management strategies and has been used extensively in different works; however, using ECMS for controlling gearbox cannot always lead to optimal fuel consumption and drivability. The slow dynamics of gearbox might introduce unnecessary gear changing, which leads to suboptimal fuel efficiency and degraded drivability. In this paper, a simple prediction strategy is implemented to improve fuel efficiency and drivability. The presented prediction method does not use any information from the environment and does not need any extra sensor. The strategy is not computationally heavy compared to other predictive methods. The simplicity of the method makes it suitable for implementations.

A comparative study of three advanced controllers for the regulation of hypnosis

The anesthetic state is a dynamic combination of hypnosis, analgesia and neuromuscular blockade that is maintained by infusing a cocktail of drugs. This work focuses on controlling the hypnosis during a surgical procedure by automatic regulation of isoflurane and employs Bispectral Index (BIS) as the primary controlled variable. A seventh-order nonlinear pharmacokinetic–pharmacodynamic representation has been used for the hypnosis dynamics of patients. This study uses a model predictive control structure for the regulation of BIS. Performance of this controller has been tested for a range of patients and compared with two previously employed control strategies (cascade internal model controller and cascade controller with modeling error compensation). Performance of the three controllers has also been studied for a step change in BIS, measured disturbances and noise in the measured variables. Numerical simulations show that the model predictive controller performed better than the other two controllers.

Advanced Control Strategies for the Regulation of Hypnosis with Propofol

Anesthesia implies maintaining a triad of hypnosis, analgesia, and neuromuscular blockade by infusing several drugs which specifically act on each of the above aspects. This work focuses on controlling the hypnosis by automatic infusion of propofol using bispectral index (BIS) as the primary controlled variable. A fourth-order nonlinear pharmacokinetic (PK)−pharmacodynamic (PD) representation is used for the hypnosis dynamics of patients. A reliable PK−PD model with associated parameters is obtained from the literature and the closed-loop responses of four types of control strategies (model predictive control, internal model control, controller with modeling error compensation, and proportional-integral-derivative (PID) control) are compared. Robust performance of the four controllers is tested for a broad range of patients by considering variability in PK−PD parameters. Also, the relative performance of the four controllers is studied for different set-points, noise, and disturbances in BIS signal. Numer...

Servo-Controlled Hind-Limb Electrical Stimulation for Short-Term Arterial Pressure Control

Autonomic neural intervention is a promising tool for modulating the circulatory system thereby treating some cardiovascular diseases. In 8 pentobarbital-anesthetized cats, it was examined whether the arterial pressure (AP) could be controlled by acupuncture-like hind-limb electrical stimulation (HES). With a 0.5-ms pulse width, HES monotonically reduced AP as the stimulus current increased from 1 to 5 mA, suggesting that the stimulus current could be a primary control variable. In contrast, the depressor effect of HES showed a nadir approximately 10 Hz in the frequency range between 1 and 100 Hz. Dynamic characteristics of the AP response to HES approximated a second-order low-pass filter with dead time (gain: -10.2 +/- 1.6 mmHg/mA, natural frequency: 0.040 +/- 0.004 Hz, damping ratio 1.80 +/- 0.24, dead time: 1.38 +/- 0.13 s, mean +/- SE). Based on these dynamic characteristics, a servo-controlled HES system was developed. When a target AP value was set at 20 mmHg below the baseline AP, the time required for the AP response to reach 90% of the target level was 38 +/- 10 s. The steady-state error between the measured and target AP values was 1.3 +/- 0.1 mmHg. Autonomic neural intervention by acupuncture-like HES might provide an additional modality to quantitatively control the circulatory system.

