An Automated System for Controlling the Laminar Flow Interface in a Microfluidic System

2004 ◽  
Author(s):  
Brandon Kuczenski ◽  
Philip R. LeDuc ◽  
William C. Messner
Keyword(s):  
Laminar Flow ◽  
Feedback System ◽  
Microfluidic System ◽  
Automated System ◽  
Time Varying ◽  
Relative Flow Rate ◽  
Microfluidic Network ◽  
Piezoresistive Pressure Sensor ◽  
Pressure Feedback ◽  
And Performance

The interface between adjacent laminar flow streams in the output channel of a Y-shaped confluent microfluidic network is useful for investigating the response of individual living cells to steep chemical gradients. This paper reports the design and performance of an automated pressure-feedback system for accurately and rapidly changing the position of that interface. The device will be employed to investigate the dynamic response of cells to time-varying chemical stimulation. The system works by controlling the pressure difference between the two adjoining inputs of the microfluidic network, altering the relative flow rate of the laminar streams in the output microchannel. Continuity of incompressible fluids dictates that the plane of the interface between the two streams will move from side to side as the flow rates change. The sample-data control system samples a temperature-compensated monolithic piezoresistive pressure sensor at 1 kilohertz, allowing the control of high-bandwidth microfluidic systems. This automated system enables long-duration, high-precision experiments that involve time-varying parameters to be performed simply, rapidly, and inexpensively.

scholarly journals Microfluidic Network Simulations Enable On-Demand Prediction of Control Parameters for Operating Lab-On-A-Chip-Devices

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1320
Author(s):  
Julia Sophie Böke ◽  
Daniel Kraus ◽  
Thomas Henkel
Keyword(s):  
Fluid Management ◽  
Lab On A Chip ◽  
Microfluidic System ◽  
Flow Rates ◽  
Demand Prediction ◽  
Fast Running ◽  
Microfluidic Network ◽  
Fluid Properties ◽  
Network Simulations ◽  
User Friendly

Reliable operation of lab-on-a-chip systems depends on user-friendly, precise, and predictable fluid management tailored to particular sub-tasks of the microfluidic process protocol and their required sample fluids. Pressure-driven flow control, where the sample fluids are delivered to the chip from pressurized feed vessels, simplifies the fluid management even for multiple fluids. The achieved flow rates depend on the pressure settings, fluid properties, and pressure-throughput characteristics of the complete microfluidic system composed of the chip and the interconnecting tubing. The prediction of the required pressure settings for achieving given flow rates simplifies the control tasks and enables opportunities for automation. In our work, we utilize a fast-running, Kirchhoff-based microfluidic network simulation that solves the complete microfluidic system for in-line prediction of the required pressure settings within less than 200 ms. The appropriateness of and benefits from this approach are demonstrated as exemplary for creating multi-component laminar co-flow and the creation of droplets with variable composition. Image-based methods were combined with chemometric approaches for the readout and correlation of the created multi-component flow patterns with the predictions obtained from the solver.

Disturbance observer–based neural flight control for aircraft with switched time-varying distributed delays

2021 ◽  
pp. 095441002199850
Author(s):  
Dawei Wu ◽  
Jun Zhou ◽  
Hui Ye
Keyword(s):  
Flight Control ◽  
Disturbance Observer ◽  
Distributed Delay ◽  
Unsteady Aerodynamics ◽  
Feedback System ◽  
Functional Method ◽  
System Control ◽  
Time Varying ◽  
Time Varying Delay ◽  
Feedback System Control

In this article, the high angle of attack (AOA) maneuver control problem is studied under multiple disturbances and uncertainties. For the first time, the switched distributed delay is constructed to characterize the unsteady aerodynamics. Based on neural networks (NNs) and hyperbolic tangent function, the disturbance observer technique is extended to the nonstrict-feedback system control. To handle the switching problem, time-delay problem, and nonstrict-feedback problem caused by switched distributed delay terms, the Lyapunov–Krasovskii (LK) functional method and a variable separation method are cleverly combined. The proposed LK function can relax the constraints on time-varying delay. Finally, a disturbance observer–based neural finite-time prescribed performance flight control law is developed to improve the flight performance at high AOA, and its effectiveness has been verified through rigorous theoretical analysis and simulation experiments.

