Design Of Adaptive System Research Articles

Multivariable Adaptive Artificial Pancreas System in Type 1 Diabetes.

The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems. A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep. The development of a fully automated AP will necessitate the design of multivariable and adaptive systems that use information from wearable devices in addition to glucose sensors and modify the models used in their model-predictive alarm and control systems to adapt to the changes in the metabolic state of the user. These AP systems will also integrate modules for controller performance assessment, fault detection and diagnosis, machine learning and classification to interpret various signals and achieve fault-tolerant control. Advances in wearable devices, computational power, and safe and secure communications are enabling the development of fully automated multivariable AP systems.

Number of Transmit Antennas Detection Using Time-Diversity of the Fading Channel

Detection of the number of transmit antennas plays an important role in the design of adaptive multiantenna wireless communication systems. This paper develops a detection algorithm based on second- and fourth-order statistics of the received signal and time-varying nature of the wireless channel. In contrast to existing feature-based algorithms, the proposed algorithm does not require a priori information about the transmitted signals, such as coding scheme, modulation type, and pilot patterns. Unlike information-theoretic algorithms, it does not require the number of receive antennas to be larger than that of transmit antennas. In fact, the proposed algorithm is capable of operating with a single receive antenna even for large numbers of transmit antennas. The performance analysis of the proposed algorithm is provided, and the results are verified through simulations. The algorithm exhibits a good performance over a wide range of signal-to-noise ratios, and is robust to the carrier frequency and timing offsets.

Aberrations in stimulated emission depletion (STED) microscopy

Like all methods of super-resolution microscopy, stimulated emission depletion (STED) microscopy can suffer from the effects of aberrations. The most important aspect of a STED microscope is that the depletion focus maintains a minimum, ideally zero, intensity point that is surrounded by a region of higher intensity. It follows that aberrations that cause a non-zero value of this minimum intensity are the most detrimental, as they inhibit fluorescence emission even at the centre of the depletion focus. We present analysis that elucidates the nature of these effects in terms of the different polarisation components at the focus for two-dimensional and three-dimensional STED resolution enhancement. It is found that only certain low-order aberration modes can affect the minimum intensity at the Gaussian focus. This has important consequences for the design of adaptive optics aberration correction systems.

Adaptive Sparse System Identification in Compressed Space

In this paper, we propose a method for adaptive identification of sparse systems. The method requires low number of filter weights, significantly less than the number of taps of sparse system. The approach is based on compressed sensing (CS) technique. In fact, we adaptively estimate a compressed version of high dimensional sparse system. The aim is accomplished by using the structure of random filter and an interpolator at the transmission line. They are arranged such that the linear time invariant (LTI) property of the overall system (compressed system) is preserved. The unique features of the identification in the reduced dimensions are investigated. Stability in high convergence rates and robustness against highly correlated input signals are two important advantages of the proposed method. Furthermore, we propose a modified algorithm for optimization of the random filter and illustrate its impact by numerical results. Two appropriate reconstruction algorithms are evaluated for recovery of original sparse system. Simulation results indicate that at low levels of sparsity, the proposed approach outperforms the conventional least mean square (LMS) method and has comparable performance with the regularized LMS algorithms, only by half number of the filter weights.

A novel adaptive order/parameter identification method for variable order systems application in viscoelastic soft tissue modeling

This paper presents an adaptive system identification approach to identify the order and parameters of a specific type of variable order systems, which, as a motivating example, describes the stress-strain relation of viscoelastic materials. First, the concept of non-integer order modeling will be introduced. Next, the proposed order/parameter identification approach will be presented. Afterwards, a simulation study is performed to validate the identification approach. Finally, the method will be applied on real data gathered from an experimental study for further validation.

