Real Machine Research Articles

TimeStitch: Exploiting Slack to Mitigate Decoherence in Quantum Circuits

Quantum systems have the potential to demonstrate significant computational advantage, but current quantum devices suffer from the rapid accumulation of error that prevents the storage of quantum information over extended periods. The unintentional coupling of qubits to their environment and each other adds significant noise to computation, and improved methods to combat decoherence are required to boost the performance of quantum algorithms on real machines. While many existing techniques for mitigating error rely on adding extra gates to the circuit [ 13 , 20 , 56 ], calibrating new gates [ 50 ], or extending a circuit’s runtime [ 32 ], this article’s primary contribution leverages the gates already present in a quantum program without extending circuit duration. We exploit circuit slack for single-qubit gates that occur in idle windows, scheduling the gates such that their timing can counteract some errors. Spin-echo corrections that mitigate decoherence on idling qubits act as inspiration for this work. Theoretical models, however, fail to capture all sources of noise in Noisy Intermediate Scale Quantum devices, making practical solutions necessary that better minimize the impact of unpredictable errors in quantum machines. This article presents TimeStitch: a novel framework that pinpoints the optimum execution schedules for single-qubit gates within quantum circuits. TimeStitch, implemented as a compilation pass, leverages the reversible nature of quantum computation to boost the success of circuits on real quantum machines. Unlike past approaches that apply reversibility properties to improve quantum circuit execution [ 35 ], TimeStitch amplifies fidelity without violating critical path frontiers in either the slack tuning procedures or the final rescheduled circuit. On average, compared to a state-of-the-art baseline, a practically constrained TimeStitch achieves a mean 38% relative improvement in success rates, with a maximum of 106%, while observing bounds on circuit depth. When unconstrained by depth criteria, TimeStitch produces a mean relative fidelity increase of 50% with a maximum of 256%. Finally, when TimeStitch intelligently leverages periodic dynamical decoupling within its scheduling framework, a mean 64% improvement is observed over the baseline, relatively outperforming stand-alone dynamical decoupling by 19%, with a maximum of 287%.

In-situ or side-by-side? A user study on augmented reality maintenance instructions in blind areas

In recent years, Augmented Reality (AR) has been effectively proposed as a tool to support workers in manual procedural tasks in industry, such as assembly and maintenance. It is very common for workers to deal with complete equipment in maintenance. Then, instructions may refer to components located in blind areas, i.e., visually occluded by part of the equipment. Displaying in-situ AR instructions in such blind areas requires handling occlusions; otherwise, side-by-side instructions can be exploited. In the literature, it is still unclear which solution better leverage workers’ performance in maintenance tasks accomplished in blind areas. Thus, we designed three AR presentation modes to convey maintenance instructions in a real machine: 3D in-situ rendered with X-ray technique; 3D side-by-side on a CAD model replicating the blind area; 2D side-by-side on a virtual mirror. We conducted a user study comparing these three presentation modes with a 2D drawing extracted from the original maintenance documentation of the machine. The performance of 42 participants was evaluated in terms of completion time, accuracy, and cognitive load. The results revealed that both in-situ and 3D side-by-side presentation modes perform better than 2D drawing. Specifically, the in-situ presentation mode outperforms the 3D side-by-side mode in terms of completion time. The side-by-side virtual mirror does not improve performance with respect to 2D drawing, then it needs to be redesigned for effective use in AR maintenance interfaces.

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics.

