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Real-world multi-objective optimization problems encounter different types of uncertainty that may affect the quality of solutions. One common type is the stochastic noise that contaminates the objective functions. Another type of uncertainty is the different forms of dynamism including changes in the objective functions. Although related work in the literature targets only a single type, in this paper, we study Dynamic Multi-objective Optimization problems (DMOPs) contaminated with stochastic noises by dealing with the two types of uncertainty simultaneously. In such problems, handling uncertainty becomes a critical issue since the evolutionary process should be able to distinguish between changes that come from noise and real environmental changes that resulted from different forms of dynamism. To study both noisy and dynamic environments, we propose a flexible mechanism to incorporate noise into the DMOPs. Two novel techniques called Multi-Sensor Detection Mechanism (MSD) and Welford-Based Detection Mechanism (WBD) are proposed to differentiate between real change points and noise points. The proposed techniques are incorporated into a set of Dynamic Multi-objective Evolutionary Algorithms (DMOEAs) to analyze their impact. Our empirical study reveals the effectiveness of the proposed techniques for isolating noise from real dynamic changes and diminishing the noise effect on performance.

IEEEWhile the zeitgeist of today prescribes localization as a key to eliminate the excessive transportation for manufacturing firms, firm-level practical and workable guidelines that go beyond maxims are scant. Laden with analytical methods, this paper offers a phronetic, customizable framework based on Lean Six Sigma (LSS). The framework mimics Define-Measure-Analyze-Improve-Control (DMAIC) process. The early phases of the framework, ex-ante, prescribe the use of a variety tools and techniques from Lean Six sigma (LSS) and employ fuzzy analytic hierarchy process (FAHP) to extract the vital few root-causes that may impede localization. The latter phases suggest the use of design of experiments (DOE) to evaluate the root-causes, ex-post. The paper outlines a clear and established procedure and demonstrates each analytical phase using evidence from a two-year real-world application in steel industry.

ispartof: IEEE Control Systems Letters vol:7 pages:1670-1675 status: published

Silo Discharge System Silo Discharge System Operation: Step-by-Step Process and Workflow. In a silo discharge system, silos serve as essential storage solutions for a wide range of industries, including food, plastics, paint, cement, steel, and pharmaceuticals. These versatile storage facilities play a crucial role in maintaining uninterrupted production amidst disruptions in the supply chain. By accommodating large quantities of bulk powders, silos effectively handle the demands of production lines, surpassing the limitations of manual handling alone. Moreover, the operation of silos remains impeccable, boasting a clean and dust-free environment that eliminates any human contact with the product. In the silo discharge system, the filling process of these silos depends on the availability of raw materials, which can be done using bulk tankers by way of bulk tanker unloading systems orpneumatic conveying systems. To ensure efficient discharge, each silo is equipped with various live bottom arrangements, such as bin activators, fluidizing cones, and unloading valves. These components work harmoniously to enable the consistent and controlled discharge of different materials, facilitating effective inventory management and control. As a result, the plant operation becomes more flexible and benefits from improved material supply chain management. Components of the Silo Discharge System Conveyor System:The conveyor system plays a vital role in transferring material from the silo to the desired destination. It consists of belts, buckets, or screw conveyors that move the material efficiently and safely. The type of conveyor system used depends on the characteristics of the stored material and the distance it needs to be transported. Silo Discharge Equipment:This includes various devices and mechanisms responsible for facilitating the flow of material from the silo. Discharge Gates and Valves:These are openings located at the bottom of the silo that can be manually or automatically controlled to regulate the material flow. They provide a controlled discharge of materials into the conveyor system. Fluidizers and aerators:These are devices attached to the silo walls that use compressed air to agitate and fluidize the stored material, ensuring smooth flow and preventing bridging or clogging issues. Rotary Dischargers:Also known asrotary valves, these devices consist of rotating blades that break up and disperse the material as it moves through them. Rotary dischargers are commonly used for fine or granular materials. Vibrators:Vibrators are installed on the walls of the silo to eliminate material buildup and promote material flow. They use mechanical vibrations to loosen and dislodge materials that may be sticking to the silo walls. Control System:An efficient control system is essential for smooth operation and proper coordination of the silo discharge system. It includes sensors, level indicators, and automated controls that monitor material levels, flow rates, and other critical parameters. This ensures optimal performance, prevents overflows or blockages, and maintains the desired flow rate. Operation of a Silo Discharge System: The operation of a silo discharge system generally involves the following steps: Material Loading:Bulk materials are loaded into the silo through an inlet point, either manually or through automated systems such as conveyors, bulk tanker unloading systems orpneumatic transfer systems. Material Storage:Once inside the silo, the material is stored in a controlled environment, protecting it from external elements such as moisture, pests, or contamination. The stored material settles, and if necessary, it can be conditioned or aerated to maintain its quality. Material Discharge:When the need arises to retrieve the stored material, the discharge system is activated. The discharge gates/valves are opened, allowing the material to flow into the conveyor system. Material Transportation:The conveyor system, driven by motors or other power sources, transfers the material from the silo to the desired destination, such as trucks, trains, or processing equipment. Monitoring and Control:Throughout the entire operation, the control system continuously monitors various parameters like material levels, flow rates, and any potential issues. It provides feedback to the operators and adjusts the system's parameters to maintain optimal performance. Safety Measures:Silo discharge systems are designed with safety in mind. They incorporate safety devices such as emergency shut-off switches, alarms, and pressure relief systems to prevent accidents or equipment damage. By implementing an efficient silo discharge system, industries can streamline their operations, minimize downtime, and ensure a constant and reliable supply of materials. AtStratgem, we specialize in designing and implementing advanced silo bulk storage systems and Silo discharge systems tailored to your specific needs. With our expertise and high-quality equipment, we can help you optimize your material handling processes and enhance productivity. Contact us todayto learn more about our services and how we can assist you in achieving efficient, reliable storage & discharge of bulk materials. &nbsp