Design of Range Adaptive Wireless Power Transfer System Using Non-coaxial Coils

Wireless Power Transfer (WPT) is a remarkable technology because of its convenience and applicability in harsh environment. Particularly, Magnetic Coupling WPT (MC-WPT) is a proper method to midrange power transfer, but the frequency splitting at over-coupling range, which is related with transfer distance, is challenge of transmission efficiency. In order to overcome this phenomenon, recently the range adaptive WPT is proposed. In this paper, we aim to the type with a set of non-coaxial driving coils, so that this may remove the connection wires from PA (Power Amplifier) to driving coil. And, when the radius of driving coil is changed, on the different gaps between driving and TX coils, coupling coefficient between these is computed in both cases of coaxial and non-coaxial configurations. In addition, the designing steps for 4-coil WPT system using non-coaxial coils are described with the example. Finally, the reliability of this topology has been proved and simulated with PSPICE.

A Mobile Channel Model for VLC and Application to Adaptive System Design

In this letter, we propose a realistic channel model for visible light communication (VLC) assuming a mobile user. Based on non-sequential ray tracing, we first obtain channel impulse responses for each point over the user movement trajectories, and then express path loss and delay spread as a function of distance through curve fitting. Our results demonstrate large variations in received power. In system design, this necessitates the use of adaptive schemes, where transmission parameters can be selected according to channel conditions. To demonstrate the benefits of link adaptation over a mobile VLC channel, we propose an adaptive system with luminary selection and demonstrate improvements in spectral efficiency over non-adaptive systems.

High precision system modeling of liquid crystal adaptive optics systems

In this paper, we present a heuristic method to simplify the liquid crystal adaptive optics system (LCAOS) into a single-input-single-output (SISO) system, then build the dynamic model of LCAOS based on subspace identification. Results show that the identified model could accurately describe the dynamical behavior of LCAOS (97% match), with extremely low complexity. The wonderful features of low complexity and high precision, make the identified model highly beneficial for model based controller design, system analysis and dynamical behavior simulation of liquid crystal adaptive optics systems.

Design and control optimisation of adaptive insulation systems for office buildings. Part 1: Adaptive technologies and simulation framework

The increasing insulation levels imposed by building regulations have the effect of reducing heating energy use, while increasing cooling energy use and/or reducing thermal comfort especially in summer. Adaptive insulation technologies could provide an opportunity to reduce building energy use while simultaneously improving indoor environmental quality, but there is a lack of information about the performance of these novel technologies.This paper is the first of a two part study, which aims to evaluate the performance of adaptive insulation. Part 1 proposes a simulation framework for optimising adaptive insulation design and control parameters and explains its implementation. The customised simulation strategy optimises design and control aspects of adaptive building envelopes by minimising the total primary energy use and thermal discomfort within a building. Moreover the simulation model for adaptive insulation is validated qualitatively. Part 2 applies this framework in a parametric study to explore the potential of adaptive insulation.

Fully printable, strain-engineered electronic wrap for customizable soft electronics

Rapid growth of stretchable electronics stimulates broad uses in multidisciplinary fields as well as industrial applications. However, existing technologies are unsuitable for implementing versatile applications involving adaptable system design and functions in a cost/time-effective way because of vacuum-conditioned, lithographically-predefined processes. Here, we present a methodology for a fully printable, strain-engineered electronic wrap as a universal strategy which makes it more feasible to implement various stretchable electronic systems with customizable layouts and functions. The key aspects involve inkjet-printed rigid island (PRI)-based stretchable platform technology and corresponding printing-based automated electronic functionalization methodology, the combination of which provides fully printed, customized layouts of stretchable electronic systems with simplified process. Specifically, well-controlled contact line pinning effect of printed polymer solution enables the formation of PRIs with tunable thickness; and surface strain analysis on those PRIs leads to the optimized stability and device-to-island fill factor of strain-engineered electronic wraps. Moreover, core techniques of image-based automated pinpointing, surface-mountable device based electronic functionalizing, and one-step interconnection networking of PRIs enable customized circuit design and adaptable functionalities. To exhibit the universality of our approach, multiple types of practical applications ranging from self-computable digital logics to display and sensor system are demonstrated on skin in a customized form.