This systematic review aims to study and classify machine learning models that predict pandemics’ evolution within affected regions or countries. The advantage of this systematic review is that it allows the health authorities to decide what prediction model fits best depending upon the region’s criticality and optimize hospitals’ approaches to preparing and anticipating patient care. We searched ACM Digital Library, Biomed Central, BioRxiv+MedRxiv, BMJ, Computers and Applied Sciences, IEEEXplore, JMIR Medical Informatics, Medline Daily Updates, Nature, Oxford Academic, PubMed, Sage Online, ScienceDirect, Scopus, SpringerLink, Web of Science, and Wiley Online Library between 1 January 2020 and 31 July 2022. We divided the interventions into similarities between cumulative COVID-19 real cases and machine learning prediction models’ ability to track pandemics trending. We included 45 studies that rated low to high risk of bias. The standardized mean differences (SMD) for the two groups were 0.18, 95% CI, with interval of [0.01, 0.35], I{^2}=0, and p value=0.04. We built a taxonomic analysis of the included studies and determined two domains: pandemics trending prediction models and geolocation tracking models. We performed the meta-analysis and data synthesis and got low publication bias because of missing results. The level of certainty varied from very low to high. By submitting the 45 studies on the risk of bias, the levels of certainty, the summary of findings, and the statistical analysis via the forest and funnel plots assessments, we could determine the satisfactory statistical significance homogeneity across the included studies to simulate the progress of the pandemics and help the healthcare authorities to take preventive decisions.

Machine-Learning-Based Approach for Virtual Machine Allocation and Migration

Due to its ability to supply reliable, robust and scalable computational power, cloud computing is becoming increasingly popular in industry, government, and academia. High-speed networks connect both virtual and real machines in cloud computing data centres. The system’s dynamic provisioning environment depends on the requirements of end-user computer resources. Hence, the operational costs of a particular data center are relatively high. To meet service level agreements (SLAs), it is essential to assign an appropriate maximum number of resources. Virtualization is a fundamental technology used in cloud computing. It assists cloud providers to manage data centre resources effectively, and, hence, improves resource usage by creating several virtualmachine (VM) instances. Furthermore, VMs can be dynamically integrated into a few physical nodes based on current resource requirements using live migration, while meeting SLAs. As a result, unoptimised and inefficient VM consolidation can reduce performance when an application is exposed to varying workloads. This paper introduces a new machine-learning-based approach for dynamically integrating VMs based on adaptive predictions of usage thresholds to achieve acceptable service level agreement (SLAs) standards. Dynamic data was generated during runtime to validate the efficiency of the proposed technique compared with other machine learning algorithms.

Neural networks and NARXs to replicate extrusion simulation in digital twins for fused filament fabrication

In this work we propose the use of nonlinear autoregressive models with exogenous variables (NARXs), powered by dynamic recurrent neural networks, to replicate the results of a previously developed, complex simulation of the extrusion process in fused filament fabrication. The NARXs predict extrusion rate of the extrudate and compression force acting on the filament with an average discrepancy of 0.12 % with respect to the original simulation, but at a fraction of the computational time (0.1 s of the NARX vs. 600 s needed by the original simulation to process the same one-minute time interval). In addition to illustrating how NARXs can be created to mimic an existing simulation, in this work we show how the NARXs can be physically connected to the sensors of a real FFF machine, thus creating an effective digital twin of the extrusion process, useful to support real-time decision making by an AI machine controller. Finally, we show how the implemented digital twin can be used for in-process monitoring, bringing as example the automated detection of an extrusion clogging event.

Fast two‐vector‐based model‐free predictive current control of permanent magnet synchronous motor

Finite control set model predictive control (FCS-MPC) is becoming an important control method in the high-performance control of permanent magnet synchronous motor (PMSM) because of its simple concept, easy implementation, and strong performance. However, the traditional FCS-MPC uses only one basic voltage vector in the whole control cycle, so its steady-state control performance is poor. In addition, FCS-MPC relies significantly on machine parameter correctness. The performance is significantly degraded if the controller parameters do not match the real machine parameters. This study proposed a PMSM fast two-vector model-free predictive control approach to address the abovementioned issues. The process employs no motor parameters and relies only on the system's inputs and outputs. In addition, this method combines the fast vector selection with the two-vector control method, which simplifies the computational effort while improving the steady-state control accuracy at the same time. MATLAB/Simulink built the control system model, the simulation and experimental studies were carried out. The simulation and experimental results confirm the effectiveness of this method.