ispartof: JOURNAL OF PROCESS CONTROL vol:117 pages:169-180 status: published

In this article, a kinematic continuous time swarm model is considered by extending the results in the paper \"A class of attractions/repulsion functions for stable swarm aggregations\" (as shown in the International Journal of Control, vol. 77, pages 1567-1579). The extension regards a more general class of attraction/repulsion functions that can be used to explain the formation of rotational patterns in a swarm of agents. The main characteristic of this model is the introduction of two coordinate-coupling matrices weighting both the attractive and the repulsive interactions among the agents. A stability analysis is presented to characterize the swarm in terms of size and cohesiveness. Moreover, a class of attraction/repulsion functions is presented to guarantee the cohesiveness of the swarm in a polytopic region and a steady-state motion of the agents in a rotational frame around the swarm centroid. Numerical simulations are provided to illustrate the obtained results.

Harmonic current estimation is required in active power filters for compensation purposes. The most efficient way of calculating the total harmonic current up to the infinite order is to subtract the fundamental component from the distorted current. Direct determination of the fundamental component of a distorted current of mains frequency was realized recently with a third-order modulation integral observer. This article shows that using an exponential modulation function (Exp-MF) for the integral observer will: 1) significantly enhance the robustness of the observer against noise and 2) automatically remove the low-frequency envelope arising from the D/A and A/C sampling processes compared to previous polynomial modulation function. These new and advantageous features are supported with detailed analysis and experimental verification. The robust observer can be implemented in grid-connected power electronics circuits that require the instantaneous information of the fundamental and/or harmonic currents. Practical comparative tests with the adaptive notch filter and recursive discrete Fourier transform (DFT) methods in an active power filter have confirmed the good performance under both the steady and dynamic states of the proposed Exp-MF integral observer.

Streaming data provides substantial challenges for data analysis. From a computational standpoint, these challenges arise from constraints related to computer memory and processing speed. Statistically, the challenges relate to constructing procedures that can handle the so-called concept drift--the tendency of future data to have different underlying properties to current and historic data. The issue of handling structure, such as trend and periodicity, remains a difficult problem for streaming estimation. We propose the real-time adaptive component (RAC), a penalized-regression modeling framework that satisfies the computational constraints of streaming data, and provides the capability for dealing with concept drift. At the core of the estimation process are techniques from adaptive filtering. The RAC procedure adopts a specified basis to handle local structure, along with a least absolute shrinkage operator-like penalty procedure to handle over fitting. We enhance the RAC estimation procedure with a streaming anomaly detection capability. The experiments with simulated data suggest the procedure can be considered as a competitive tool for a variety of scenarios, and an illustration with real cyber-security data further demonstrates the promise of the method.

