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Material flow control in remanufacturing is an important issue in the field of disassembly. This paper deals with the potential of autonomous material release decisions for remanufacturing systems to balance the uncertainties related to changing bottlenecks, to maximise throughput ($$TH$$ TH ) and to minimise work-in-process ($$WIP$$ WIP ). The goal is to achieve the highest possible throughput rate using real-time data while keeping costs to a minimum. Unlike traditional production systems, remanufacturing must consider and handle high uncertainties in the process. Up to now, classical methods such as CONWIP, Material Requirement Planning (MRP) and Kanban have been used for material flow control. However, these methods do not perform well in a system with high variation and uncertainties such as remanufacturing as they aim to find solutions for static environments. Crucial for optimal production in stochastic environments is finding the optimum pull or release rate which can vary over time in terms of maximising $$TH$$ TH and minimising $$WIP$$ WIP . We propose a deep reinforcement learning approach that acts on the environment and can adapt to changing conditions. This ensures that changing bottlenecks are taken care of and that there is a minimum $$WIP$$ WIP in the system.

We consider a wireless control system where sensors transmit their measurements to a controller over a fading channel. We assume that each sensor is equipped with a rechargeable battery and can harvest energy from the environment or other energy sources. To predict the harvested energy, we consider a stationary Markovian model conditioned on a scenario and estimate the unknown parameters based on empirical measurements. The transmission energy is assumed to be inversely proportional to the communication channel gain, which is described by a compound distribution. To increase energy efficiency, we use a continuous event-trigger allowing the sensors to transmit new data only when certain conditions are satisfied. Finally, we analyze the exponential stability of the closed-loop nonlinear system with multiple sector-bounded nonlinearities based on the input-delay method and Lyapunov–Krasovskii technique.

In this paper we study the problem of how quantization may affect the maximum likelihood estimation of the parameters of a probability density function representing a compound distribution. We consider and compare three different approaches to design a variable quantizer allowing to guarantee a predefined loss of Fisher information which is used as a measure of the information loss due to quantization. We also propose the approximations which characterize the asymptotic behavior of the loss allowing a significant reduction of the computational complexity.

This paper proposes an engineering-oriented framework that casts the problem of learning as an automatic control problem, and that can ultimately be used to design education activities that autonomously adapt to individual students’ abilities, prerequisites, learning goals and other restrictions. The framework leverages on quantitative descriptions of knowledge flows within university programs in terms of Knowledge Components Matrices (KCMs) and Knowledge Flow Graphs (KFGs), that serve as the basis for developing the aforementioned automated approach to personalized education. Essentially, the manuscript proposes to: 1) combine these descriptions with results from exams and assessments to statistically estimate the learning status of a student; 2) combine these descriptions with data-driven approaches to derive models of how knowledge ladders logically and in time; 3) use these two ingredients to automatically design suitable and personalized study activities for a student, given his/her current knowledge status and desired learning outcome. We describe all steps (modelling of the knowledge flows, estimating the current learning status, and derivation of suitable learning activities to close the loop) with formal and control-oriented notation. The paper serves thus the purpose of showing how methods from the field of system theory and control engineering are naturally useful for the implementation of quantitative-based personalized education.

We consider the problem of calibrating range measurements of a lidar sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that &ldquo, fixed features shall have fixed relative distances and angles&rdquo, The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.

This paper considers the problem of cyber attacks onto industrial plants and proposes to use a digital twin to detect and localize such attacks. The digital twin consists of a representation of the nominal plant behavior, i.e., not under attack, for which differential-algebraic equation systems or discrete state models may be used. By simulating the nominal behavior of the system online, i.e., in parallel to the process, and continuously comparing the simulated behavior to the measured values allows to detect attacks. Their localization is facilitated through a root-cause analysis, which is also based on the model description of the plant. The concept has been implemented and tested on a small scale industrial prototype.

Wireless sensors and networks are used only occasionally in current control loops in the process industry. With rapid developments in embedded and highperformance computing, wireless communication, and cloud technology, drastic changes in the architecture and operation of industrial automation systems seem more likely than ever. These changes are driven by ever-growing demands on production quality and flexibility. However, as discussed in "Summary," there are several research obstacles to overcome. The radio communication environment in the process industry is often troublesome, as the environment is frequently cluttered with large metal objects, moving machines and vehicles, and processes emitting radio disturbances [1], [2]. The successful deployment of a wireless control system in such an environment requires careful design of communication links and network protocols as well as robust and reconfigurable control algorithms.

We investigate the optimal power control for multisensor estimation of correlated random Gaussian sources. A group of wireless sensors obtains local measurements and transmits them to a remote fusion center (FC), which reconstructs the measurements using the minimum mean-square error estimator. All the sensors are equipped with an energy harvesting module and a transceiver unit for wireless, directed energy sharing between neighboring sensors. The sensor batteries are of finite storage capacity and prone to energy leakage. Our aim is to find optimal power control strategies, which determine the energies used to transmit data to the FC and shared between sensors, so as to minimize the long-term average distortion over an infinite horizon. We assume centralized causal information of the harvested energies and channel gains, which are generated by independent finite-state stationary Markov chains. The optimal power control policy is derived using a stochastic predictive control formulation. We also investigate the structure of the optimal solution, a Q-learning based suboptimal power control scheme and two computationally simple and easy-to-implement heuristic policies. Extensive numerical simulations illustrate the performance of the considered policies.

