Differential Entropy-Based Fault-Detection Mechanism for Power-Constrained Networked Control Systems.

In this work, we consider the design of power-constrained networked control systems (NCSs) and a differential entropy-based fault-detection mechanism. For the NCS design of the control loop, we consider faults in the plant gain and unstable plant pole locations, either due to natural causes or malicious intent. Since the power-constrained approach utilized in the NCS design is a stationary approach, we then discuss the finite-time approximation of the power constraints for the relevant control loop signals. The network under study is formed by two additive white Gaussian noise (AWGN) channels located on the direct and feedback paths of the closed control loop. The finite-time approximation of the controller output signal allows us to estimate its differential entropy, which is used in our proposed fault-detection mechanism. After fault detection, we propose a fault-identification mechanism that is capable of correctly discriminating faults. Finally, we discuss the extension of the contributions developed here to future research directions, such as fault recovery and control resilience.

Toward an Intelligent Campus: IoT Platform for Remote Monitoring and Control of Smart Buildings.

With the growing need to obtain information about power consumption in buildings, it is necessary to investigate how to collect, store, and visualize such information using low-cost solutions. Currently, the available building management solutions are expensive and challenging to support small and medium-sized buildings. Unfortunately, not all buildings are intelligent, making it difficult to obtain such data from energy measurement devices and appliances or access such information. The internet of things (IoT) opens new opportunities to support real-time monitoring and control to achieve future smart buildings. This work proposes an IoT platform for remote monitoring and control of smart buildings, which consists of four-layer architecture: power layer, data acquisition layer, communication network layer, and application layer. The proposed platform allows data collection for energy consumption, data storage, and visualization. Various sensor nodes and measurement devices are considered to collect information on energy use from different building spaces. The proposed solution has been designed, implemented, and tested on a university campus considering three scenarios: an office, a classroom, and a laboratory. This work provides a guideline for future implementation of intelligent buildings using low-cost open-source solutions to enable building automation, minimize power consumption costs, and guarantee end-user comfort.

A High-Throughput Microtiter-Based Fungicide Sensitivity Assay for Oomycetes Using Z'-Factor Statistic.

Current methods to quantitatively assess fungicide sensitivity for a diverse range of oomycetes are slow and labor intensive. Microtiter-based assays can be used to increase throughput. However, many factors can affect their quality and reproducibility. Therefore, efficient and reliable methods for detection of assay quality are desirable. The objective of this study was to develop and validate a robust high-throughput fungicide phenotyping assay based on spectrophotometric quantification of mycelial growth in liquid culture and implementation of quality control with Z' factor and growth curves. Z' factor was used to ensure that each isolate grew enough in the absence of fungicides compared with the negative control, and growth curves were used to ensure active growth at the time of concentration of a fungicide that reduces growth by 50% (EC50) estimation. EC50 and relative growth values were correlated in a side-by-side comparison with values obtained using the amended medium (gold standard) assay. Concordance correlation indicated that the high-throughput assay is accurate but may not be as precise as the amended medium assay. To demonstrate the utility of the high-throughput assay, the sensitivity of 216 oomycete isolates representing four genera and 81 species to mefenoxam and ethaboxam was tested. The assay developed herein will enable high-throughput fungicide phenotyping at a population or community level.

Tuning Generalized Predictive PI controllers for process control applications.

