Scheduling Parameters Research Articles

Robust constrained tension control for high-precision roll-to-roll processes

Tension control is critical for maintaining good product quality in most roll-to-roll (R2R) production systems. Previous work has primarily focused on improving the disturbance rejection performance of tension controllers. Here, a robust linear parameter-varying model predictive control (LPV-MPC) scheme is designed to enhance the tension tracking performance of a pilot R2R system for deposition of materials used in flexible thin film applications. The performance of a tension controller may degrade due to disturbances associated with model uncertainties and the slowly-changing dynamics in R2R systems. We introduce a method that separately treats these two sources of disturbance. The controller utilizes an incremental model to eliminate the errors caused by the mismatch between the nominal model and the actual system. A tube-based MPC formulation combined with scheduled parameters adequately updates models and corrects for the time-varying dynamics. Constraints on the rated motor torque are incorporated in the MPC to maintain the controller reliability and avoid machine failures. We illustrate the operation of our control algorithm through simulation of an actual R2R system. The controller outperforms the benchmarks in terms of fast transient response and offset-free tension tracking. It also demonstrates immunity from variations due to parametric uncertainties.

Optimizing the Timeliness of Surgical Therapy in Patients with Breast Cancer Undergoing Immediate Breast Reconstruction.

In breast cancer, coordination of surgical therapy with immediate breast reconstruction (IBR) has been found to significantly delay surgical therapy, which in turn can have an adverse effect on patient survival. The objective of this study was to investigate factors that impact the timeliness of surgical therapy in this setting, which may help to optimize the care of patients with breast cancer. Patients with breast cancer undergoing surgical therapy for breast cancer and immediate reconstruction were reviewed. Patients were divided into two groups: those who underwent surgery ≤ 30 days (group A) and > 30 days (group B) after diagnosis. Multivariate statistical analysis of demographic, disease, surgical, and process of care factors was performed. A total of 348 cases met inclusion criteria, of which 255 (73.2%) were in group A and 93 (26.7%) were in group B. No significant differences were identified in clinical stage, oncologic procedure, or type of reconstruction. On multivariate analysis, an increased likelihood of undergoing surgery ≤ 30 days of diagnosis was observed, with shorter time intervals between surgical oncologist and plastic surgeon consultations [odds ratio (OR) 1.3; 95% confidence interval (CI) 1.1-1.6, p = 0.011]. The number of operating days in common between the surgical oncologist and plastic surgeon nor having the same clinic day impacted timeliness. Patients may undergo both breast conservation surgery and mastectomy with all major types of immediate reconstruction in a timely manner. Early initiation of plastic surgery referrals and surgeon flexibility to work outside the parameters of institutional schedules may help facilitate the timeliness of surgery.

Advantage of Pausing: Parameter Setting for Quantum Annealers

Prior work showed the efficacy of pausing midanneal: such a pause improved the probability of success by orders of magnitude in a class of native problem instances and improved the time to solution in a class of embedded problem instances. A physics-based picture provides qualitative suggestions for where pausing midanneal is effective, for the interplay between annealing schedule parameters and other annealing properties and parameters such as embedding size and strength of the ferromagnetic coupling $|J_F|$, and for the conditions under which pausing can improve the time to solution. Here, through demonstrations on an updated annealing architecture that has higher connectivity than previous annealers, and on multiple embedded problem classes, we are able to confirm various aspects of this picture. We demonstrate the robustness of the optimal pause parameters across platforms and problem classes, explore how to set $|J_F|$ to optimize performance in different scenarios, and provide empirical evidence that short pauses trump longer overall annealing times in time to solution. We also identify the number of different coefficients in a problem as a predictor of problem hardness, and explore its interplay with the optimal $|J_F|$ and embedding size. Based on these results we are able to present qualitative guidelines for parameter setting in quantum annealers.