Advanced Regulatory Controller for Automatic Control of Anesthesia

Anesthesia process is to maintain a triad of hypnosis, analgesia and neuromuscular blockade by infusing several drugs which are specific for each state. This work focuses on controlling the hypnosis with RTDA (Robustness, Set-point tracking, Disturbance rejection, Aggressiveness) controller by regulation of propofol using Bispectral Index (BIS) as primary controlled variable. One of the main advantages of RTDA controller is its intuitive tuning parameters when compared to PID and MPC controllers. For the controller design, a fourth-order nonlinear pharmacokinetic - pharmacodynamic representation is used for the hypnosis dynamics of patients. Nominal values for pharmacokinetics and pharmacodynamics were taken from the literature. Then the performance of the RTDA controller is compared with the performances of the PID and MPC controllers. Robust performance of these controllers is tested for a selected range of patients by considering variability in parameters of the patient model. Also studied are the relative performances with respect to different set-points in BIS, and disturbances in BIS signal. Numerical simulations show that the RTDA controller provides better performance compared to the other two controllers.

Perfect Steady-State Indirect Control

Indirect control is commonly used in industrial applications where the primary controlled variable is not measured. This paper considers the case of “perfect indirect control” where one attempts to control a combination of the available measurements such that there is no effect of disturbances on the primary outputs at steady-state. This is always possible provided the number of measurements is equal to the number of independent variables (inputs plus disturbances). It is further shown how extra measurements may be used to minimize the effect of measurement error. The results in this paper also provide a nice link to previous results on inferential control, perfect disturbance rejection and decoupling (DRD), and self-optimizing control.

A Microwave Satellite Observational Operator for Variational Data Assimilation of Soil Moisture

An observational operator and its adjoint have been created that are suitable for use within variational data assimilation using polarized 6- and 10-GHz passive microwave satellite observations. When used within a variational data assimilation system, the operator will facilitate NWP soil moisture initialization using existing and future satellite datasets [e.g., NASA Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR-E), Advanced Earth Observing Satellite-II (ADEOS-II), Department of Defense (DoD) WindSat, and the National Polar Orbiter Environmental Satellite System (NPOESS) Conical Microwave Imager Sounder (CMIS)]. Five primary control variables are used within the operator, and surface soil moisture is explicitly included as one of the control variables. In future studies, this operator and its adjoint will be used to perform land surface model data assimilation experiments to better determine important NWP surface characteristics. In the current study, the adjoint model development and analysis of the potential information content of passive microwave measurements sensitive to the land surface and soil parameters are focused on. The multivariate results clarify the effects of masking phenomena upon the soil moisture signal. Also included is a useful transformation of the adjoint between real and complex number spaces. The transformations are necessary when higher-level mathematical operators, such as powers, use complex number arguments. This can be a common occurrence within radiative transfer models. Thus, the adjoint of this particular observational operator serves as an example of this behavior. Various observational operator results and sensitivity analyses are presented, demonstrating significant utility of the operator for NWP soil moisture data assimilation studies.

Development and characterization of poly(trimethylene terephthalate)‐based bicomponent meltblown nonwovens

AbstractPoly(trimethylene terephthalate) (PTT)‐based mono and bico meltblown webs have been produced by using a Reicofil® Bi‐Component Meltblown Line at TANDEC, located at the University of Tennessee, Knoxville, TN. Thermal and flow properties of PTT were first examined by DSC (differential scanning calorimetry) and with a Melt Indexer for an effective experimental design through the Surface Response Methodology (SRM). The processability of meltblowing in a wide range of operating windows was extensively investigated. Melt temperature, melt throughput, air temperature, airflow rate, and DCD (distance of collector to die) were considered as primary process control variables. The produced webs were characterized for fiber diameter, bulk density, air permeability, hydrostatic head, tensile properties, and heat shrinkage. Non‐round and curly or twisted fibers were observed in the bico PP/PTT webs by SEM (scanning electrical microscope). The PTT grade studied is quite suitable for the meltblown process. The PTT/PP‐based bico webs showed enhanced barrier properties and heat resistance. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 83: 1280–1287, 2002