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the project being ongoing. Copyright © 2023 by the authors. Electric distribution grids are seeing an increased penetration of photovoltaic (PV) generation. High PV generation exceeding the grid load demand results in a reverse active power flow in the grid, which raises the voltage level. This paper presents a reactive power controller to overcome the overvoltage problem in the distribution system. A sequentially coordinated and cooperative volt/var control technique is presented. The proposed controller aims to use as low reactive power as possible while mitigating the voltage issues. Accordingly, it reduces the active power loss associated with reactive power flow and reduces the probability for active power curtailment of the PV system. The controller is developed for each lateral and is replicated for all laterals. The lateral controller coordinates the operation of the smart PV inverters in a sequential manner. Cooperative control is proposed between the laterals’ controllers as well and is engaged when the individual laterals’ controllers are unable to solve their overvoltage issues. The performance of the proposed controller is evaluated by comparing it to two other volt/var controllers, and it demonstrates better performance in terms of reactive power requirement. To conduct the simulation study, a modified version of the unbalanced IEEE 13-bus system is utilized, which includes an additional 44 low-voltage bus. The study involves simulating 720 operating points across daily time series. The results indicate that the proposed controller effectively addresses overvoltage problems that occur during periods of high PV generation. Newton Fund Institutional Links grant number 623801791, under the Newton-Katip Çelebi Fund partnership. The grant was funded by the UK Department for Business, Energy and Industrial Strategy and TUBITAK grant number 120N996 and delivered by the British Council.

In this paper, afull-car prototype of the recently proposed mechatronic suspension, Series Active Variable Geometry Suspension (SAVGS), is developed for on-road driving experimental proof of concept, aiming to be adopted by suspension OEMs (original equipment manufacturers) as an alternative solution to fully active suspensions. Particularly, mechanical modifications are performed to both corners of the front double-wishbone suspensionof a production car, with active single-links attached to the upper-ends of the spring-damper units, while both corners of the rear suspension remain inthe original (passive) configurations.The mechanical modifications involve innovatively designed parts to enable the desired suspension performance improvements, while maintaining ride harshness at conventional levels.Areal-time embedded system is further developed to primarily implement:1) power supply, data acquisition and measurementsof the vehicle dynamics related variables, and 2) robust control application for the ride comfort and road holding enhancement, which is based on a derived linearized model of the full-car dynamics and a newly synthesizedH-infinity control scheme. Results obtained from on-road driving experiments are inessential agreement with numerical simulation results also produced. Overall, the full-car prototypeof SAVGS demonstrates promising suspension performance,with anaverage 3 dB attenuation (or equivalently 30% reduction) of the chassis vertical acceleration at aroundthe human-sensitive frequencies (2-5Hz),as compared to the original vehicle with the passive suspension system. More importantly, the prototype also indicatesthe practicality of the solution, as the SAVGS retrofit to a real car is achieved by simple mechanical modifications, compact actuator packaging, smallmass increment(21.5kg increase with respect to the original vehicle), limited power usage (an average value of 134W in DC batteries with a Class D random road) and acceptable economic cost.

ispartof: IET CONTROL THEORY AND APPLICATIONS vol:16 issue:10 pages:1042-1061 status: published

Multiobjective optimisation is used when a system needs to be optimised for multiple conflicting objectives. However, the solution of such a problem is not one point but a set of equally optimal points called Pareto points which highlight the trade-off required to achieve optimisation of one objective over another. As any computer aided approaches depend on the model, any uncertainties in the model have a large influence on the solution of the optimisation. Thus, the uncertainty needs to be incorporated into the optimisation methodology. In this paper, along with incorporating uncertainty in the optimisation, an ellipsoid based filter is described to select points in the Pareto set which are significantly different from each other. This filter is applied to the case study of beer fermentation. Fermentation is a key step in brewing which affects the flavour and stability of beer. Along with operating parameters like yeast pitching, dissolved oxygen, etc., fermentation temperature has a large influence on beer flavour and fermentation progression. In this paper, this temperature profile is optimised using multiobjective optimisation for conflicting objectives. The novel Pareto ellipsoid based filter is used to discriminate between Pareto points and generate a Pareto set that contains only the relevant solutions.

