Cloud-based Hardware-in-the-Loop testing of building automation controllers

This paper aims to fill the gap in knowledge about building control testing, thus encouraging control evaluation, to ultimately improve energy efficiency in building operation. A hardware controller and its digital twin are tested in real-time in a separate platform in the cloud, and the results are analyzed to determine what insights can tests on virtual controllers give about the behaviors of their real-life hardware counter-parts. Key performance indicators (KPIs) from literature are used to evaluate the control. It is shown that although the controllers differ in their control output time-series, their overall performance profile along the entire simulation run follows a similar pattern (KPI percent error varies between 3% and 8%). This implies that virtual controller testing can be a good indicator of hardware controller behavior.

Methacholine Challenges: comparison of different tidal breathing challenge methods

Tidal breathing methacholine challenges are now recommended by guidelines, to avoid the bronchoprotective effects of deep inhalation. This study compared different tidal breathing methacholine challenge methods, assessed the agreement between tidal dosimetric and continuous output challenges, and challenge repeatability with different methods. 15 asthma patients performed dosimetric challenges and a continuous output breath actuated challenge, all at least 3 days apart. All subjects had a pre-bronchodilator forced expired volume in 1 s (FEV1) ≥65% predicted, and PD20 <1.2 mg. Of the dosimetric challenges, one method increased methacholine concentration (standard dosimetric challenge), and one adjusted nebuliser output time to increase dose (adjusted dosimetric challenge). The adjusted dosimetric and continuous output challenges were performed twice on separate days to assess for repeatability. All challenges were matched for dose at each dose step. The mean PD20 ratio of the standard dosimetric challenge to the adjusted dosimetric challenge was 0.90 (CI: 0.66–1.23; p=0.49), and intraclass correlation coefficient (ICC)=0.82. Repeated adjusted dosimetric challenges had an ICC=0.62 for PD20. Repeated continuous output challenges had an ICC =0.74 for PD20. The adjusted dosimetric and continuous output challenges correlated (r=0.69, p=0.0043; ICC: 0.65), but PD20 was higher for the adjusted dosimetric challenge (mean PD20 ratio=2.31; CI: 1.57–3.40; p=0.0004). Tidal dosimetric methacholine challenge using adjustment of nebuliser output produces results with good repeatability. The results of this adjusted dosimetric method differed from the continuous output method, underscoring that the results of different methacholine challenge methodologies may not be directly comparable.

Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system

The control of a pH process is complex because of severe nonlinearities in its behavior. A continuous pH neutralization process is usually represented as a first-order plus dead time system, but its gain varies for different operating points. Therefore, a conventional linear controller cannot be used, and the pH system was thus represented as a linear state-space model around an equilibrium point. This linear model was then used to compute the PID controller gains using robust and optimization techniques like quantitative feedback theory, bacterial foraging technique-based particle swarm optimization algorithm, and genetic algorithm. The corresponding controller gains resulting from the three algorithms were used to control the pH using a reconfigurable I/O device, NI myRIO-1900. Finally, the output time domain specifications and the servo and regulatory responses, resulting from the three algorithms, were compared in simulation and in real-time to deduce the appropriate tuning algorithm for this system.

High-Order Filtered PID Controller Tuning Based on Magnitude Optimum

The paper presents a tuning method for PID controllers with higher-order derivatives and higher-order controller filters (HO-PID), where the controller and filter orders can be arbitrarily chosen by the user. The controller and filter parameters are tuned according to the magnitude optimum criteria and the specified noise gain of the controller. The advantages of the proposed approach are twofold. First, all parameters can be obtained from the process transfer function or from the measured input and output time responses of the process as the steady-state changes. Second, the a priori defined controller noise gain limits the amount of HO-PID output noise. Therefore, the method can be successfully applied in practice. The work shows that the HO-PID controllers can significantly improve the control performance of various process models compared to the standard PID controllers. Of course, the increased efficiency is limited by the selected noise gain. The proposed tuning method is illustrated on several process models and compared with two other tuning methods for higher-order controllers.

An ECG Signal Classification Method Based on Dilated Causal Convolution

The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are tight. The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an automatic classification of ECG signals based on a dilated causal convolutional neural network. To solve the problem that the recurrent neural network framework network cannot be accelerated by hardware equipment, the dilated causal convolutional neural network is adopted. Given the features of the same input and output time steps of the recurrent neural network and the nondisclosure of future information, the network is constructed with fully convolutional networks and causal convolution. To reduce the network depth and prevent gradient explosion or gradient disappearance, the dilated factor is introduced into the model, and the residual blocks are introduced into the model according to the shortcut connection idea. The effectiveness of the algorithm is verified in the MIT-BIH Atrial Fibrillation Database (MIT-BIH AFDB). In the experiment of the MIT-BIH AFDB database, the classification accuracy rate is 98.65%.

Improving the integrated hydrological simulation on a data-scarce catchment with multi-objective calibration

The process-based hydrological model Soil and Water Assessment Tool ensures the simulation's reliability by calibration. Compared to the commonly applied single-objective calibration, multi-objective calibration benefits the spatial parameterization and the simulation of specific processes. However, the requirements of additional observations and the practical procedure are among the reasons to prevent the wider application of the multi-objective calibration. This study proposes to consider three groups of objectives for the calibration: multisite, multi-objective function, and multi-metric. For the study catchment with limited observations like the Yuan River Catchment (YRC) in China, the three groups corresponded to discharge from three hydrometric stations, both Nash–Sutcliffe efficiency (NSE) and inversed NSE for discharge evaluation, and MODIS global terrestrial evapotranspiration product and baseflow filtered from discharge as metrics, respectively. The applicability of two multi-objective calibration approaches, the Euclidean distance and nondominated sorting genetic algorithm II, was analyzed to calibrate the above-mentioned objectives for the YRC. Results show that multi-objective calibration has simultaneously ensured the model's better performance in terms of the spatial parameterization, the magnitude of the output time series, and the water balance components, and it also reduces the parameter and prediction uncertainty. The study thus leads to a generalized, recommended procedure for catchments with data scarcity to perform the multi-objective calibration.