A locational gaming model with CO 2 emission tax and limits

This paper presents a locational (spatial) gaming model with CO 2 emission and transmission capacity limits. It is developed for simulating strategic behavior of electricity producers in deregulated electricity markets. The model has multiple players, each maximizing their individual profit with a CO 2 emission tax included to reflect the societal cost of environment damages caused by CO 2 emission from different locations. In the paper, the multiple-producer profits are converted into a set of Lagrangian functions with power production and supply as the primary control variables, resulting in a set of unconstrained, individual profit maximization equations. The Karush–Kuhn–Tucker necessary conditions are then derived and solved simultaneously incorporating Cournot gaming strategy. Case studies show the successful application of the model.

Effect of Bubble Velocity and pH Step Changes on the Foam Fractionation of Sporamin

Foam fractionation can be employed to concentrate and separate sporamin (ipomoein) from a sweet potato protein−water extract. In this foam fractionation study, the bulk solution pH and the air superficial velocity, V0, were the two primary control variables. It was determined that the protein separation ratio is strongly dependent on the bulk solution pH, with the highest sporamin recovery occurring at pH 3. Two types of experiments were conducted here. The first was to hold the air superficial velocity constant and step down the pH in small increments, with 10 mL of foamate collected at each pH level. The second was to hold the pH constant and step up the air superficial velocity, V0, in small increments, again with 10 mL of foamate collected at each V0 level. The lowest feasible air superficial velocity generally corresponds to the maximum protein recovery at a given pH. One strategy for separating β-amylase from sporamin in the sweet potato extract is to first foam the bulk solution at pH 5 to concentrate the sporamin, followed by foaming at pH 3 to collect the concentrated β-amylase. Keywords: Foam fractionation; protein separation; sweet potato protein; sporamin; ipomoein

ABB: Direct torque control

Examines the development of a powerful AC variable speed drive technology that is reported to offer the best performance yet from an AC drive, allowing it to match and even better the performance of DC motor drives. The technology is called direct torque control [DTC] and uses motor flux and torque as primary control variables. Outlines the background of DTC research and highlights two key developments, the latest high speed, signal‐processing technology and a highly advanced Motor Model. Describes how the motor model is fed information about the motor, which is collected during a test run, and how the information is applied.

Optimal power flow using a generalized power balance constraint

The paper presents a new method for optimal power flow calculations, using the generalized power balance constraint, obtained from the power flow equations by elimination of all state variables. The reduced optimization problem in the control variables is subsequently decoupled into two stages: a primary stage in the primary control variables (generator active power injections) and a secondary stage mainly in the secondary control variables (generator voltages). The primary stage is solved by economic generation scheduling, where the power system losses are accounted for by the coefficients of the generalized power balance constraint. The secondary stage is solved by linear programming, subject to all power system constraints. The linear programming formulation is such that the solution of the primary stage is maintained, if feasible, or otherwise optimally adjusted. Thus, the method takes full advantage of decoupling, if it is applicable, and otherwise inherently reverts to a coupled approach. A step-limit control strategy successfully damps the oscillations that are always present in successive linear programming, and ensures the optimality and feasibility of the solution.

Milling parameter optimization through a discrete variable transformation

Abstract Although milling is one of the most common chip metal removal processes, little has been done to aid in identifying the optimum operational conditions for milling processes. In this paper, a mathematical model for milling operations is developed and the five primary control variables identified. The model is then decomposed and an efficient optimization procedure developed for control variable identification.

High-speed Computation of Ascent Trajectories within Iterative Loops Using Collocation Methods

has found applications in spaceflight trajectory optimization.2 It is shown in this paper how the method can be adapted to the computation of trajectories within a combined design/performance optimization program for multistage launch vehicles. The parameters of the collocation expansion are adjusted in a single loop, together with the primary control variables of the problem, to satisfy the conditions of a constrained optimum. This results in an order of magnitude reduction in computer time compared with a more traditional approach. Good accuracy is obtained by segmenting the trajectory.