AbstractOne of the major impacts of the current COVID-19 pandemic is the immense strain that is being put on the intensive care units. As a typical SARS-CoV-2 infection often leads to severe acute respiratory syndromes, one of the most basic essential examinations is lung auscultation. There are different categories of lung sounds that can be assessed with a stethoscope like crackles, wheezes, rhonchi and coarse breath sounds. Each of these malicious lung sounds contribute to a clinical assessment and follow-up of the patient’s airways and lungs and can indicate the development of pneumonia. Therefore, auscultation on a regular basis is essential, but it also takes a lot of time for the physicians. Moreover, because of the risk of infection for health care workers, it is becoming increasingly difficult or sometimes impossible to safely use a stethoscope. Here we introduce a remote stethoscope system that can help to reduce the workload of physicians and nurses, diminishes the risk of contamination and leads to more standardized auscultation, which can also be stored and reevaluated. We provide a detailed description of our system, which consists of parts that are low cost, easily available in most parts of the world, or can be 3D printed. We provide instructions and an open source software stack to run such a system for a large number of patients which can be remotely auscultated from a central computer. We believe that the system described in this paper can allow various institutes to replicate the system, and increase the safety and reduce the overall workload in the intensive care units around the world.

The overall motion performance of mechatronic machines depends heavily on proper knowledge of the machine’s characteristics, among which the inertia. Moreover, the inertia of most multi-body mechanisms, for example, reciprocating slider-crank mechanisms, varies as a function of the machine position, thereby challenging optimal control. Nevertheless, the inertia’s variation is sometimes not known accurately enough or changes over time due to, e.g. a production process or premature wear, forcing an online control/correction of the inertia profile. Therefore, earlier, the author developed a new computational-friendly online method based on a frequency-specific magnitude response to estimate the machine’s position-dependent load side inertia, assuming good knowledge of all other machine properties. Yet, to guarantee accuracy, the maximum machine speed during estimation had to be strongly reduced, resulting in an undesirable longer motion time. Therefore, in this work, the failure mechanism of the earlier proposed estimator is analysed and overcome by intelligently separating estimation and motion signals. As a result, the estimator is no longer limited by the machine’s motion speed but rather by a maximum in the inertia variation per signal excitation period and, thus, time. Moreover, given this limiting variation, a guideline was established to evaluate the new method’s estimation quality. Finally, both in simulation and on a physical machine, by taking advantage of the newly proposed method, the minimal required estimation time is reduced significantly (up to 50% on the machine) without diminishing the estimation accuracy.

The use of technologies that automate handling goods and loading units in warehouses and depots is not new. Yet, the purchase process of these technologies issues troubles and the estimation of the economic advantages brought by one or another technology to the entire chain of operations in logistics are not always known. Faults or not documented decisions put pressure on managers and prices for services. They can cause a drop in the competitiveness of warehouse operators, particularly in uncertain conditions. Academia documented the cost of warehouse storage well. Yet, little research has looked into the economic justification of implementing automatic systems for loading or unloading activities and the impact on complementary operations. For this reason, a model is needed to calculate the cost of operations when different technical equipment is used. This research further investigates the cost categories that must be considered when purchasing automated loading/unloading technologies. The model includes the purchase and operational loading costs that new technologies generate and the cost of adjacent operations to loading activity. The case study uses forklifts as the reference scenario and provides an overview of the return on investment and a break-even period when other technologies are in use. The calculation model shows that increasing cargo volume leads to a better RoI. The same observation is also made regarding the rise in labour costs. For the latter, using human operators to handle pallets on a one-by-one basis generates an exponential increase in operational cost due to delays and faults. On the other side, the cost of implementing automated loading/unloading technologies and the consideration of technology risk determine the low economic advantages. An in-depth cost and benefit analysis shows in which situation a technology generates greater benefits. Further results of this paper show that better use of trucks' loading capacity can positively impact the financial performance of automated loading technologies, as a higher volume of cargo is moved (at once) without human intervention.

For economic reasons, machine builders are increasingly challenged to make single-axis driven machines perform rest-to-rest movements as fast as possible over time.In terms of control, a time-optimal motion is performed by injecting a bang-bang torque profile, characterized by a discrete switching point.However, the state-of-the-art to obtain the ideal applicationdependent switching point is often computationally demanding and lacks robustness, hampering smooth system implementation.Moreover, machine builders invariably design their machines using CAD software, which automatically provides good knowledge about, e.g., the machine's load torque profile.Therefore, in this work, based on Newton's work-energy principle, a framework for variable inertia systems is derived and used as a starting point to estimate the optimal bang-bang switching point efficiently, employing CAD extracted data.In addition, a self-learning control structure is proposed to correct for the initial switching point so that the application continues to move time-optimally, regardless of system influences such as temperature variation.A case study is used to validate the proposed methodology and associated control structure through simulation.