Predictive PI (PPI) controllers have demonstrated to exceed traditional PID controllers when they are applied to systems with long delays. This work proposes a new controller structure and tuning that we call Generalized Predictive PI (GPPI) controller which provides greater design flexibility than PI and PPI strategies. To realize a fair comparison, the design and tuning rules for discrete PI and PPI controllers were developed using optimal arguments based on the root-locus, for critically damped response before a step change in the reference. Experimental results, using industrial equipment, have illustrated the tuning methodology and the performance of the proposed controller under real conditions. Flow and water level process in a laboratory flume were considered. For these processes, First Order Plus Time Delay (FOPTD) models are used. The GPPI control results are encouraging, reducing the settling time plus a very small overshoot before step change in the reference regarding the PI and PPI strategies, up to 41.03% for the flow control loop and up to 54.21% for the level control loop. The discrete analysis of the strategies in the Z plane was performed, allowing for a direct translation to recursive equations that can then be programmed into a Programmable Logic Controller (PLC), other industrial controllers such as Distributed Control Systems (DSC), or microcontrollers, such as Arduino, Raspberry or FPGA. This is an important result, since it demonstrates that the increased complexity of the proposed controller does not hamper its implementation in industrial controller systems. In this work, we used a Rockwell ControlLogix \protect \relax \special {t4ht=®} PLC with Structured Text programming language.

Signal-to-noise ratio constrained feedback control: Robust stability analysis.

The explicit consideration of a communication channel model in a feedback control loop is known to be constrained by a fundamental limitation for stabilizability on the channel signal-to-noise ratio (SNR) when the linear time invariant (LTI) plant model is unstable. The LTI modelling approach for real, usually nonlinear, processes compromises accuracy versus complexity of the resulting model. This in turn introduces a gap between the proposed model and the real process, which is known as the model uncertainty. In this paper we then study SNR limitations by considering the continuous-time scenario and the case of an additive coloured Gaussian noise (ACGN) channel with bandwidth limitation, for which we then quantify the infimal SNR subject to the simultaneous presence of plant, channel and noise model uncertainties. We observe, for the special case of memoryless additive white Gaussian noise (AWGN) channels, that the obtained SNR limitation subject to plant model uncertainty can be redefined as a channel capacity limitation for stabilization.

Clustering of atrial fibrillation based on surface ECG measurements.

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical research. In particular, the study of AF types or sub-classes is a very interesting research topic. In this paper we present a preliminary study to find sub-classes of AF from real 12-lead ECG recordings using k-means and hierarchical clustering algorithms. We applied blind source separation to an initial set of 218 recordings from which we extracted a subset of 136 atrial activity signals displaying known properties of AF. As features for clustering we proposed the peak frequency mean value (PFM), peak frequency standard deviation (PFSD) and the spectral concentration (SC). We computed the silhouette coefficient to obtain an optimal number of clusters of k=5, and conducted preliminary feature selection to evaluate clustering quality. We observed that the separability increases if we discard SC as a feature. The proposed method is the first stage to a future AF classification method, which combined with specialist advice, should help in the clinical field.

Atrial activity selection for atrial fibrillation ECG recordings.

In this paper we apply independent component analysis (ICA) followed by second order blind identification (SOBI) to an atrial fibrillation (AF) 12-lead electrocardiogram (ECG) recording in order to extract the source that represents atrial activity (AA) (ICA-SOBI method). Still, there is no assurance that only one source obtained from this method will contain AA, and thus we aim to select the most representative source of AA. The novelty in this paper is the proposal of three parameters to select the most representative source of AA. These parameters are correlation coefficient with lead V1 (CV1), peak factor (PF) and spectral concentration (SC). The first two parameters are introduced as new indicators, addressing features overlooked by the SC even when they are present in AA during AF. For synthesized data, at least two of the three parameters select the same representation of AA in 93.3% of the cases. For real data (218 ECG recordings), we observe that PF presents, in 89.5% of the cases, values between 2 and 4.5 for the selected sources, ensuring a well-defined range of values for AA. The actual values of CV1 and SC were scattered throughout their possible ranges (0-1 for CV1 and 0.08-0.7 for SC), and the correlation coefficient between these variables was found to be ρ=0.58. We compared our results with three known algorithms: QRST cancellation, principal components analysis (PCA) and ICA-SOBI. The results obtained from this comparison show that our proposed methods to select the best representation of AA in general outperform the three above-mentioned algorithms.