Thermal error modeling of machine tool based on dimensional error of machined parts in automatic production line

Thermally induced error has proven to be the major source of machining error for the machine tool working in a non-temperature-controlled workshop. Current research on thermal error modeling and compensation is implemented based on the measured thermal deformation of machine tool, and the data is obtained referring to spindle idling rather than metal cutting condition, resulting in the disadvantages of the established model with low adaptability to varying conditions. In this paper, a modeling and compensation method based on the dimensional error of the machined parts is proposed to address the issue and verified through machine tools in an automatic production line. Compared with the modeling based on thermal deformation, the method formulates a more direct relations between temperature rise and machining error. The thermal error modeling is carried out by measuring the dimension deviation of the inner hole diameter of the motor end cover and the screened representative temperature variables. Meantime, considering the temperature coefficient in model is difficult to converge when modeling with the single-day data, the unified modeling with the multi-day data is realized by improving the conventional multiple linear regression model. Finally, the generalized model used for thermal error compensation in x direction of the machine tool is obtained. The real-time compensation of the thermal error based on the established model is realized with the machine tool machining in mass production. The verification results show that this error modeling and compensation method can reduce the machining error of the end cover by more than 52%, irrespective of experiencing various complicated working conditions. Stability and robustness of the modeling are also validated through application on the other machine tool with the same configuration and real machining over seven days.

Nonlinear Model Learning for Compensation and Feedforward Control of Real-World Hydraulic Actuators Using Gaussian Processes

This paper presents a robust machine learning framework for modeling and control of hydraulic actuators. We identify several important challenges concerning learning accurate models of the dynamics for real machines, including noise and uncertainty in state measurements, nonlinear effects, input delays, and data-efficiency. In particular, we propose a dual-Gaussian process (GP) model architecture to learn a surrogate dynamics model of the actuator, and showcase the accuracy of predictions against the piecewise and neural network models that have been widely used in the literature. In addition, we provide robust techniques for learning neural network inverse models and controllers by batch GP inference in an automated, seamless and computationally fast manner. Finally, we demonstrate the performance of the trained controllers in real-world feedforward and tracking control applications.

Soil-Adaptive Excavation Using Reinforcement Learning

In this letter, we present an excavation controller for a full-sized hydraulic excavator that can adapt online to different soil characteristics. Soil properties are hard to predict and can vary even within one scoop, which requires a controller that can adapt online to the encountered soil conditions. The objective is to fill the bucket with excavation material while respecting machine limitations to prevent stalling or lifting of the machine. To this end, we train a control policy in simulation using Reinforcement Learning (RL). The soil interactions are modeled based on the Fundamental Equation of Earth-Moving (FEE) with heavily randomized soil parameters to expose the agent to a wide range of different conditions. The agent learns to output joint velocity commands, which can be directly applied to the standard proportional valves of the real machine. We test the controller on a 12-ton excavator in different types of soils. The experiments demonstrate that the controller can adapt online to changing conditions without the explicit knowledge of the soil parameters, solely from proprioceptive observations, which are easily measurable.

APPLICATION OF PREDICTIVE MAINTENANCE IN THE PACKAGING PRODUCTION

To solve the problem of predictive maintenance for packaging manufacturing, we propose a hybrid model that optimizes the maintenance plan. The model is based on monitoring the state of many components of a multi-position automatic packaging machine and makes it possible to predict their future malfunctions and estimate the remaining service life of the equipment. The effectiveness of the proposed solution is demonstrated with the help of a real industrial multi-position machine for the automatic production of film bags and packaging of paste in them. The methodology is based on the analysis of diagnostic information using an expert system.

Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing

Cloud computing is frequently alluded to as a model that furnishes boundless information handling conveniences with compensation for each utilization framework. Present-day cloud foundations resources as virtual machines (VMs) to actual machines utilizing virtualization innovation. All VMs works their involved structure and exhaust resources from their actual machine which behaves like a host. For load adjusting, Cloud moves VMs from exceptionally troubled real machines to low troubled actual machines. The delay of this calculation expansions in the organization as virtual machines are relocated. This work puts forward a new algorithm, namely o for VM migration. The proposed optimization algorithm has been implemented in MATLAB software. A comparative analysis is performed between the outcomes of the preceding and the new algorithm. The proposed algorithm has been evaluated over three performance parameters including delay, bandwidth used and space used.

Configuration-aware model predictive motion planning for Tractor–Trailer Mobile Robot

ABSTRACT This study proposes a motion planning method with strict obstacle avoidance constraints among polygonal-shaped objects for a tractor–trailer mobile robot. The conventional method generally expresses collision-free constraints by approximating a robot as one circle or several circles or ellipsoids. However, accomplishing moving tasks with minimum margins is desirable, with improved efficiency of space availability when multiple robots operate in one area such as a factory, restaurant, office, or warehouse. The target of this study is a tractor–trailer mobile robot that can transport more goods efficiently, while significantly reducing margins during movement. To control the target, multiple convex polygonal objects expressing the mobile robot and surroundings are transformed into linear simultaneous inequalities using ‘Farkas’ lemma.' Embedding inequalities into model predictive control yields a motion planner that allows tractor–trailer mobile robot movement and possesses non-holonomic constraints. The proposed model is validated through simulation and real machine experiments.

Leak detection in water distribution systems by classifying vibration signals

In addition to the issues of water quality and economic losses, leakages in pipe networks are a potential danger to public safety. Early detection of water leaks in pipe networks is crucial for implementing countermeasures to reduce structural damage and water losses. The method of data collection based on sensors installed in pipeline networks has gained a lot of attention due to its potential for application in real-time monitoring systems for leak detection. However, many models developed on data collected from simulations, engineered tests or field experiments, and few have been validated using real network data. To make further investigations in real water distribution systems (WDSs), machine learning (ML)-based leak detection models were developed and validated using signal data collected by piezoelectric accelerometers installed in WDSs over several cities of China. Nonlinear features describing the non-stationary characteristics contained in leak signals are extracted from the signals to build feature sets as inputs to the common classifiers, i.e., support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). In addition, the spectrograms obtained by the short-time Fourier transform processing are used for the convolutional neural network SqueezeNet. The effects of different features, different dataset compositions, and different models for leak detection are compared and analyzed. SqueezeNet has the best performance with 95.15% accuracy in identifying leaks, and KNN is the best of the three classifiers with superior sensitivity and 88.17% accuracy in identification. This paper demonstrates the effectiveness and practicability of using machine learning models to identify leaks in real pipe networks. The application of machine learning models can perform effective leak detection with minimal human intervention, thus providing a meaningful contribution to the study of leak detection in real WDSs. The proposed model aims to identify leakages under operational conditions in real-life pipe networks with higher efficiency and reduced manual involvement. This paper demonstrates the applicability of accelerometers based on microelectromechanical systems for leak detection and establishes real network-based machine learning models; thereby, providing meaningful contribution to the literature regarding leak detection in a real WDS.

Mode shape database-based estimation for machine tool dynamics

The system dynamics of machine tools plays an important role in machine health monitoring and quality assessment of manufacturing products. For former identification methods, the system dynamics are achieved with premises that do not fit with the real cutting conditions, as experimental modal analysis is realized only in idle status, and operational modal analysis limits the excitation form for vibration. Meanwhile, the dynamics variation during the machining is viewed discretely with no proper handles for the changes caused by cutting position movements. In this study, a mode shape database-based TOMA (MSDB-TOMA) estimation method is proposed for the system dynamics of machine tools under variation caused by component movements. The establishment principle of a mode shape database for capturing dynamics variation influenced by movements of cutting positions is proposed, with good fitting performance proved in a finite element model (FEM) of a machine tool. After that, the performance of dynamics estimation with the proposed method is validated using matched mode shapes from the pre-built database. The estimation for system dynamics parameters, e.g., natural frequency, presents a maximum error of within 0.04%, which turns out to be precise when compared with the real value from modal analysis in simulation. The feasibility experiment on FEM for estimating dynamics variation with component movements indicates the possibility of in-process dynamics recognition for real machining using the proposed method.