Design of an LMI-based Polytopic LQR Cruise Controller for an Autonomous Vehicle towards Riding Comfort

In this paper, we present an LMI-based approach for comfort-oriented cruise control of an autonomous vehicle. First, vehicle longitudinal dynamics and a corresponding parameter-dependent state-space representation are explained and discussed. An LMI-based polytopic LQR controller is then designed for the vehicle speed to track the reference value in the presence of noise and disturbances, where the scheduling parameters are functions of the vehicle mass and the speed itself. An appropriate disturbance force compensation term is also included in the designed controller to provide a smoother response. Then we detail how the reference speed is calculated online, using polynomial functions of the given desired comfort level (quantified by the vertical acceleration absorbed by the human body) and of the road type characterized by road roughness. Finally, time-domain simulations illustrate the method’s effectiveness.

An Adaptable and Agnostic Flow Scheduling Approach for Data Center Networks

Cloud applications have reshaped the model of services and infrastructure of the Internet. Search engines, social networks, content delivery and retail and e-commerce sites belong to this group of applications. An important element in the architecture of data centers where these applications run is the communication infrastructure, commonly known as data center networks (DCNs). A critical challenge DCNs have to address is the processing of the traffic of cloud applications, which due to its properties is essentially different to the traffic of other Internet applications. In order to improve the responsiveness and throughput of applications, DCNs should be able to prioritize short flows (a few kB) over long flows (several MB). However, given the time and space variations the traffic presents, the information about flow sizes is not available in advance in order to plan the flow scheduling. There has been a wealth of solutions developed in this space, and prior work includes flow scheduling mechanisms optimizing for a specific workload but fall short when workloads are not known in advance, or comprise a collection of applications changing dynamically. In this paper, we present an adaptable mechanism called Adaptable Workload-Agnostic Flow Scheduling (AWAFS). It is an adaptable approach that can adjust in an agnostic way the scheduling configuration of DCN forwarding devices. This agnostic adjustment contributes to reduce the Flow Completion Time (FCT) of those short flows, representing around 85% of the traffic handled by cloud applications. AWAFS operates by observing the traffic and detecting statistical properties that provide a hint to adapt the scheduling parameters. Our evaluation results based on simulation show that AWAFS can reduce the average FCT of short flows between 16.9% and 45.2% when compared to the best existing agnostic non-adaptable solution, without inducing starvation on long flows. Indeed, it can provide improvements as high as 39% for long flows. Additionally, AWAFS can improve the FCT for short flows in scenarios with high heterogeneity in the traffic present in the network, with a reduction up to 5% for the average FCT and 15% for the tail FCT.

Simultaneous actuator and sensor fault reconstruction of singular delayed linear parameter varying systems in the presence of unknown time varying delays and inexact parameters

SummaryIn this article, robust fault diagnosis of a class of singular delayed linear parameter varying systems is considered. The considered system has delayed dynamics with unknown time varying delays and also it is affected by noise, disturbance and faults in both actuators and sensors. Moreover, in addition to the aforementioned unknown inputs and uncertainty, another source of uncertainty related to inexact measures of the scheduling parameters is present in the system. Making use of the descriptor system approach, sensor faults in the system are added as additional states into the original state vector to obtain an augmented system. Then, by designing a suitable proportional double integral unknown input observer (PDIUIO), the states, actuator, and sensor faults are estimated. The uncertainty due to the mismatch between the inexact parameters that schedule the observer and the real parameters that schedule the original system is formulated with an uncertain system approach. In the PDIUIO, the uncertainty induced by unknown inputs (disturbance, noise and actuator, and sensor faults), unknown delays, and inexact parameter measures are attenuated in H∞ sense with different weights. The constraints regarding the existence and the robust stability of the designed PDIUIO are formulated using linear matrix inequalities. The efficiency of the proposed method is verified using an application example based on an electrical circuit.