Following the seminal work of Zames, the input-output theory of the 70s acknowledged that incremental properties (e.g. incremental gain) are the relevant quantities to study in nonlinear feedback system analysis. Yet, non-incremental analysis has dominated the use of dissipativity theory in nonlinear control from the 80s. Results connecting dissipativity theory and incremental analysis are scattered and progress has been limited. This abstract investigates whether this theoretical gap is of fundamental nature and considers new avenues to circumvent it., Comment: To be presented at MTNS 2022

ispartof: pages:709-714 ispartof: IFAC PAPERSONLINE vol:55 issue:7 pages:709-714 ispartof: 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS) location:SOUTH KOREA, Busan date:14 Jun - 17 Jun 2022 status: published

ispartof: pages:364-369 ispartof: IFAC PAPERSONLINE vol:55 issue:7 pages:364-369 ispartof: 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS) location:SOUTH KOREA, Busan date:14 Jun - 17 Jun 2022 status: published

This paper presents a novel optimization approach to find the optimal STATCOM installation in the power system using Mixed Integer Distributed Ant Colony optimization (MIDACO). The proposed method is mathematically formulated based on minimizing the Voltage Deviation Index (VDI) and the installation cost of STATCOM. The ability of STATCOM to provide shunt compensation to maintain bus voltage is exploited here. The proposed approach has been tested on the standard IEEE 14-bus system.

© 2023 by the authors.The important parts of a transformer, such as the core, windings, and insulation materials, are in the oil-filled tank. It is difficult to detect faults in these materials in a closed area. Dissolved Gas Analysis (DGA)-based fault diagnosis methods predict a fault that may occur in the transformer and take the necessary precautions before the fault grows. Although these fault diagnosis methods have an accuracy of over 95%, their validity is controversial since limited data are used in the studies. The success rates and reliability of fault diagnosis methods in transformers, one of the most important pieces of power systems equipment, should be increased. In this study, a hybrid fault diagnosis system is designed using DGA-based methods and Fuzzy Logic. A mathematical approach and support vector machines (SVMs) were used as decision-making methods in the hybrid fault diagnosis systems. The results of tests performed with 317 real fault data sets relating to transformers showed accuracy of 95.58% using a mathematical approach and 96.23% using SVMs.

Controlling soft continuum manipulators is a challenging task due to the structural compliance of these systems which results in underactuation. While a variety of control approaches have been proposed for regulation tasks, tracking control requires further investigation. To this end, a new dynamic tube-Model Predictive Control formulation is presented, which includes an ancillary control law constructed analytically with an energy-shaping approach. Two different models are considered: (i) a rigid-link model with direct-torque actuation, which is representative of miniature soft continuum manipulators supplied by digital pressure regulators; (ii) an extended model that includes the pressure dynamics of the fluid, which is representative of fluidic actuation by means of a variable flow rate. The effectiveness of the proposed algorithm is demonstrated with numerical simulations considering bounded external disturbances, either constant or time-varying.

© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.With the growing state/action space, learning a satisfactory policy for regular Reinforcement Learning (RL) algorithms such as flat Q-learning becomes quickly infeasible. One possible solution to handle such cases is to employ hierarchical RL (HRL). In this work, we present two methods to autonomously construct (1) skills (ASKA) and (2) arbitrarily elaborate superskills or complexes through defining an arbitrary number of hierarchies in HRL (ASKAC) over a graph-based iteratively-growing environment model. We employ dynamic community detection (DCD) in detecting subgoals since DCD considers local changes only over the partially growing graphs and lowers the time complexity of the subgoal detection where groups of environment states (i.e., subenvironments) are modeled by communities from the graph theory. DCD’s drawback is oversegmentation where it mispartitions a subenvironment further into smaller components. To maintain the robustness of ASKAC against DCD’s possible oversegmentation we introduce the concept of skill coupling. Skill coupling does not only robustly solve the oversegmentation issue, but it also improves HRL by building up more elaborate complexes (i.e., skill compositions) obtained at an arbitrary number of hierarchies and reduces the number of decisions leading to the goal employing these complexes. In addition to the experiments that investigate the effect of parameters, proposed methods are experimentally evaluated in grid world and taxi driver benchmark environments.

