Adaptive Identification Research Articles

Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Golden jackal optimization (GJO) is inspired by the cooperative attacking behavior of golden jackals and mainly simulates searching for prey, stalking and enclosing prey, and pouncing on prey to solve complicated optimization problems. However, the basic GJO has the disadvantages of premature convergence, a slow convergence rate and low computation precision. To enhance the overall search and optimization abilities, an enhanced golden jackal optimization (EGJO) method with the elite opposition-based learning technique and the simplex technique is proposed to address adaptive infinite impulse response system identification. The intention is to minimize the error fitness value and obtain the appropriate control parameters. The elite opposition-based learning technique boosts population diversity, enhances the exploration ability, extends the search range and avoids search stagnation. The simplex technique accelerates the search process, enhances the exploitation ability, improves the computational precision and increases the optimization depth. EGJO can not only achieve complementary advantages to avoid search stagnation but also balance exploration and exploitation to arrive at the best value. Three sets of experiments are used to verify the effectiveness and feasibility of EGJO. The experimental results clearly demonstrate that the optimization efficiency and recognition accuracy of EGJO are superior to those of AOA, GTO, HHO, MDWA, RSO, WOA, TSA and GJO. EGJO has a faster convergence rate, higher computation precision, better control parameters and better fitness value, and it is stable and resilient in solving the IIR system identification problem.

An Improved Parameter Identification Algorithm for the Friction Model of Electro-Hydraulic Servo Systems.

Friction is an inherent nonlinear disturbance that can lead to creeping, jitter, and decreased tracking precision in an electro-hydraulic servo system. In this paper, the LuGre friction model is used to describe the dynamic and static characteristics of the friction force of a servo system comprehensively. Accurate identification of model parameters is key to implementing friction compensation. However, traditional genetic identification algorithms have the shortcomings of a premature solution, slow convergence, and poor accuracy. To address these shortcomings, this paper proposes an improved adaptive genetic identification algorithm. The proposed algorithm selects evolutionary processes adaptively according to the population concentration in the initial stage of population evolution. Moreover, it adjusts the crossover probability and the mutation probability to identify a local optimum accurately and converge to the global optimum rapidly. During the late stage of population evolution, the accuracy of the global optimal solution can be improved by reducing the search range of identification parameters. The simulation results show that the relative error of the model parameter values identified by the proposed algorithm is reduced to less than 1% and the convergence speed is faster. Compared with the existing traditional genetic algorithm and adaptive genetic algorithm, the overall performance of the proposed method is better. This study provides a feasible and highly accurate identification method for parameter identification of friction models used in electro-hydraulic servo systems.

Infrastructure Adaptation and Climate Resilience for California’s National Forests

This paper describes the road infrastructure found in California’s national forests, their vulnerabilities, and specific measures that can be taken to adapt to projected climate change effects, thus minimizing damage from fires and storms. Over the past 40 years this region has been hit by numerous climate change–related events including droughts, major forest fires, major storms, and flooding. Billions of dollars in damage have been sustained and numerous lives lost. It is necessary now to assess vulnerabilities, rank resources at risk, and prioritize adaptation actions. The Forest Service has recently been involved in infrastructure vulnerability assessment and adaptation strategy projects involving climate model studies, interviews, a literature review, local workshops, website information, and publication of the project findings. Different agency vulnerability assessment methods have been reviewed to establish a functional assessment and risk analysis methodology. Efforts to mitigate the impacts of climate change have included greenhouse gas reduction from agency vehicles, evaluating alternative transportation routes, implementing energy-saving measures, and identifying “stormproofing” road design measures to reduce the vulnerability of roads to extreme climate-related events. Much of the effort has been the identification of road adaptation and resiliency measures, particularly measures that are practical and implementable at minimum cost. These measures include: routine road maintenance; relocating road segments as needed; adding trash racks and diversion prevention dips to prevent culvert failures; building stream simulation projects; protecting bridges from debris and scour; covering soil with deep-rooted vegetation; and using soil bioengineering stabilization and deep-patch shoulder reinforcement to prevent local slope failures.

Quadcopter Drone for Vision-Based Autonomous Target Following

Unmanned aerial vehicles (UAVs) are becoming popular in various applications. However, there are still challenging issues to be tackled, such as effective obstacle avoidance, target identification within a crowd, and specific target tracking. This paper focuses on dynamic target following and obstacle avoidance to realize a prototype of a quadcopter drone to serve as an autonomous object follower. An adaptive target identification system is proposed to recognize the specific target in the complicated background. For obstacle avoidance during flight, we introduce an idea of space detection and use it to develop a so-called contour and spiral convolution space detection (CASCSD) algorithm to evade obstacles. Thanks to the low architecture complexity, it is appropriate for implementation on onboard flight control systems. The target prediction is integrated with fuzzified flight control to fulfill an autonomous target tracker. When this series of technical research and development is completed, this system can be used for applications such as personal security guard and criminal detection systems.