scholarly journals HEDGE FUNDS: RISK AND PERFORMANCE

2018 ◽  
Vol 06 (01) ◽  
pp. 1850003
Author(s):  
SANGHEON SHIN ◽  
JAN SMOLARSKI ◽  
GÖKÇE SOYDEMIR
Keyword(s):  
Risk Factors ◽  
Hedge Funds ◽  
Factor Model ◽  
Explanatory Power ◽  
Hedge Fund ◽  
Investment Strategy ◽  
Time Varying ◽  
Asset Class ◽  
And Performance ◽  
Fund Portfolio

This paper models hedge fund exposure to risk factors and examines time-varying performance of hedge funds. From existing models such as asset-based style (ABS)-factor model, standard asset class (SAC)-factor model, and four-factor model, we extract the best six factors for each hedge fund portfolio by investment strategy. Then, we find combinations of risk factors that explain most of the variance in performance of each hedge fund portfolio based on investment strategy. The results show instability of coefficients in the performance attribution regression. Incorporating a time-varying factor exposure feature would be the best way to measure hedge fund performance. Furthermore, the optimal models with fewer factors exhibit greater explanatory power than existing models. Using rolling regressions, our customized investment strategy model shows how hedge funds are sensitive to risk factors according to market conditions.

scholarly journals Linkages and performance comparison among Eastern Europe stock markets

2014 ◽  
Author(s):  
José Soares Da Fonseca
Keyword(s):  
Stock Markets ◽  
Performance Comparison ◽  
Market Model ◽  
Var Model ◽  
Time Varying ◽  
Comparative Performance ◽  
Regional Market ◽  
Auto Regressive ◽  
And Performance ◽  
The Empirical Analysis

This article studies the linkages among the stock markets of Bulgaria, Czech Republic, Estonia, Hungary, Poland, Romania, Russia, Serbia, Slovenia and Ukraine. The empirical analysis begins with the estimation of a regional market model, whose beta parameters depend on predetermined information variables. Those parameters support the calculation of time‑varying Treynor ratios used on a comparative performance analysis. A Vector Auto Regressive Model (VAR) is used to estimate the performance causality within this group of markets. The VAR model results provide evidence that there is reciprocal performance across the majority of the selected stock markets.

Exploring IoT-Enabled Smart Transportation System

2019 ◽  
pp. 186-202 ◽  
Author(s):  
Sushruta Mishra ◽  
Shikha Patel ◽  
Amiya Ranjan Ranjan Panda ◽  
Brojo Kishore Mishra
Keyword(s):  
Weather Conditions ◽  
Transportation System ◽  
Automated System ◽  
Smart Transportation ◽  
Parking Spot ◽  
Parking System ◽  
Traffic Scenario ◽  
Smart Parking ◽  
And Performance ◽  
Real Time Information

Internet of Things (IoT) is a platform that makes a device smart such that every day communication becomes more informative. A Smart Transportation system basically consists of three components which include smart roads, smart vehicles and a smart parking system. Smart roads are used to describe roads that use sensors and IoT technology which makes driving safer and greener. Smart parking system involves an automated system model that can assist the drivers in selecting the suitable parking spot for them. The data that the system collects will be sent for some analysis. It provides real time information to drivers about various aspects of transportation like weather conditions, traffic scenario, road safety, parking space, and many other things. A well-built Smart Transportation system reduces the risk of accidents, improves safety, increases capacity, reduces fuel consumption, and enhances overall comfort and performance for drivers. Our chapter deals with the in-depth discussion of these various aspects of a smart transportation system enabled with IoT technology.