Towards narrowing the reality gap in electromechanical systems: error modeling in virtual commissioning

Digital factories and smart manufacturing systems have been increasingly researched and multiple concepts were developed to cope with prevailing ever-shortening life-cycles. The ubiquitous digital twin, despite many definitions, is often praised for accurate virtual models. One key idea to improve manufacturing through such virtual models is virtual commissioning (VC), aiming at early machine code validation. VC and its virtual models are still lacking behind their real counterparts. This gap between reality and its virtual model, commonly termed reality gap, increases the complexity of creating cyber-physical systems. An especially stark contrast is visible between the idealized virtual model and a real machine encountering errors. While error simulations exist in other fields of research, a thorough investigation in VC is missing. Thus, this paper addresses the task of narrowing the reality gap in VC based on two steps. First, a comprehensive body of research of possible errors encountered in virtual commissioning is analyzed. Secondly, the feasibility of error implementation is discussed. This paper lays the foundation for narrowing the reality gap and enabling test automation and digital twin-based control.

Orthogonal Optimization Design of the Compound Impeller for a New Type of Dishwasher Pump

A new type of dishwasher pump without a pipeline system was invented for the first time to form an original sink-type dishwasher avoiding the accumulation of dirt in the hydraulic system. In order to improve the hydraulic performance of this new type of dishwasher pump with twin-volute passive rotation driven by a compound impeller, an orthogonal optimal design method was first introduced to optimize the compound impeller composed of axial cascades and radial blades. Six geometric parameters were selected as the main factors to design the orthogonal table L18(36). All impeller models were manufactured via high-precision 3D printing and tested to determine the optimal solution. Firstly, the hydraulic performance was measured using a specially designed test bench under the assumption of static-volute conditions. Then, the optimization scheme was verified in real machine experiments under the rotating-volute condition, and numerical calculations were used to assess the performance improvement due to optimization under the static- and rotating-volute conditions. The results show that the main factors affecting the pump head and efficiency are impeller outlet diameter D2 and profile line L. The optimized pump head and efficiency are significantly higher than those of the original scheme. Furthermore, the entropy production of each flow passage component of this new type of dishwasher pump is significantly reduced after optimization. Under the static-volute condition, the scheme obtained using the range analysis displays an increase of 0.3 m in the pump head and 2.99% in pump efficiency. Under the rotating-volute condition, the optimization scheme is also effective (pump head increased by 0.54 m, and pump efficiency by 7.46%). At the same time, the passive rotation speed of the twin-volute increases from 60 rpm to 70 rpm, verifying that both the optimization method is reliable and can be used when developing and optimizing dishwasher pumps.

Experimental Investigation of Flow Inside the Back-Sidewall Gaps of Centrifugal Pump and its Impact on Axial Thrust

Despite the sidewall gaps of centrifugal pumps are tiny narrow spaces, the flow inside them can have a great impact on the flow field in the whole machine. Regardless of its influence, there is no full understanding of the flow in those regions. A theory exists for the simplified cylindrical cavity with a rotating disk, whose application to real hydraulic machines is questionable. To investigate the flow in the sidewall gaps of centrifugal pumps, a test rig including a real impeller has been built up. It enables observation of the flow in the back-sidewall gap by optical methods and evaluation of axial thrust and torque. The measurements were performed for a range of rotational speeds in order to map possible flow regimes and patterns. It was found out, that the basic theory and map of the flow regimes obtained for a simple rotating cavity is inapplicable for the back-sidewall gaps of real hydraulic machines. The flow field is significantly influenced by the impeller blades, and consequently, the map of the regimes is shifted compared to the one derived for the simple cylindrical cavity with a rotating disk. The discrepancies were described and a new map of flow regimes was introduced.

A Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing

Cloud computing is frequently alluded to as a model that furnishes boundless information handling conveniences with a compensation for each utilization framework. Present day cloud foundations resources as virtual machines (VMs) to actual machines utilizing virtualization innovation. All VMs works their involved structure and exhaust resources from their actual machine which behaves like a host. For load adjusting, Cloud moves VMs from exceptionally troubled real machines to low troubled actual machines. The delay of this calculation expansions in the organization as virtual machines are relocated. This work puts forward a new algorithm, namely Butterfly optimization for VM migration. The proposed optimization algorithm has been implemented in the MATLAB software. A comparative analysis is performed between the outcomes of the preceding and the new algorithm. The proposed algorithm has been evaluated over three performance parameters including delay, bandwidth used, and space used.

Do pupillary responses during authentic slot machine use reflect arousal or screen luminance fluctuations? A proof-of-concept study.

Modern slot machines are among the more harmful forms of gambling. Psychophysiological measures may provide a window into mental processes that underpin these harms. Here we investigated pupil dilation derived from eye tracking as a means of capturing changes in sympathetic nervous system arousal following outcomes on a real slot machine. We hypothesized that positively reinforcing slot machine outcomes would be associated with increases in arousal, reflected in larger pupil diameter. We further examined the contribution of game luminance fluctuations on pupil diameter. In Experiment 1A, experienced slot machine gamblers (N = 53) played a commercially-available slot machine in a laboratory for 20 minutes while wearing mobile eye tracking glasses. Analyses differentiated loss outcomes, wins, losses-disguised-as-wins, and (free-spin) bonus features. Bonus features were associated with rapid increases in pupil diameter following the onset of outcome-related audiovisual feedback, relative to losses. In Experiment 1B, luminance data were extracted from captured screen videos (derived from Experiment 1A) to characterize on-screen luminance changes that could modulate pupil diameter. Bonus features and wins were associated with pronounced and complex fluctuations in screen luminance (≈50 L and ≈25L, respectively). However, the pupil dilation that was observed to bonus features in Experiment 1A coincided temporally with only negligible changes in screen luminance, providing partial evidence that the pupil dilation to bonus features may be due to arousal. In Experiment 2, 12 participants viewed pairs of stimuli (scrambled slot machine images) at luminance difference thresholds of ≈25L, ≈50L, and ≈100L. Scrambled images presented at luminance differences of ≈25L and greater were sufficient to cause pupillary responses. Overall, pupillometry may detect event-related changes in sympathetic nervous system arousal following gambling outcomes, but researchers must pay careful attention to substantial in-game luminance changes that may confound arousal-based interpretations.

Experimental Analysis of Circular Milling for Material Identification in Aerospace Industry

In the airplane design, thousand holes should be manufactured by machining leading this process in the first place for process optimization. Recently, the stack materials used in this application are sheet layers of Titanium alloy, Aluminum alloy and CFRP, which normally are machined under different cutting conditions. There are two main solutions when drilling stacks: using the more conservative ones or changing the parameters for each layer. In orbital drilling the forces are lower, with better quality but with longer machining time, compared to drilling, allowing the measurement and control of the process. Smart machining techniques can be applied for recognising and adapting the cutting parameters in real time, depending on a database for decision-making. In this paper, a technique is proposed to identify the cutting material based on the cutting and feed components of the machining force in a circular milling process. Experiments consist of machining separately Titanium and Aluminium alloy workpieces in a similar range of cutting speed and feed, measuring forces and the position of the cutting tool (X,Y, SP) using the machine-tool data. The results show that it is possible to identify the materials using the calculated tangential and radial force components in the tool referential frame because the values of specific cutting and feed force are significantly different for each material. An specific force map is used as a signature to distinguish the materials in real time machining which can be used for orbital drilling in the next step of the research.