Designing healthcare for human use: Human factors and practical considerations for the translational process.

In recent years, the focus of implementation science (IS) shifted to emphasize the influence of contextual factors on intervention adaptations in clinical, community, and corporate settings. Each of these settings represent a unique work system with varying contexts that influence human capabilities, needs, and performance (otherwise known as "human factors"). The ease of human interaction with a work system or an intervention is imperative to IS outcomes, particularly adoption, implementation, and maintenance. Both scientific approaches consider the "big picture" when designing interventions for users and stakeholders to improve work and health outcomes. IS and human factors are therefore complementary in nature. In this paper, the authors will (1) provide perspective on the synergistic relationship between human factors and IS using two illustrative and applied cases and (2) outline practical considerations for human factors-based strategies to identify contextual factors that influence intervention adoption, implementation, and maintenance dimensions of the RE-AIM framework. This article expands on recent research that developed user- and human-centered design strategies for IS scientists to use. However, defining the complementary relationship between IS and human factors is a necessary and valuable step in maximizing the effectiveness of IS to transform healthcare. While IS can complement practitioners' identification of intervention adaptations, human interaction is a process in the work system often overlooked throughout implementation. Further work is needed to address the influence that organizational endorsement and trust have on intervention adaptations and their translation into the work system.

Inferring Moore Machine for adaptive online Hybrid Automaton Identification

In the context of CPS modeling, this paper proposes a new method for online adaptive identification of hybrid systems. The method relies on a Moore machine identification process; A mapping between a hybrid automaton and a discrete event Moore machine is proposed, and a new online adaptive Moore machine identification method is proposed. A case study illustrates the proposed method.

Anthecology of some species ofHamanthus BungeandHalimocnemis C.A. Mey

It is promising to grow the long-vegetating annual halophytesGamanthus BungeandHalimocnemis C.A. Mey. (Chenopodiaceae Vent.) in arid locations since they are important grazing plants for Karakol sheep and camels. These species’ xerothermic periods are when they flower and bear fruit. Research on reproductive biology must first examine how certain traits of plant reproductive organs in harsh environments relate to environmental influences. Identification of reproductive organ adaptation to air temperature and water stress in desert plants is an issue that has not received much attention fromChenopodiaceaefamily members. Such knowledge is crucial for developing perennial agrophytocenoses and boosting the production of pastures in need. The study ofHalimocnemisspecies is particularly interesting since this genus is thought to be confined to the Iran-Turonian floristic zones, with the Central Asian (Turonian) plains being its center of origin. In the studied species, there are signs that ensure the xenogamous type of pollination by entomophilia. These are proterandria in flowers, brightly colored outgrowths of pollen which are secondary attractants, morning type of flowering when insects are in high flight, and autogamous type of pollination by anemophily. Similar conditions eventually lead to funneling ofHalimocnemisandG. gamocarpus.

Vibration-Adaption Deep Convolutional Transfer Learning Method for Stranded Wire Structural Health Monitoring Using Guided Wave

External vibration is the main disturbance condition in the practical monitoring of outdoor stranded structure using laser ultrasonic guided wave (UGW). It is difficult to extract and identify the real damage state under different vibration conditions due to the variation of the guided-wave feature distribution. At present, there is no effective solution to this practical problem. In this article, a new deep cross-domain adaptive semisupervised damage identification method is proposed by using transfer learning method and combining with the actual demand of stranded guided-wave monitoring. First, a novel laser excitation-piezoelectric receiving sensing method is realized by taking full advantage of the noncontact characteristics, wide frequency band, and high stability of the laser and piezoelectric sensors. Second, a multilayer convolutional neural network (CNN) is constructed to extract the damage features of the guided-wave signals in the source domain and map them to the high-level hidden space. Then, a multicore maximum mean discrepancy (MMD) method is designed to reduce the distribution difference of damage features between the target and source domains by using the optimal multicore selection method, and the essential damage features of UGWs were learned. Finally, different damage states of the target domain are effectively identified by feature identification. The experimental results illustrate that the proposed method can realize automatic extraction of inherent damage features and adaptive matching of multilayer features, connect the source and target domains in the high-level feature space, and learn the invariant features of guided-wave signals under different vibrations. Moreover, the proposed method has a good performance both in the mean between-class average distance and the mean within-class average distance damage degree of feature under various vibration conditions, reaches 100% accuracy in damage degree identification under different vibration conditions, and shows better performance than the comparison methods.