Model-based systems engineering (MBSE) techniques can help manage the growing complexity in the design and development of cyber-physical systems, and can even allow for the optimization of a system under design in simulation. However, models are always an abstraction of the real-world systems they represent. This introduces uncertainty at the model level, which affects the validity of simulation results, and thus also the results of the optimization. This, together with variations in real-world system parameters, significantly complicates the validation of simulation and optimization results. In this experience report, we first use a descriptive process model to describe our efforts to validate the results of a model-based design space exploration (DSE) process given this uncertainty. After this, we discuss lessons learned and insights gained, and identify future challenges. We present a possible prescriptive process model for future validation efforts, which specifically takes into account uncertainty.

The essential advantage of the conventional stepping motor drive technique bases on step command pulses is the ability of open-loop positioning. By ruling out the cost of a position sensor, stepping motors are preferred in low power positioning applications. However, machine developers also want to obtain high dynamics with these small and cheap stepping motors. For that reason, stepping motors are used at its limits as much as possible. A drawback of the open-loop control is the continuous risk of missing a step due to overload. Due to this uncertainty, robustness is a major issue in stepping motor applications. Until today, to reduce the possibility of step loss, the motor is typically driven at maximum current level or is over-dimensioned with results in low-efficiency. Therefore in this paper, a self-learning [Formula: see text]-controller optimizing the current is presented. Moreover, to allow broad industrial applicability, this technique is computationally simple, needs no mechanical or electrical parameter knowledge and take into account the unique character of stepping motors and their conventional drive technique based on step command pulses. The proposed algorithm is validated through measurements on a hybrid stepping motor.

Choosing sine-wave instead of square-wave shaped currents to drive a brushless DC (BLDC) motor can increase the energy-efficiency up to 9.5%.But, unfortunately, for sine-wave setpoint current generation, the typical electronic or sensing low-resolution commutation feedback techniques become unavailable. For broad industrial employability, other sensing techniques such as computationally complex observers or signal injection methods requiring access to the switching states of the power electronics are not preferred. This raises the need to develop a computationally sufficient simple sensorless controller that optimizes the energy efficiency. Therefore, the authors propose a PID algorithm controlling the estimated load angle technique that enables sinusoidal current supply without the need for position feedback or user input. The aim is to implement a controller for dynamic speed-varying BLDC motor applications, which means that accurate speed trajectory tracking behavior and high robustness against load changes should be guaranteed. The application-dependent PID settings for setpoint and disturbance rejection control are estimated during an initialization speed trajectory based on only one stator winding current and voltage measurement. The proposed control algorithm is validated through experimental measurements on a BLDC motor with a nominal speed and power of 3000 r/min and 225 $\mathrm{W}$ , respectively.

Nowadays, Brushless dc (BLDC) motors are increasingly used to drive speed-controlled applications (e.g., drones, air compressors, etc.) thanks to their high power density, good torque inertia ratio, and their simple square wave commutation principle. However, with growing attention toward energy usage, a more efficient sinusoidal current wave is favored over the classical square wave profile. Nevertheless, changing the waveform to absent the silence phase implicates that simple control principles (e.g., hall-sensors or back electromotive force tracing) are no longer functional, and a more advanced sensorless solution is required. Earlier, an innovative sensorless load angle based controller was suggested with the potential to overcome all restrictions concerning state-of-the-art sensorless control. One of the proposed estimator’s fundamental building blocks is a sliding discrete Fourier filter, requiring both a signal with a fixed base frequency and a full signal period of samples before updating completely. Both requirements severely limit the application field. This article presents an enhanced load angle estimator that can estimate the load angle 66% faster on average and without quality deterioration, regardless of the rotor speed. Both speed and accuracy of the estimated load angle are validated based on BLDC measurements, confirming the potential and revealing possible further estimation lag decreases if a limited estimation quality regression is allowed.

Cyber–physical systems are becoming increasingly complex. In these advanced systems, the different engineering domains involved in the design process become more and more intertwined. Therefore, a traditional (sequential) design process becomes inefficient in finding good design options. Instead, an integrated approach is needed where parameters in multiple different engineering domains can be chosen, evaluated, and optimized to achieve a good overall solution. However, in such an approach, the combined design space becomes vast. As such, methods are needed to mitigate this problem. In this paper, we show a method for systematically capturing and updating domain knowledge in the context of a co-design process involving different engineering domains, i.e. control and embedded. We rely on ontologies to reason about the relationships between parameters in the different domains. This allows us to derive a stepwise design space exploration workflow where this domain knowledge is used to quickly reduce the design space to a subset of likely good candidates. We illustrate our approach by applying it to the design space exploration process for an advanced electric motor control system and its deployment on embedded hardware.