In this paper, we study the heterogeneous consensus problem in directed networks consisting of first- and second-order agents that can only receive the position states of their neighbors. Necessary and sufficient conditions on the controller parameters are obtained in order to achieve consensus in the network. The mathematical expressions of the consensus equilibria are given for two different scenarios. Furthermore, we propose a systematic method for choosing controller parameters to ensure stability in a network of agents with heterogeneous dynamics. Several numerical examples are also provided to illustrate the theoretical results.

Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multistate and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with that, this article presents a novel charging load extraction method based on residential smart meter data to noninvasively extract REV charging load pattern. The proposed algorithm harnesses the low-frequency characteristics of the charging load pattern and applies a two-stage decomposition technique to extract the characteristics of the charging load. The two-stage decomposition technique mainly includes: the trend component of the charging load being decomposed by seasonal and trend decomposition using loess method, and the low-frequency approximate component being decomposed by discrete wavelet technology. Furthermore, based on the extracted characteristics, event monitoring, and dynamic time warping is applied to estimate the closest charging interval and amplitude. The key features of the proposed algorithm include 1) significant improvement in extraction accuracy; 2) strong noise immunity; 3) online implementation of extraction. Experiments based on ground truth data validate the superiority of the proposed method compared to the existing ones.

Today, the use of smart transportation systems has become widespread due to the change in the structure of cities, technological developments, and the increase in the number of vehicles. It aims at targets such as reducing the damage to people and the environment by increasing traffic safety with smart transportation systems. In addition, one of the big problems of long-distance drivers is falling asleep while driving. This is an event that puts both the driver’s life, the passengers in the vehicle, and the vehicles on the road in danger, and puts the company in a difficult situation if it works for a company. In this direction, the driver sleep alarm project has been developed. This project is a control system based on monitoring the user, measuring, and analyzing their movements. Literature research, which is the first stage of the project, was conducted and research continued throughout the project process. Within the scope of this project, a headset was designed for the driver’s sleep alarm and a mobile application was created. The first prototype of the project was built. First, in the project, the angle values made by the driver's head were produced with the IMU connected to the microcontroller. 10° to 20° margins identified as sleeping state for roll, pitch, and yaw angles. Later, a wearable headset was designed for the driver. Based on these angles produced in the microcontroller, an algorithm has been created that detects whether the driver is asleep. These generated angle values and status information were transferred from the headset to the mobile application via Bluetooth. This data transferred in the mobile application is reflected on the screen. In addition, an algorithm has been created in the mobile application, which sends a notification to the driver when sleep status is detected and sends an informative SMS to the headquarters if the driver is unresponsive to this notification. For the second prototype, it was aimed to produce a PCB for the headset and thus make a new mechanical design. Data mining applications are planned with the collected data from drivers for future works.

ispartof: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE vol:116 status: published

© 2022 IEEE.Deep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal cancer. Digital histology images are adjustable to the application of convolutional neural networks (CNNs) for analysis and classification, due to the sheer size of pixel data present in them. Which can provide a lot of information about colorectal cancer. We used ten different types of pr-trained models with two type method of classification techniques namely (normal classification and k-fold crosse validation) to classify the tumor tissue, we used two different kinds of datasets were these datasets consisting of three classes (normal, low tumors, and high tumors). Among all these eight models of deep transfer learning, the highest accuracy achieved was 96.6% with Darknet53 for 5-Fold and for normal classification the highest results obtained was 98.7% for ResNet50. Moreover, we compared our result with many other papers in stat-of-the-art, the results obtained show clearly the proposed method was outperformed the other papers.