THE PROBLEM OF MINIMIZING DISPERSION

The work is related to the problem of finding the probability density of a continuous random variable in such a way that the variance or initial moments of this variable would be the smallest. The problem of minimizing the variance is solved under the condition that the probability density u x of a continuous random variable does not exceed a given function q x . The result of the work is obtaining such a density: it should be equal to the given function q x in some interval , , outside this interval the density should be zero. The numbers α and β are find from the system of nonlinear equations and are included in these equations as limits of definite integrals. By substituting the found function in the extreme condition, the smallest variance value for any continuous random variable is obtained. The problem of finding the probability density of a random variable is also considered under the conditions x u x dx u x dx u x q k              min, 1, 0 , where q and k are given positive numbers. The described problems belong to the class of Lyapunov extremal problems containing integrals. When solving them, the minimum principle and the method of Lagrange multipliers were applied. As a consequence of the Weierstrass theorem, it follows that the found stationary points are the points of the smallest values of the considered functionals. Substituting the solution of the problem into an extreme condition, an inequality for the initial moments of any continuous random variable, which is bounded from above by a positive constant q is obtained. It is emphasized that the determination of parameters as a result of minimization will allow to determine more accurate estimates in other values and to achieve an effective level of parameter optimization. The results of solving mathematical problems are given. An exemplary application of the given problem is the implementation of adaptive forecasting and statistical identification of nonlinear dynamic models of physical and economic processes. The scientific novelty of the obtained solution lies in the fact that, for the first time, a system was obtained in which the mathematical expectation of an extreme random variable is located in the middle of the segment , , due to which, by substituting the found function in an extreme condition, the smallest variance value for any continuous random variable is obtained.

CAT HPPR: a critical appraisal tool to assess the quality of systematic, rapid, and scoping reviews investigating interventions in health promotion and prevention

BackgroundFor over three decades researchers have developed critical appraisal tools (CATs) for assessing the scientific quality of research overviews. Most established CATs for reviews in evidence-based medicine and evidence-based public health (EBPH) focus on systematic reviews (SRs) with studies on experimental interventions or exposure included. EBPH- and implementation-oriented organisations and decision-makers, however, often seek access to rapid reviews (RRs) or scoping reviews (ScRs) for rapid evidence synthesis and research field exploration. Until now, no CAT is available to assess the quality of SRs, RRs, and ScRs following a unified approach. We set out to develop such a CAT.MethodsThe development process of the Critical Appraisal Tool for Health Promotion and Prevention Reviews (CAT HPPR) included six phases: (i) the definition of important review formats and complementary approaches, (ii) the identification of relevant CATs, (iii) prioritisation, selection and adaptation of quality criteria using a consensus approach, (iv) development of the rating system and bilingual guidance documents, (v) engaging with experts in the field for piloting/optimising the CAT, and (vi) approval of the final CAT. We used a pragmatic search approach to identify reporting guidelines/standards (n = 3; e.g. PRISMA, MECIR) as well as guidance documents (n = 17; e.g. for reviews with mixed-methods approach) to develop working definitions for SRs, RRs, ScRs, and other review types (esp. those defined by statistical methods or included data sources).ResultsWe successfully identified 14 relevant CATs, predominantly for SRs (e.g. AMSTAR 2), and extracted 46 items. Following consensual discussions 15 individual criteria were included in our CAT and tailored to the review types of interest. The CAT was piloted with 14 different reviews which were eligible to be included in a new German database looking at interventions in health promotion and prevention in different implementation settings.ConclusionsThe newly developed CAT HPPR follows a unique uniformed approach to assess a set of heterogeneous reviews (e.g. reviews from problem identification to policy evaluations) to assist end-users needs. Feedback of external experts showed general feasibility and satisfaction with the tool. Future studies should further formally test the validity of CAT HPPR using larger sets of reviews.

High-Tracking-Precision Sensorless Control of PMSM System Based on Fractional Order Model Reference Adaptation

In order to solve parameter fluctuations and disturbances, a novel fractional order model reference adaptive speed observer that estimates the rotor position and the angular velocity from the stator currents is proposed for sensorless control of permanent-magnet synchronous motors (PMSM). Firstly, a novel fractional order model reference adaptive controller (FOMRAC) for adaptive identification is designed to achieve fast response and high precise identification of load torque in the full speed range when the motor is running at variable-speed or variable-load. Additionally, an appropriate adjustable matrix P is chosen to make the convergence of the adaptive law meet the requirements. Next, an improved model reference adaptive observer (MRAO) is proposed to suppress the serious chattering and compensate rotor position error, which can stabilize the system. The validity of the proposed fractional order model reference adaptive sensorless control strategy for PMSM is demonstrated with simulations.