Objectives: To determine whether the revised 2018 ATS/ERS/JRS/ALAT radiological criteria for usual interstitial pneumonia (UIP) provide better diagnostic agreement compared to the 2011 guidelines. Methods: Cohort for this cross-sectional study (single center, nonacademic) was recruited from a multidisciplinary team discussion (MDD) from July 2010 until November 2018, with clinical suspicion of fibrosing interstitial lung disease (n= 325). Exclusion criteria were technical HRCT issues, known connective tissue disease (rheumatoid arthritis, systemic sclerosis, poly-or dermatomyositis), exposure to pulmonary toxins or lack of working diagnosis after MDD. Four readers with varying degrees in HRCT interpretation independently categorized 192 HRCTs, according to both the previous and current ATS/ERS/JRS/ALAT radiological criteria. An inter-rater variability analysis (Gwet’s second-order agreement coefficient, AC2) was performed. Results: The resulting Gwet’s AC2 for the 2011 and 2018 ATS/ERS/JRS/ALAT radiological criteria is 0.62 (±0.05) and 0.65 (±0.05), respectively. We report only minor differences in agreement level among the readers. Distribution according to the 2011 guidelines is as follows: 57.3% ‘UIP pattern’, 24% ‘possible UIP pattern’, 18.8% ‘inconsistent with UIP pattern’ and for the 2018 guidelines: 59.6% ‘UIP’, 14.5% ‘probable UIP’, 15.9% ‘indeterminate for UIP’ and 10% ‘alternative diagnosis’. Conclusions: No statistically significant higher degree of diagnostic agreement is observed when applying the revised 2018 ATS/ERS/JRS/ALAT radiological criteria for UIP compared to those of 2011. The inter-rater variability for categorizing the HRCT patterns is moderate for both classification systems, independent of experience in HRCT interpretation. The major advantage of the current guidelines is the better subdivision in the categories with a lower diagnostic certainty for UIP. Advances in knowledge: - In 2018, a revision of the 2011 ATS/ERS/JRS/ALAT radiological criteria for UIP was published, part of diagnostic guidelines for idiopathic pulmonary fibrosis. - The inter-rater agreement among radiologist is moderate for both classification systems, without a significantly higher degree of agreement when applying the revised radiological criteria.

The performance and robustness of a sensorless controller strongly depends on how quickly and accurately the feedback is obtained. For this reason, the purpose of this paper is to introduce an improved load angle estimator that provides feedback on the ability of the stepping motor to follow the imposed position/speed setpoint. The novel estimation dynamics is compared theoretically and experimentally with dynamics of similar estimators.

The assessment of thermal performance and efficiency of the Pavement Solar Collector (PSC) systems is essential in designing such systems. In this paper, a simplified simulation model is developed to predict PSC's performance at the design stage. Due to its simplicity, accuracy, and extremely quick simulation run-time, the model output can be linked with other thermal models and renewable heat sources to apply real-time changes in the PSC systems and used in the automation of a PID-controller. The output results show good compatibility between the predicted and simulated data, where the relative error values for model parametrization and validation are less than 0.21% and 0.5%. In terms of computational cost, a yearly long-term performance run time of the present model was simply 3 s (without parametrization), 300,000 times faster than a FEM model for only one month. A systematic sensitivity analysis on different design parameters shows that the efficiency index increases with an increase in pipe spacing, pipe embedment depth, and flow rate, while it decreases with an increase in the asphalt thermal conductivity in the wintertime. Also, the variation of the heat transfer coefficient UA* and correlation parameter k in different PSC configurations was established.

Hall sensors embedded in Brushless DC system enable electronic commutation. However, even small misalignment of the Hall sensors can lead to unwanted torque ripples or reduced performance. Sensorless back-EMF feedback algorithms based on the stator winding dynamics seem a promising alternative for the Hall sensors. However, what remains to be done to arrive at an implementable solution is to assess the electrical parameters, i.e. resistance and inductance, as these parameters vary with the motor conditions and cannot be assumed constant. Therefore in this paper, an impedance estimator is presented. An disconnected approach is proposed, whereby the back-EMF estimation is seen separately from the real-time identification of stator impedance. Through this, the accuracy of the back-EMF estimator depends on the accuracy of the resistance and inductance estimator and not vice versa. The proposed estimation algorithm is validated through experimental measurements on a 225 W BLDC.