© 2022 IEEE.Businesses are concerned about energy losses. Electronic gadgets have become more prevalent as a result of their adoption. The frequency with which home electricity usage data is collected has grown, allowing for sophisticated data analysis that was previously unavailable. Indeed, adopting Smart Grid (SG) networks, which are freshly improved networks of linked devices, may considerably enhance the existing energy infrastructure\"s dependability, economy, and durability. The SG involves sharing a lot of data, including information on specific users\" power use. And using this information, machine learning and deep learning algorithms may be able to detect power theft users reliably. This paper presented a Convolutional Neural Network (CNN)-based model for automated network-based vulnerability scanning that has excellent classification performance in many categories. Testing from iteration two to four iterations, this study examines research to discover the ideal configuration of the sequential model (SM) for categorization. The method is validated using a two-layer design, including an efficiency of 0.92, the whole first layer is composed of 128 nodes while the second level consists of 64 nodes. This allows for the development of a higher-level classifier for electrical signals, which may be employed in a number of applications. CNN was used to create electrical signal detectors, and SM was used to extract data from an electricity usage dataset. Furthermore, the number of features in the data set can be reduced using the Blue Monkey (BM) approach, and these results are then used to develop high-performance models. In this regard, the focus of this study has been on lowering the amount of needed features in the dataset in order to establish a rising classification algorithm for electrical signals. Experiments have applied the proposed systems\" fantastic performance, with just 666 characteristics required to combine the CNN and BM methods. Comparative to 1035 traits when CNN was used alone. This shows that the CNN and BM models are better than the CNN model in terms of lowering sufficient know while maintaining the same reliability.

© 2022 IEEE.This paper proposes an optimal coordination strategy for electric vehicles and energy storage devices in distribution grids besides the optimal allocation problem of renewable distributed generation (RDGs) and energy storage devices (ESDs). By finding the optimal number, size, and site of the RDGs and ESDs, together with the operation strategy of the ESDs and smart charging of a large number of EVs, the performance of the distribution grids will be improved. An advanced grey wolf optimization (AGWO) algorithm is used to minimize energy losses and voltage violations simultaneously in the test systems. Simulations are tested on IEEE 33 and 69 bus networks to find near-optimal solutions for the optimization problem. Based on the simulation results, the proposed optimization framework reduced the systems\" losses while minimizing the voltage violations by finding the optimal control parameters of the devices.

The extant Fine–Kinney frameworks are insufficient to tackle the risk evaluation problem with Fermatean fuzzy information, in which the prioritization degrees and psychological characteristics of decision-makers are considered. Hence, this study develops a hybrid Fine–Kinney-based occupational risk evaluation framework with an extended Fermatean fuzzy MARCOS method (measurement of alternatives and ranking to Compromise solution). Such a MARCOS method improves conventional MARCOS by integrating Fermatean fuzzy prioritized weighted average operator and prospect theory. This improved method has the capability to handle the occupational risk analysis problem with Fermatean fuzzy data in the risk ranking procedure considering the prioritization degrees and bounded rational behavior of decision-makers. In addition, the Fermatean fuzzy numbers-based risk rating scales are established to transform the linguistic risk scores from decision-makers, it allows for handling uncertain risk rating information from decision-makers more effectively. Further, the improved MARCOS method is incorporated into the occupational risk ranking procedure, as it considers the decision-maker’s prioritization relationships among decision-makers and their reference point effect in occupational risk priority calculation. After that, an occupational risk analysis case for construction operations is selected to test the applicability and validity of the proposed framework. The result indicates that the occupational risk OR6 (Back injury) is the most serious risk with the lowest utility function value (-0.324), and OR7 (Tendinitis) is the least severe risk with the highest utility function value (0.682). Finally, sensitivity exploration and comparative study are implemented to further test the advantages of the developed framework.

Learning from noisy data is a challenging task especially when the system under consideration has a non-stationary nature. The source of the noise is often assumed to be stationary, however the severity or characteristics of noise may also be time-varying, which causes multiple sources of drift in the collected data. This study introduces a novel adaptive learning rate approach to improve learning when the observations from a non-stationary system is altered by an also non-stationary source of noise. As an example to this approach, we propose Persistence Aware Robust Learner (PeARL), an online learning method that utilizes a novel concept called persistence, which is a local noisiness estimation metric to measure the correspondence of a signal to discrete white noise. Making use of this metric, PeARL is able to adaptively adjust the learning rate for each observation during learning to reduce the effect of noise. With this level of control on the learning rate, noisy instances have less disruptive effect on the maintained estimate. We experimentally evaluate PeARL on (a) systematically generated synthetic data and (b) real-world data, including accelerometer readings collected from people (HASC2010corpus) and current measurements from electric motors collected within the scope of EU-funded research project iRel40. The experiments reveal a favorable region of noise rate, in which the proposed method achieves up to 40% reduction in mean absolute error (MAE).