Model Predictive Control Technique for Ducted Fan Aerial Vehicles Using Physics-Informed Machine Learning

This paper proposes a model predictive control (MPC) approach for ducted fan aerial robots using physics-informed machine learning (ML), where the task is to fully exploit the capabilities of the predictive control design with an accurate dynamic model by means of a hybrid modeling technique. For this purpose, an indigenously developed ducted fan miniature aerial vehicle with adequate flying capabilities is used. The physics-informed dynamical model is derived offline by considering the forces and moments acting on the platform. On the basis of the physics-informed model, a data-driven ML approach called adaptive sparse identification of nonlinear dynamics is utilized for model identification, estimation, and correction online. Thereafter, an MPC-based optimization problem is computed by updating the physics-informed states with the physics-informed ML model at each step, yielding an effective control performance. Closed-loop stability and recursive feasibility are ensured under sufficient conditions. Finally, a simulation study is conducted to concisely corroborate the efficacy of the presented framework.

Can cross-sector information improve multi-energy demand forecasting accuracy?

More and more distributed energy resources are integrated into regional integrated energy systems (RIES), which poses great challenges to energy balance. RIES can coordinate power, gas, heat, and cooling systems jointly to enhance energy efficiency and explore flexibility for distributed renewable energy accommodation. Multi-energy demand forecasts are the basis of the flexible operation of RIES. However, the multi-energy demands are deeply coupled in RIES. Related researches utilize cross-sector information from different sectors to tackle the coupling relationship. Nevertheless, the unknown influence of cross-sector information offered by other sectors varies with the operating pattern, which is difficult to be evaluated. This work proposes an operating pattern recognition-based method for adaptive cross-sector information identification. Firstly, the K-means cluster algorithm is adopted to identify different operating patterns. After that, cross-sector information is selected based on the Pearson coefficient. Furthermore, two models, i.e., the local model and fine-tuned model, are modified with the assistance of selected cross-sector information. The proposed method is evaluated by a RIES with three energy types (electricity, chill water, and steam). The proposed two methods acquire better accuracy than the three benchmark models. Moreover, the Shapley value is applied to verify the contribution of selected cross-sector information. The result shows that all selected cross-sector information plays a significant role in load prediction.

An adaptive synchroextracting transform for the analysis of noise contaminated multi-component nonstationary signals

The Synchro-Extracting Transform technique (SET) can capture the changing dynamic in a non-stationary signal which can be applied for fault diagnosis of rotating machinery operating under varying speed or/and load conditions. However, the time frequency representation (TFR) of a signal produced by SET can be affected by noise contained in the signal, which can largely reduce the accuracy of fault diagnosis. This paper addresses this drawback and presents a new extraction operator to improve the energy concentration of the TFR of a noise contaminated multi-component signal by using an adaptive ridge curve identification process together with SET. The adaptive ridge curve extraction is deployed to extract the signal components of a multi-component signal via an iterative approach. The effectiveness of the algorithm is verified using one set of simulated noise-added signals and two sets of experimental bearing and gearbox defect signals. The result shows that the proposed technique can accurately identify the fault components from noise contaminated multi-component non-stationary machine defect signals.

Ensemble Modified Aptamer Based Pattern Recognition for Adaptive Target Identification.

The difficulty of the molecular design and chemical synthesis of artificial sensing receptors restricts their diagnostic and proteomic applications. Herein, we report a concept of "ensemble modified aptamers" (EMAmers) that exploits the collective recognition abilities of a small set of protein-like side-chain-modified nucleic acid ligands for discriminative identification of molecular or cellular targets. Different types and numbers of hydrophobic functional groups were incorporated at designated positions on nucleic acid scaffolds to mimic amino acid side chains. We successfully assayed 18 EMAmer probes with differential binding affinities to seven proteins. We constructed an EMAmer-based chemical nose sensor and demonstrated its application in blinded unknown protein identification, giving a 92.9% accuracy. Additionally, the sensor is generalizable to the detection of blinded unknown bacterial and cellular samples, which enabled identification accuracies of 96.3% and 94.8%, respectively. This sensing platform offers a discriminative means for adaptive target identification and holds great potential for diverse applications.