A Direct Parameter-Error Codesign Approach of Discrete-Time Saturated LPV Systems

Considering a discrete-time linear parameter-varying systems with input constraints, this article deals with the codesign of a parameter-dependent state-feedback controller and a new parameter-dependent event-triggering mechanism. Two independent event-triggering policies are introduced at the sensor node to economize the limited network resources. They indicate whether the current state or the current scheduling parameters should be transmitted to the controller or not. In this sense, the controller scheduling parameters can differ from those of the system, yielding a certain degree of robustness concerning parameter deviations. Sufficient conditions, given in terms of linear matrix inequalities, ensure the regional asymptotic stability of the closed loop. To formulated them, we use the Lyapunov theory along with the generalized sector condition. Moreover, we formulate a convex optimization procedure indirectly reducing the transmission activity over the network. The proposed methodology effectiveness is attested through numerical examples and comparisons with related works from the literature.

Age of Information-Based Scheduling for Wireless D2D Systems With a Deep Learning Approach

Device-to-device (D2D) links scheduling for avoiding excessive interference is critical to the success of wireless D2D communications. Most of the traditional scheduling schemes only consider the maximum throughput or fairness of the system and do not consider the freshness of information. In this paper, we propose a novel D2D links scheduling scheme to optimize an age of information (AoI) and throughput jointly scheduling problem when D2D links transmit packets under the last-come-first-serve policy with packet-replacement (LCFS-PR). It is motivated by the fact that the maximum throughput scheduling may reduce the activation probability of links with poor channel conditions, which results in terrible AoI performance. Specifically, We derive the expression of the overall average AoI and throughput of the network under the spatio-temporal interfering queue dynamics with the mean-field assumption. Moreover, a neural network structure is proposed to learn the mapping from the geographic location to the optimal scheduling parameters under a stationary randomized policy, where the scheduling decision can be made without estimating the channel state information(CSI) after the neural network is well-trained. To overcome the problem that implicit loss functions cannot be back-propagated, we derive a numerical solution of the gradient. Finally, numerical results reveal that the performance of the deep learning approach is close to that of a local optimal algorithm which has a higher computational complexity. The trade-off curve of AoI and throughput is also obtained, where the AoI tends to infinity when throughput is maximized.

Optimal replacement scheduling for a multi-component system with failure interaction

In this study, we consider a system consisting of n different components with failure interaction. Each component suffers from either minor or major failure: the former is removed via minimal repair, while the latter induces complete failure of the system and requires corrective replacement of the system. This study investigates two scheduling problems of preventive replacement policies for a multi-component system that incorporates minimal repair, shortage, and excess costs. First, we consider the scheduling problem of an age replacement policy, in which the system is replaced at a planned time T or upon the occurrence of any major failure. Next, we consider that the system operates N successive random jobs without interruptions. In this scheduling problem, the system is replaced upon the accomplishment of N jobs or upon the occurrence of any major failure. For each scheduling problem, we derive the optimal scheduling parameter ( T * or N * ) analytically and numerically, according to their existence and uniqueness based on minimizing the mean cost rate function. Finally, a numerical example is designed to validate the theoretical results in this article.

Tube-Based Output Feedback Robust MPC for LPV Systems With Scaled Terminal Constraint Sets.

This article provides a solution to tube-based output feedback robust model predictive control (RMPC) for discrete-time linear parameter varying (LPV) systems with bounded disturbances and noises. The proposed approach synthesizes an offline optimization problem to design a look-up table and an online tube-based output feedback RMPC with tightened constraints and scaled terminal constraint sets. In the offline optimization problem, a sequence of nested robust positively invariant (RPI) sets and robust control invariant (RCI) sets, respectively, for estimation errors and control errors is optimized and stored in the look-up table. In the online optimization problem, real-time control parameters are searched based on the bounds of time-varying estimation error sets. Considering the characteristics of the uncertain scheduling parameter in LPV systems, the online tube-based output feedback RMPC scheme adopts one-step nominal system prediction with scaled terminal constraint sets. The formulated simple and efficient online optimization problem with fewer decision variables and constraints has a lower online computational burden. Recursive feasibility of the optimization problem and robust stability of the controlled LPV system are guaranteed by ensuring that the nominal system converges to the terminal constraint set, and uncertain state trajectories are constrained within robust tubes with the center of the nominal system. A numerical example is given to verify the approach.