This letter considers the problem of how uniform quantization affects the maximum likelihood estimation of the parameters of a probability density function representing a compound distribution. As a measure of the information loss due to quantization, the loss of Fisher information is used. The main contribution of this letter is the approximation which characterizes the asymptotic behavior of the loss allowing a significant reduction of the computational complexity. We further investigate how to choose the quantization interval to guarantee a predefined loss of Fisher information. An extensive numerical simulation demonstrates the efficiency of the approximation.

We consider the problem of control and remote state estimation with battery constraints and energy harvesting at the sensor (transmitter) under DoS/jamming attacks. We derive the optimal non-causal energy allocation policy that depends on current properties of the channel and on future energy usage. The performance of this policy is analyzed under jamming attacks on the wireless channel, in which the assumed and the true channel gains differ, and we show that the resulting control cost is not monotonic with respect to the assumed channel gain used in the transmission policy. Additionally, we show that, in case there exists a stabilizing policy, then the optimal causal policy ensures stability of the estimation process. The results were illustrated for non-causal and causal energy allocation policies under different jamming attacks.

This paper proposes individualized, dynamical and data-driven models that describe pelvic floor muscle responses in women that use vaginal dilation. Specifically, the models describe how the volume of an inflatable balloon inserted at the vaginal introitus dynamically affects the aggregated pressure exerted by the pelvic floor muscles of the person. The paper inspects the approximation capabilities of different model structures, such as Hammerstein-Wiener and NARX, for this specific application, and finds the specific model structures and orders that best describe the recorded measurement data. Hence, although the current dataset is drawn from a sample of healthy volunteers, this paper is an initial step towards better understanding women’s responses to vaginal dilation and facilitating individualised medical vaginal dilation treatment.

This Research-to-Practice Full Paper investigates the emerging perspectives for the 21st engineering curriculum, and discusses the crucial role that digital technologies will have in facilitating the management, evaluation, and development of such a curriculum. First, a vision for future engineering curricula is distilled from modern curricular perspectives and trends of future engineering professions, where the integration of non-cognitive competences and the increased individualization of study paths are central. Core requirements for future curricula are outlined, which pose significant barriers to curricular changes. Then, the role of technology in mitigating these barriers is discussed, by outlining key aspects of a data-driven digital approach to the management of future curricula. To illustrate the proposed approach, the paper presents a case example of a digital tool that analyzes curriculum coherency at the content level. The paper concludes with a discussion of future research directions regarding the conceptualization and management of future engineering curricula through digital technologies.

In this paper, we propose a method to analyse the coherence of existing curricula at higher education institution. We focus our attention to engineering programmes at universities but the proposed method is by no means restricted to those cases. In contrast to other known methods, our approach is quantitative, decentralised, and asynchronous and allows to analyse entire programmes (in contrast to single courses) and does not depend on using specific teaching methods or tools. We propose to perform this quantitative assessment in two steps: first, representing the university programme as an opportune graph with courses and concepts as nodes and connections between courses and concepts as edges; second, analysing the structure of the programme using methods from graph theory. We thus perform two investigations, both leveraging a practical case – data collected from three engineering programmes at two Swedish universities: (a) how to represent university programmes in terms of graphs (here called concepts-courses graph (CCG)) and (b) how to reinterpret the most classical graph-theoretical node centrality indexes and connectivity and network flow results in order to analyse the programme structure, including to discover flows and mismatches. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

In this paper, we consider a problem of decentralized non-Bayesian quickest change detection using a wireless sensor network where the sensor nodes are powered by harvested energy from the environment. The underlying random process being monitored by the sensors is subject to change in its distribution at an unknown but deterministic time point, and the sensors take samples (sensing) periodically, compute the likelihood ratio based on the distributions before and after the change, quantize it and send it to a remote fusion centre (FC) over fading channels for performing a sequential test to detect the change. Due to the unpredictable and intermittent nature of harvested energy arrivals, the sensors need to decide whether they want to sense, and at what rate they want to quantize their information before sending them to the FC, since higher quantization rates result in higher accuracy and better detection performance, at the cost of higher energy consumption. We formulate an optimal sensing and quantization rate allocation problem (in order to minimize the expected detection delay subject to false alarm rate constraint) based on the availability (at the FC) of non-causal and causal information of sensors’ energy state information, and channel state information between the sensors and the FC. Motivated by the asymptotically inverse relationship between the expected detection delay (under a vanishingly small probability of false alarm) and the Kullback-Leibler (KL) divergence measure at the FC, we maximize an expected sum of the KL divergence measure over a finite horizon to obtain the optimal sensing and quantization rate allocation policy, subject to energy causality constraints at each sensor. The optimal solution is obtained using a typical dynamic programming based technique, and based on the optimal quantization rate, the optimal quantization thresholds are found by maximizing the KL information measure per slot. We also provide suboptimal threshold design policies using uniform quantization and an asymptotically optimal quantization policy for higher number of quantization bits. We provide an asymptotic approximation for the loss due to quantization of the KL measure, and also consider an alternative optimization problem with minimizing the expected sum of the inverse the KL divergence measure as the cost per time slot. Numerical results are provided comparing the various optimal and suboptimal quantization strategies for both optimization problem formulations, illustrating the comparative performance of these strategies at different regimes of quantization rates.