Design and control optimisation of adaptive insulation systems for office buildings. Part 2: A parametric study for a temperate climate

This paper is the second of a two part study, which aims to evaluate the performance of adaptive insulation. Part 1 proposes a simulation framework for optimising adaptive insulation design and control parameters, it describes its implementation, and validates the simulation strategy qualitatively. This second paper applies the simulation framework, by means of a parametric study on a specific building typology in a particular climatic region, to explore the potential of adaptive insulation in this context. Alternative adaptive insulation configurations and control strategies for opaque wall applications are evaluated, for an office room in a temperate climate of Shanghai, in order to optimise two design objectives: total primary energy saving and thermal comfort. It is found that adaptive insulation, when properly designed and controlled, has significant potential to improve both design objectives simultaneously. For the case study considered in this paper, yearly energy savings and thermal comfort improvements of up to 50% could be achieved by adaptive insulation compared to an equivalent astatic insulation alternative. The performance improvements of the adaptive insulation depend on the design choices (thermal mass, position of the adaptive insulation, switching range of insulation) and control strategy adopted.

A framework to design a human-centred adaptive manufacturing system for aging workers

The so-called smart manufacturing systems (SMS) combine smart manufacturing technologies, cyber-physical infrastructures, and data control to realize predictive and adaptive behaviours. In this context, industrial research focused mainly on improving the manufacturing system performance, almost neglecting human factors (HF) and their relation to the production systems. However, in order to create an effective smart factory context, human performance should be included to drive smart system adaptation in efficient and effective way, also by exploiting the linkages between tangible and intangible entities offered by Industry 4.0. Furthermore, modern companies are facing another interesting trend: aging workers. The age of workers is generally growing up and, consequently, the percentage of working 45–64years old population with different needs, capabilities, and reactions, is increasing. This research focuses on the design of human-centred adaptive manufacturing systems (AMS) for the modern companies, where aging workers are more and more common. In particular, it defines a methodology to design AMS able to adapt to the aging workers’ needs considering their reduced workability, due to both physical and cognitive functional decrease, with the final aim to improve the human-machine interaction and the workers’ wellbeing. The paper finally presents an industrial case study focusing on the woodworking sector, where an existing machine has been re-designed to define a new human-centred AMS. The new machine has been engineered and prototyped by adopting cyber-physical systems (CPS) and pervasive technologies to smartly adapt the machine behaviour to the working conditions and the specific workers’ skills, tasks, and cognitive-physical abilities, with the final aim to support aging workers. The achieved benefits were expressed in terms of system usability, focusing on human-interaction quality.

A new design method for adaptive IIR system identification using hybrid CPSO and DE

Adaptive infinite impulse response filters have received much attention due to its utilization in a wide range of real-world applications. The design of the IIR filters poses a typically nonlinear, non-differentiable and multimodal problem in the estimation of the coefficient parameters. The aim of the current study is the application of a novel hybrid optimization technique based on the combination of cellular particle swarm optimization and differential evolution called CPSO–DE for the optimal parameter estimation of IIR filters. DE is used as the evolution rule of the cellular part in CPSO to improve the performance of the original CPSO. Benchmark IIR systems commonly used in the specialized literature have been selected for tuning the parameters and demonstrating the effectiveness of the CPSO–DE method. The proposed CPSO–DE method is experimentally compared with two new design methods: the tissue-like membrane system (TMS), the hybrid particle swarm optimization and gravitational search algorithm (HPSO–GSA), the original CPSO-outer and CPSO-inner, and classical implementations of PSO, GSA and DE. Computational results and comparison of CPSO–DE with the other evolutionary and hybrid methods show satisfactory results. The hybridization of CPSO and DE demonstrates powerful estimation ability. In particular, to our knowledge, this hybridization has not yet been investigated for the IIR system identification.

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

ABSTRACTThe paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method.

Using physiological signals to measure operator’s mental workload in shipping – an engine room simulator study

ABSTRACTMental workload (MWL) is one of core elements of human factor construct reflecting arousal level, and its optimisation is crucial to maintain favourable operator functional state. However, sensible, reliable, and diagnostic measurement of MWL is essential for applications of adaptive aiding system design, usability testing, and seafarers’ training. To develop robust MWL measures, 10 participants voluntarily participated in a simulator-based experiment study. During this study, the participants carried out standard four-level calibration tasks and simulated four-level maritime operation tasks, their heart rate and electroencephalogram (EEG) were continuously measured using a heart rate sensor and an ambulatory EEG device, which includes an accelerometer to distinguish signal corruption epochs induced by body movement artefact. After each task, NASA Task Load Index was collected as subjective measurement. One-way analysis of variance was used to test the sensitivity of MWL measures and Pearson’s correlation coefficients were calculated. The significantly sensitive indices for n-back task and simulator-based maritime operation task were different, supporting the limited cognitive resource pool theory. EEG features showed higher sensitivity than heart-rate-related measures.