Hyperparameter optimization strategies for machine learning-based stochastic energy efficient scheduling in cyber-physical production systems

One of the major challenges faced by system operators in an integrated energy system (IES) is to come up with an optimal schedule of the energy generation units in the presence of uncertainties at both the demand and supply sides. Employing efficient forecasts of the uncertain parameters while ensuring the least possible prediction error is crucial to the overall scheduling of IESs. Long Short-Term Memory (LSTM), a Recurrent Neural Network (RNN) based architecture is widely used in natural language processing and time series forecasting and has reported to be a viable and powerful forecasting technique. The aim of this work is to enhance the autonomy of energy scheduling in cyber-physical production systems (CPPS) through the development of data-driven LSTM based forecast models for the uncertain parameters. However, one of the demanding tasks in time series forecasting methods such as LSTM and the subject that received little attention is the judicious choice/fine-tuning of the hyperparameter values, which significantly impacts the performance of the developed forecasting models. This paper mainly focuses on the performance analysis of various hyperparameter tuning techniques and algorithms used by LSTM networks in forecasting uncertain parameters for stochastic energy scheduling in the context of smart manufacturing. The plant under consideration is the highly energy intensive Model Factory (MF) at the Singapore Institute of Manufacturing Technology (SIMTech), that features an actual production environment supporting both experimentation and learning of digitalisation technologies for Industry 4.0. Three main energy sources installed in an IES framework consisting of solar photo voltaic (PV) system, waste-to-energy (WTE) unit and the main utility grid serves as the energy supply module to meet the requested energy by the industrial facility. Various strategies to tune the LSTM hyperparameters in forecasting the uncertain parameters are examined. Among the four strategies, the first one deals with the traditional manual tuning approach and the second one focuses on automating the traditional manual approach using a FOR loop. While the third one focuses on a tuning strategy using Optuna with a grid search algorithm, the fourth strategy concentrates on adopting Optuna with a Bayesian optimization framework. The overall objective entails generating an optimal day-ahead forecast of the uncertain parameters (in this case solar PV irradiance, temperature, main utility grid energy price and the energy demand of the MF) using LSTM based on the best performing hyperparameter tuning techniques. These forecasts are then utilized to solve a day-ahead stochastic energy scheduling problem that generates an optimal energy dispatch schedule with the overall objectives to minimize the total operational cost and carbon emission.

STUN: Reinforcement-Learning-Based Optimization of Kernel Scheduler Parameters for Static Workload Performance

Modern Linux operating systems are being used in a wide range of fields, from small IoT embedded devices to supercomputers. However, most machines use the default Linux scheduler parameters implemented for general-purpose environments. The problem is that the Linux scheduler cannot utilize the features of the various hardware and software environments, and it is therefore, difficult to achieve optimal performance in the machines. In this paper, we propose STUN, an automatic scheduler optimization framework. STUN modifies the five scheduling policies of the Linux kernel and 10 parameters automatically to optimize for each workload environment. STUN decreases the training time and enhances the efficiency through a filtering mechanism and training reward algorithms. Using STUN, users can optimize the performance of their machines at the OS scheduler level without manual control of the scheduler. STUN showed an execution time and improved FPS of 18.3% and 22.4% on a face detection workload, respectively. In addition, STUN showed 26.97%, 54.42%, and 256.13% performance improvements for microbenchmarks with 4, 44, and 120 cores for each.

Prospects of introducing microgrids in Russian industry

Improving energy efficiency keeps on being one of the most pressing problems for Russian industry. The paper aims to examine the prospects of using microgrids in Russian regions, including in the old industrial ones, to reduce energy costs of industrial enterprises. The methodological basis of the study comprises theoretical aspects of pricing within the models of retail and wholesale energy markets, tenets of uneven demand for energy under the use of microgrids. The authors apply analysis, synthesis, systematisation and statistical observation, create matrices and positioning maps and explore the parameters of energy consumption schedules at industrial enterprises of various types as well as the values of ‘common pot’ electricity transmission tariffs introduced in the subjects of the Russian Federation. The researchers develop own system of indicators for assessing the variability in the cost of electricity transmission services and present a map of Russian regions that illustrate the prospects of using microgrids and mechanisms of demand management in industrially developed regions with a view to cutting energy costs.

PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud

SummaryLoad balancing and task scheduling in cloud have gained a significant attention by many researchers, due to the increased demand of computing resources and services. For this purpose, there are various load balancing methodologies are developed in the existing works, which are mainly focusing on allocating the tasks to Virtual Machines (VMs) based on their priority, order of tasks, and execution time. Still, it facing the major difficulties in finding the best tasks for allocation, because the sequence of patterns are normally used to categorize the relevant tasks with respect to the load. Thus, this research work intends to develop an intelligent group of mechanisms for efficiently allocating the tasks to the VMs by finding the best tasks with respect to the scheduling parameters. Initially, the user tasks are given to the load balancer unit, where the Probabilistic Gray Wolf Optimization (PGWO) technique is used to find the best fitness value for selecting the tasks. Then, the Adaptive Vector Searching (AVS) methodology is utilized to cluster the group of tasks for efficiently allocating the tasks with improved Quality of Service (QoS). Finally, the Recursive Data Acquisition (RDA) based scheduler unit can allocate the clustered tasks to the appropriate VMs in the cloud system by analyzing the properties of storage capacity, balancing load of VM, CPU usage, memory consumption, and execution time of tasks. During evaluation, the performance of the proposed load balancing model is validated by using various measures. Then, the obtained results are compared with some state‐of‐the‐art models for proving the betterment of the proposed scheme.

A reliability based integrated model of maintenance planning with quality control and production decision for improving operational performance

This paper presents an integrated planning between the three core functions of shop floor management; maintenance, production scheduling, and quality. The methodology is based on the conditional reliability of components and its effect on system operation. The objective is to minimize the system operation cost of the combined decision and investigate the cost-effectiveness of the integrated policy over the non-integrated planning. The integrated approach helps the decision-maker to find maintenance actions for individual components, production schedule and sampling parameters for process quality inspection. Mathematical models for integration and a flow chart for the combined methodology of the three functions are presented. Metaheuristic approaches, simulated annealing, and genetic algorithm are used for optimization. A simulation study at different levels of component degradation (service age) and schedule tightness factor is presented. Besides, we present an experimental analysis of varying model parameter values for the integrated planning and compare the results with the independent (non-integrated) planning approach. The results show the improved operational performance of the integrated planning and a lesser operating cost than the non-integrated planning. The proposed approach is dynamic as quality control parameters and production decisions will change as per the maintenance decision.

Energy-Aware Cloud Task Scheduling algorithm in heterogeneous multi-cloud environment

Cloud computing is growing massively in the world of information technology. As a result of which the number of servers and virtual machines is increasing day by day. It leads to huge energy consumption and carbon emission. In this regard, task scheduling has drawn the attention of cloud service providers to reduce energy consumption and increase cloud utilization. This is a crucial issue in a heterogeneous cloud environment. In this paper, energy consumption issues of cloud datacenters are addressed and an Energy-Aware Cloud Task Scheduling (EACTS) algorithm is proposed. It is taking the concept from traditional min-min, max-min, and sufferage heuristics and merge with the energy model. These heuristics are implemented on the heterogeneous cloud environment. EACTS energy model focuses on the estimation of energy consumption in cloud datacenters. The proposed algorithms estimated the makespan, cloud utilization, and energy consumption for a benchmark dataset. The experimental results obtained from EACTS algorithm provides a valid arbitration between energy efficiency and makespan. It has shown the comparison between above mentioned algorithms for various scheduling parameters.