Traffic and Network Performance Monitoring for Effective Quality of Service and Network Management

2010 ◽  
pp. 201-227
Author(s):  
P. Papantoni-Kazakos ◽  
A. T. Burrell
Keyword(s):  
Mobile Networks ◽  
Network Performance ◽  
Performance Monitoring ◽  
Sequential Algorithm ◽  
Heterogeneous Environments ◽  
Time Varying ◽  
Distributed Environment ◽  
Data Flows ◽  
Network Performance Monitoring ◽  
And Performance

The authors consider distributed mobile networks carrying time-varying heterogeneous traffics. To deal effectively with the mobile and time-varying distributed environment, the deployment of traffic and network performance monitoring techniques is necessary for the identification of traffic changes, network failures, and also for the facilitation of protocol adaptations and topological modifications. Concurrently, the heterogeneous traffic environment necessitates the deployment of hybrid information transport techniques. This chapter discusses the design, analysis, and evaluation of distributed and dynamic techniques which manage the traffic and monitor the network performance effectively, while capturing the dynamics inherent in the mobile heterogeneous environments. Specifically, the design of a monitoring sub-network is sought, where the arising research tasks include: • the adoption of a core sequential algorithm which monitors both the variations in the rates of the information data flows and the dynamics of the network performance. • The identification of the specific operational and performance characteristics of the monitoring systems, when the core algorithm is implemented in a distributed environment; for a given network topology, it is important to identify the minimum size monitoring sub-network for complete “visibility” of data flows and network functions. • Dynamically changing monitoring sub-network architectures, as functions of time-varying network topologies. • The deployment of Artificial Intelligence learning techniques, in the presence of dynamically changing network and information flow environments, to appropriately adapt crucial operational parameters of the data monitoring algorithms.

Feedback-System-Control Integrated Microfluidic System for Fast Screening of Protein Crystallization Conditions

2020 ◽  
Vol 20 (7) ◽  
pp. 4325-4334
Author(s):  
Sankhya Bhattacharya ◽  
Pijus Kundu ◽  
J.S. Liu ◽  
Wen-Ching Wang ◽  
Fan-Gang Tseng
Keyword(s):  
Protein Crystallization ◽  
Feedback System ◽  
Microfluidic System ◽  
System Control ◽  
Feedback System Control ◽  
Fast Screening ◽  
Crystallization Conditions

A Combined Method for Time-Varying Parameter Identification Based on Variational Mode Decomposition and Generalized Morse Wavelet

2020 ◽  
Vol 20 (07) ◽  
pp. 2050077
Author(s):  
Chao Wang ◽  
Jing Zhang ◽  
Hong Pin Zhu
Keyword(s):  
Parameter Identification ◽  
Structural Response ◽  
Time Varying ◽  
Variational Mode Decomposition ◽  
Combined Method ◽  
Moving Vehicle ◽  
Mode Decomposition ◽  
Time Varying Parameter ◽  
Time Varying Systems ◽  
And Performance

Time-varying parameter identification is essential for structural health monitoring and performance evaluation. In this paper, a combined method based on the variational mode decomposition and generalized Morse wavelet is proposed to identify the structural time-varying parameters. Based on the sparse property of structural response signals in wavelet domain, a fast iterative shrinkage-thresholding algorithm is adopted to reduce the noise. Then the de-noised signal is decomposed into multi- modes by the variational mode decomposition, and the generalized Morse wavelet is performed to identify the instantaneous frequency. To validate the proposed method, a numerical example including different frequency variations is studied. Experimental validations of a moving vehicle across a bridge and a time-varying cable system considering two patterns of cable tension variations in the laboratory are carried out to investigate the capability of the proposed approach. It is confirmed that the proposed approach can effectively perform the signal decomposition, while identifying the instantaneous frequencies of the time-varying systems accurately.

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