Network models for mapping educational data

Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course.

Design of Adaptive LED Supplemental Lighting System Based on LabVIEW

With the global increase in energy prices and the urgent need to reduce CO2 emissions to the atmosphere, high energy usage is the main problem the greenhouse industry facing. Optimized control of supplemental lighting intensity and quality help to improve productivity and energy efficiency of greenhouse.In this paper,LED light source module is developed according to the design of light distribution of RRGB chip based on plants need for uniform lighting. Adaptive LED supplementary lighting system realize collection, storage and call of environmental data, and adaptive regulation of red, green, and blue light output based on combination of upper computer LabVIEW and controller STM32.PC based on LabVIEW monitor environmental parameters,and call or choose PPFD setting value of red, green, blue three bands of light based on expert database. The controller communicating with LabVIEW wirelessly through WiFi module, conducts real-time detection and adaptive regulation on photosynthetic photon flux density (PPFD) of red, green and blue bands according to PPFD setting value. This intelligent supplemental lighting system meets the requirement of plants for lighting environment and greatly saving energies through adaptive adjustment of PPFD of red, green and blue bands in environment according to the setting based on different supplemental lighting demands of different plants or the same plant in different growth stages for PPFD and light quality ratio.

Prognostic Reasoner based Adaptive Power Management System for a More Electric Aircraft

This research work presents a novel approach that addresses the concept of an adaptive power management system design and development framed in the Prognostics and Health Monitoring(PHM) perspective of an Electrical power Generation and distribution system(EPGS).PHM algorithms were developed to detect the health status of EPGS components which can accurately predict the failures and also able to calculate the Remaining Useful Life(RUL), and in many cases reconfigure for the identified system and subsystem faults. By introducing these approach on Electrical power Management system controller, we are gaining a few minutes lead time to failures with an accurate prediction horizon on critical systems and subsystems components that may introduce catastrophic secondary damages including loss of aircraft. The warning time on critical components and related system reconfiguration must permits safe return to landing as the minimum criteria and would enhance safety. A distributed architecture has been developed for the dynamic power management for electrical distribution system by which all the electrically supplied loads can be effectively controlled.A hybrid mathematical model based on the Direct-Quadrature (d-q) axis transformation of the generator have been formulated for studying various structural and parametric faults. The different failure modes were generated by injecting faults into the electrical power system using a fault injection mechanism. The data captured during these studies have been recorded to form a “Failure Database” for electrical system. A hardware in loop experimental study were carried out to validate the power management algorithm with FPGA-DSP controller. In order to meet the reliability requirements a Tri-redundant electrical power management system based on DSP and FPGA has been developed.

Smart Sensor Framework: A Pressure Sensor for Smart Home Applications

Smart home automation applications require a dense information network for proper func-tionality. Air-conditioning or filtration systems, for example, must detect airflows caused by openwindows and doors. An unambiguous detection of such airflows can be performed by a distributedsensor network. Current off-the-shelf sensors often lack processing and communication units, resulting in a large design assembly of discrete integrated circuits (ICs) on one printed circuit board (PCB)that requires additional power supply. Distributing such designs within a home without interferingwith the existing surroundings proves to be difficult in terms of acceptance and usability. This paperpresents a solution by offering an integrated design that includes a microelectromechanical system(MEMS) pressure sensor element along with an analog to digital converter (ADC) and a customizableand programmable processing unit. The integration leads to a smaller overall footprint and reducedpower consumption, which positively affects the acceptance rate of distributed smart sensor networksfor home automation. Clear interfaces between the components ensure an extensible and adaptablesystem design suitable for further smart sensor applications, resulting in a smart sensor framework.