Robust Output Feedback MPC for LPV Systems Using Interval Observers

This article addresses the problem of robust output feedback model predictive control for discrete-time, constrained, linear parameter-varying systems subject to (bounded) state and measurement disturbances. The vector of scheduling parameters is assumed to be an unmeasurable signal taking values in a given compact set. The proposed controller incorporates an interval observer, that uses the available measurement to update the set-membership estimation of the states, and an interval predictor, used in the prediction step of the model predictive control (MPC) algorithm. The resulting MPC scheme offers guarantees on recursive feasibility, constraint satisfaction, and input-to-state stability in the terminal set. Furthermore, this novel algorithm shows low computation complexity and ease of implementation (similar to conventional MPC schemes).

Irrigation water requirements as affected by diverse climate conditions

Determination of crop water requirement is one of the key parameters for precise irrigation scheduling, especially under limited water resources, such as in Egypt. Hence, an accurate estimation of reference evapotranspiration (ETo) is a vital factor for agriculture production, therefore the objectives of the present study were to study the influence of climate change on different seasons and comparing of ETo estimations using Blany- Criddle (BC) and FAO-56 Penman-Monteith (PM) equations under present and future climatic conditions. Data on the present climate have been collected from Wadi El-Natrun meteorological station, Egypt from 1991 to 2020. While the future climate data have been chosen for the concerned RCPs scenarios: RCP2.6, RCP4.5, RCP6.0, and RCP8.5 in 2040, 2060, 2080, and 2100. The results revealed that all months’ reordered Tmean above 20 oC, except December, January, and February where they ranged between 17.68 and 19.44 oC. The highest Tmean values were observed in July (32.3oC), August (31.9 oC), and June (31.8 oC), while February scored the lowest Tmean (17.7 oC).

Linear parameter-varying-based transition flight control design for a tilt-rotor aircraft

This paper presents the development of novel transition flight controllers for a class of urban air mobility aircrafts configured with a fixed-wing and six distributed electric rotor assemblies. Only the two tilt-rotors are utilized for thrust vectoring during transition flight from hovering to steady-level flight, while the four lift-rotors are modulated with aerodynamic lift induced by fixed-wing to maintain stable altitude-hold. Three tractable tilt-rotor articulation profiles are proposed by taking into account of various aircraft and hardware constraints. Given a predefined nominal tilting profile, a family of linear models is obtained by linearizing the nonlinear aircraft model at multiple tilt-rotor angular positions along the tilting profile. Using tilt-rotor angular position as a scheduling parameter, a discrete-time linear parameter-varying model can be constructed, which is then used to develop a novel transition flight control architecture that integrates the adaptive model predictive control law with feedforward effect of the dynamic reference compensation. The simulation results demonstrate the effectiveness of proposed transition flight controllers and its robustness subject to external disturbance.

Modeling and control of a class of urban air mobility tiltrotor aircraft

This paper aims at developing a control-centric analytical formulation for aeromechanics and flight dynamics of urban air mobility (UAM) vehicles. Such vehicles feature a hybrid configuration with tiltrotors and fixed wings, enabling vertical takeoff/landing capability while attaining level flight range and endurance. A comprehensive nonlinear rigid-body dynamic model is developed by incorporating multiple tiltrotor dynamics and their gyroscopic and inertial coupling effects. A quasi-steady aerodynamic formulation is used to derive the aerodynamic loads on all lifting surfaces. In addition to the conventional control surfaces of the fixed-wing aircraft, the tiltrotor angular positions and rotational speeds are also considered additional control inputs in the derivation of nonlinear flight dynamic equations. These nonlinear equations are then linearized with respect to a set of trimmed conditions to generate the corresponding linear time-invariant state-space models, which are best suited for flight control design. In particular, to capture the critical dynamical behaviors during the transition from vertical to forward flight, the linear models are attained at various angular positions of the tiltrotors. These linear models are then utilized to develop a linear parameter-varying (LPV) model in which the tiltrotor angular position is considered the scheduling parameter. Subsequently, the adaptive model predictive control (MPC) methodology is used to design the flight controllers to achieve a stable and smooth transition flight. The numerical simulations demonstrate the efficacy of the proposed modeling and control approach.