Nowadays, stepper motors are extensively used in positioning applications due to excellent open-loop accuracy and a relatively simple control principle. Every time the controller sends a step-command pulse to the motor, the rotor will move for a known discrete angle. By subsequently counting the number of pulses, the rotor angle is known at all times. Nevertheless, due to the control principle's nature, as a matter of safety, the bulk of stepper motors are often not driven at their full potential to prevent so-called step-losses. Typically, this results in low energy efficiency and an over-dimensioned motor. As a solution, maximizing the motor's load potential through intelligent algorithms contributes to smaller motors and increases efficiency since higher motion speeds are reachable. Until now, in search of optimal motor usage for point-to-point motion profiles, literature mainly focused on finding time-optimal motion profiles using simplified models with a complicated analytical approach rather than developing an easily executable methodology that optimizes at the fundamental control level. Therefore, this paper presents a novel optimization methodology, solely based on the motor's load angle, of which the resulting puls commands' timings can be easily deployed in commercial stepper motor drives. Results show a significant improvement in time-saving of 36,45% compared to a reference 5th-order polynomial point-to-point trajectory.

The importance of green technologies for generating renewable energy and sustainable development is widely accepted. Highway pavements which are exposed to solar radiation, absorb a large amount of heat that could be harvested. This study aims to design an innovative thermoelectric generator system that utilizes the thermal gradients between the pavement surface and the soil below the pavement and converts it to electricity. This system consists of a heat collector, a thermal electric generator (TEG) and a coolant module. A prototype was fabricated to embed directly into asphalt pavements. Several simulations using finite element (FE) analyses were conducted to evaluate the performance of the system components and determine their optimal design. The final design was also tested in the field. Based on the experimental and FE results, the efficiency of the system was enhanced by improving its coolant module by incorporating a phase-changing heat sink. The optimized prototype was able to generate an average of 29 mWatts of electricity for South Texas conditions, which appears to be a promising independent source of power for road-side wireless sensors and near-field data communications.

Stepping motors are well suited for open-loop positioning tasks at low-power. The rotor position of the machine is simply controlled by the user. Every time the user sends a next pulse, the stepping motor driver excites the correct stator phases to rotate the rotor over a pre-defined discrete angular position. In this way, counting the step command pulses enables open-loop positioning. However, when the motor is overloaded or stuck, the relation between the expected rotor position based on the number of step command pulses and the actual rotor position is lost. To avoid this, the bulk of the widely used full step open-loop stepping motor drive algorithms are driven at maximum current. This non-optimal way of control leads to low efficiency. To use stepping motors more optimally, closed loop control is needed. A previously described sensorless load angle estimation algorithm, solely based on voltage and current measurements, is used to provide sensorless feedback. A closed loop load angle controller adapts the current level to reach the setpoint load angle to obtain the optimal torque/current ratio. The difficulty is that the optimal load angle depends on the mechanical dynamics. To avoid the requirement of knowledge of the mechanical parameters, a practical learning algorithm to determine the optimal load angle is presented in this paper. Measurements validate the proposed approach.

Cyber-physical systems are becoming increasingly complex. In these advanced systems, the different engineering domains involved in the design process become more and more intertwined. In these situations, a traditional (sequential) design process becomes inefficient in finding good designs options. Instead, an integrated approach is needed where parameters in both the control and embedded domain can be chosen, evaluated and optimized to have a good solution in both domains. However, in such an approach, the combined design space becomes vast. As such, methods are needed to mitigate this problem. In this paper, we show how domain knowledge can be used to guide the design-space exploration process for an advanced control system and its deployment on embedded hardware. We use domain knowledge, captured in an ontology, to reason about the relationships between parameters in the different domains. This leads to a stepwise design space-exploration process where this domain knowledge is used to quickly reduce the design space to a subset of likely good candidates. In this process, we make use of cross-domain evaluation to find feasible design options with good system-level performance.

As machine users generally only define the start and end point of the movement, a large trajectory optimization potential rises for single axis mechanisms performing repetitive tasks. However, a descriptive mathematical model of the mechanism needs to be defined in order to apply existing optimization techniques. This is usually done with complex methods like virtual work or Lagrange equations. In this paper, a generic technique is presented to optimize the design of point-to-point trajectories by extracting position dependent properties with CAD motion simulations. The optimization problem is solved by a genetic algorithm. Nevertheless, the potential savings will only be achieved if the machine is capable of accurately following the optimized trajectory. Therefore, a feedforward motion controller is derived from the generic model allowing to use the controller for various settings and position profiles. Moreover, the theoretical savings are compared with experimental data from a physical set-up. The results quantitatively show that the savings potential is effectively achieved thanks to advanced torque feedforward with a reduction of the maximum torque by 12.6% compared with a standard 1/3-profile.