Population Structure and Morphological Pattern of the Black-Spotted Pond Frog (Pelophylax nigromaculatus) Inhabiting Watershed Areas of the Geum River in South Korea

Black-spotted pond frogs (Pelophylax nigromaculatus), widely distributed in East Asia, can be suitably used for the study of population genetic patterns and ecosystem monitoring. To systematically manage, conserve, and study this species, it is necessary to understand its habitat range. We analyzed the genetic and morphological range of black-spotted pond frog populations within a watershed of the Geum River, one of the main rivers in South Korea. We genotyped the frogs based on seven microsatellite loci and defined the skull shape based on landmark-based geometric morphometrics. One watershed area was divided into 14 sub-watershed areas, the smallest unit of the Geum River basin. The genetic structure of frogs among the 14 sub-watershed areas did not differ significantly, nor was correlated with geographic distance. Therefore, frogs within these watershed areas constitute a single population. Morphologically, they differed between some sub-watershed areas, but morphological distance did not correlate with genetic distance but rather with geographic distance. This morphological change differs depending on the environmental gradient rather than the genetic structure. As a single population, frogs in this watershed area need to be managed in an integrated way. We suggest that the identification of response and adaptation by population genetics must be compared across and beyond the watershed range.

Implementation of a Model Predictive Control Strategy to Regulate Temperature Inside Plug-Flow Solar Reactor With Countercurrent Flow

Abstract Solar-driven thermochemical energy storage systems are proven to be promising energy carriers (solar fuels) to utilize solar energy by using reactive solid-state pellets. However, the production of solar fuel requires a quasi-steady-state process temperature, which represents the main challenge due to the transient nature of solar power. In this work, an adaptive model predictive controller (MPC) is presented to regulate the temperature inside a tubular solar reactor to produce solid-state solar fuel for long-term thermal storage systems. The solar reactor system consists of a vertical tube heated circumferentially over a segment of its length by concentrated solar power, and the reactive pellets (MgMn2O4) are fed from the top end and flow downwards through the heated tube. A countercurrent flowing gas supplied from the lower end interacts with flowing pellets to reduce it thermochemically at a temperature range of 1000—1500 °C. A low-order physical model was developed to simulate the dynamics of the solar reactor including the reaction kinetics, and the proposed model was validated numerically by using a 7-kW electric furnace. The numerical model then was utilized to design the MPC controller, where the control system consists of an MPC code linked to an adaptive system identification code that updates system parameters online to ensure system robustness against external disturbances (sudden change in the flow inside the reactor), model mismatches, and uncertainty. The MPC controller parameters are tuned to enhance the system performance with minimum steady-state error and overshoot. The controller is tested to track different temperature ranges between 500 °C and 1400 °C with different particles/gas mass flowrates and ramping temperature profiles. Results show that the MPC controller successfully regulated the reactor temperature within ± 1 °C of its setpoint and maintained robust performance with minimum input effort when subjected to sudden changes in the amount of flowing media and the presence of chemical reaction.

Composite Adaptive Control and Identification of MIMO Aeroelastic System with Enhanced Parameter Excitation

Composite Adaptive Control and Identification of MIMO Aeroelastic System with Enhanced Parameter Excitation

Adaptive Identification of System with Bouc-Wen Hysteresis Modifications

The adaptive identification method developed to evaluate the parameters of the Bouc-Wen hysteresis (BWH). The adaptive approach based on the use of adaptive observer. We synthesize adaptive identification algorithms using the second Lyapunov method. Requirements for the input of the system which guarantee the identification of parameters considered. We propose BWH modifications (BWHM). Adaptive algorithms for estimating BWHM parameters developed. The boundedness of adaptive system processes shown in coordinate and parametric spaces. We prove the exponential dissipativity of processes in an adaptive system by using the Lyapunov vector function method. Estimating method proposed for signaling uncertainty in the system.

Adaptive dynamic adjustment star identification threshold estimation for a terrestrial star tracker

As the criterion to determine whether stars are identified, the star image identification matching threshold is an important parameter in terrestrial star trackers. It not only determines the identification success rate, but also affects the redundant matching quantity and identification efficiency. This paper focuses on the problem that the identification efficiency of terrestrial star trackers is restricted by inappropriate matching thresholds and presents an adaptive dynamic adjustment star identification threshold model. Compared to the existing matching threshold, the presented model clarifies the transformation of observation star angular distance errors and dynamically estimates the corresponding identification threshold as the variation of observation angular distances and attitudes. Therefore, we believe it completes the star identification with an excellent redundant matching quantity and identification efficiency. Numerical simulation and night sky experimental results showed that the identification efficiency was improved by more than 46.54% and 22.61%, respectively, while the identification success rate remained at 100%.