We describe COnCUR, a tool to visualize the structure of a university program in terms of the courses and knowledge components included in the program, the connections between them as well as detecting, analyzing and visualizing inconsistencies within the program structure using graph theoretical concepts. The tool is especially designed for those who are involved in the planning of study programs, since enabling to create maps linking the various content and levels of abilities in the various courses (and thus to evaluate if the prerequisite of a student are compatible with a course, especially in the case of personalized programs).

This paper studies how to describe, using a piecewise linear dynamical model, the short-term effects of fatigue and recovery on the strength of pelvic floor muscles. Specifically, we first adapt a known model that describes short-term fatigue in skeletal muscles to the specific problem of describing fatigue in pelvic floor muscles when performing Kegel exercises, and then propose a strategy to learn the modelSs parameters from field data.In details, we estimate the model parameters using a least squares approach starting from measurement data that has been obtained from three healthy women using a dedicated vaginal pressure sensor array and a connected mobile app which gamifies the Kegel exercising experience.We show that describing the pelvic floor muscles behaviour in terms of short-term fatigue and recovery factors plus learning the associated parameters from data from healthy women leads to the possibility of precisely forecasting how much pressure the players will exert while playing the game.By cross-learning and cross-testing individual models from the three volunteers we also discover that the models need to be individualized: indeed, the numerical results indicate that, generically, using data from one player to model another leads to potentially drastically lower forecasting capabilities.

Women affected by pain during penetrative sexual intercourse are often treated using fixed-size vaginal dilators that are regularly perceived as uncomfortable and leading to premature treatment drop-outs. These dilators could be improved by making them adaptive, i.e., able to exert dynamically different pressures on the vaginal duct to simultaneously guarantee comfort levels and achieve the medical dilation objectives. Implementing feedback control would then benefit from models that connect the patients' comfort levels with their experienced physiological stimuli. Here we address the problem of data-driven quantitative modeling of pain/pleasure self-assessments obtained through medical trials. More precisely, we consider time-series records of pelvic floor muscles pressure, vaginal dilation, and pain/pleasure evaluations, and model the relations among these quantities using statistical analysis tools. Besides this, we also address the important issue of the individualization of these models: different persons may respond differently, but these variations may sometimes be so small that it may be beneficial to learn from several individuals simultaneously. We here numerically validate the previous claim by verifying that clustering patients in groups may lead, from a data-driven point of view, to models with a significantly improved statistical performance.

This letter investigates networks of interconnected systems and introduces the notion of “scalable input-to-state stability” (sISS). This concept is based on input-to-state stability (ISS) and can be interpreted as an extension of the well-known concept of string stability from simple line graphs to general graphs. It guarantees that the trajectories of all states are bounded at all times independently of the network’s size and structure and can hence be regarded as an important performance notion. Further, sufficient conditions are derived to guarantee sISS of homogeneous networks with well-defined interconnection structures. In fact, the conditions depend on local ISS Lyapunov functions but guarantee the global condition of sISS. Hence, a first step is made towards developing suitable extensions of string stability to general networks. Two examples are discussed to illustrate the theoretical result.

In this paper we present a method of bidirectional control of a vehicle string that achieves l2 weak string stability. Previous work required short-range communications between vehicles within the string. Here, we utilise some recent results on integral action to remove the requirement of communications altogether.

Genital pain / penetration disorders affect a significant portion of the female population and diminish significantly the quality of life of the subjects. Treatments, that often consist in stretching opportunely the vaginal duct by means of opportune vaginal dilators, are known to be invasive, lengthy and uncomfortable. Designing better treatments (e.g., more efficient locations and levels of pressures) nonetheless requires understanding better how the pressure developed in the vaginal channel affects the patient and leads to subjective pain. Here we take a control -oriented approach to the problem, and aim at describing the dynamics of the pressure vs. pain mechanisms by means of opportune state space representations. In particular, we first collect and discuss the medical literature, that describes how the variables that are involved in the treatment of genital pain / penetration disorders with vaginal dilators, are logically related. After this we translate (and complete) this set of logical relations into a qualitative model that allows control oriented analyses of the dynamics. The obtained state space model is then proved to both mimic correctly what is expected from logical perspectives and reproduce behaviors measured in clinical settings. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.