Intelligent buildings are quickly becoming cohesive and integral inhabitants of cyberphysical ecosystems. Modern buildings adapt to internal and external elements and thrive on ever-increasing data sources, such as ubiquitous smart devices and sensors, while mimicking various approaches previously known in software, hardware, and bioinspired systems. This article provides an overview of intelligent buildings of the future from a range of perspectives. It discusses everything from the prospects of U.S. and world energy consumption to insights into the future of intelligent buildings based on the latest technological advancements in U.S. industry and government.

As the Internet of Things (IoT) continues its run as one of the most popular technology buzzwords of today, the discussion really turns from how the massive data sets are collected to how value can be derived from them, i.e., how to extract knowledge out of such (big) data. IoT devices are used in an ever-growing number of application domains (see Figure 1), ranging from sports gadgets (e.g., Fitbits and Apple Watches) or more serious medical devices (e.g., pacemakers and biochips) to smart homes, cities, and self-driving cars, to predictive maintenance in missioncritical systems (e.g., in nuclear power plants or airplanes). Such applications introduce endless possibilities for better understanding, learning, and informedly acting (i.e., situational awareness and actionable information in government lingo). Although rapid expansion of devices and sensors brings terrific opportunities for taking advantage of terabytes of machine data, the mind-boggling task of understanding growth of data remains and heavily relies on artificial intelligence and machine learning [1], [2].

Efficient modeling of high-dimensional data requires extracting only relevant dimensions through feature learning. Unsupervised feature learning has gained tremendous attention due to its unbiased approach, no need for prior knowledge or expensive manual processing, and ability to handle exponential data growth. Deep Autoencoder (AE) is a state-of-the-art deep neural network for unsupervised feature learning, which learns embedded-representations using a series of stacked layers. However, as the AE network gets deeper, these learned embedded-representations can deteriorate due to vanishing gradient, leading to performance degradation. This article presents ResNet Autoencoder (RAE) and its convolutional version (C-RAE) for unsupervised feature learning. The advantage of RAE and C-RAE is that it enables the user to add residual connections for increased network capacity without incurring the cost of degradation for unsupervised feature learning compared to standard AEs. While RAE and C-RAE inherit all the advantages of AEs, such as automated non-linear feature extraction and unsupervised learning, they also allow users to design larger networks without adverse effects on feature learning performance. We performed classification on learned embedded-representation to evaluate RAE and C-RAE. RAE and C-RAE were compared against AEs on MNIST, Fashion MNIST, and CIFAR10 datasets. When increasing the number of layers, C-RAE outperformed AE by showing significantly lower performance degradation of classification accuracy (less than 3%) compared to AE (33% to 65%). Further, C-RAE exhibited higher mean accuracy and lower variance of accuracy than standard AE. When comparing RAE and C-RAE with widely used feature learning methods (Convolutional AE, PCA, ICA, LLE, Factor Analysis, and SVD), C-RAE showed the highest accuracy.

In this article a finite control set model predictive control, is used to track a current reference in a power converter connected to an $RL$ load. An artificial intelligence approach is presented for real-time determination of the weighting factor that regulates the average switching frequency, independently of the operating point. The article focuses on the design, training, and digital implementation of an artificial neural network (ANN) that can be developed in a low-cost control platform. It is presented a sampling and offline ANN training procedure, together with a low-cost hardware implementation based on integer quantization of the ANN. The abovementioned approach provides a standalone application, serving as a framework for the development of ANN applications for power-converters. The main advantage of the presented approach is that the ANN inference is executed in real time. In this way, the weighting factor is automatically updated in real-time, allowing the system to quickly adapt to any reference step changes, and consequently provide the desired behavior. Executing the setup in laboratory prototype confirmed the theoretical analysis and successful tracking of the reference frequency.

Cyber-Physical Systems (CPSs) play a critical role in our modern infrastructure due to their capability to connect computing resources with physical systems. As such, topics such as reliability, performance, and security of CPSs continue to receive increased attention from the research community. CPSs produce massive amounts of data, creating opportunities to use predictive Machine Learning (ML) models for performance monitoring and optimization, preventive maintenance, and threat detection. However, the “black-box” nature of complex ML models is a drawback when used in safety-critical systems such as CPSs. While explainable ML has been an active research area in recent years, much of the work has been focused on supervised learning. As CPSs rapidly produce massive amounts of unlabeled data, relying on supervised learning alone is not sufficient for data-driven decision making in CPSs. Therefore, if we are to maximize the use of ML in CPSs, it is necessary to have explainable unsupervised ML models. In this paper, we outline how unsupervised explainable ML could be used within CPSs. We review the existing work in unsupervised ML, present initial desiderata of explainable unsupervised ML for CPS, and present a Self-Organizing Maps based explainable clustering methodology which generates global and local explanations. We evaluate the fidelity of the generated explanations using feature perturbation techniques. The results show that the proposed method identifies the most important features responsible for the decision-making process of Self-organizing Maps. Further, we demonstrated that explainable Self-Organizing Maps are a strong candidate for explainable unsupervised machine learning by comparing its model capabilities and limitations with current explainable unsupervised methods.

The Deep Self-Organizing Map (DSOM) was introduced to embed hierarchical feature abstraction capability to SOMs. This paper presents an extended version of the original DSOM algorithm (E-DSOM). E-DSOM enhances the DSOM in two ways: 1) learning algorithm is modified to be completely unsupervised, and 2) architecture is modified to learn features of different resolution in hidden layers. E-DSOM has three main advantages over the original DSOM: 1) improved classification accuracy, 2) improved generalization capability and 3) need of fewer sequential layers (reduced training time). E-DSOM was tested on benchmark and real-world datasets and was compared against DSOM, SOM, Stacked Autoencoder (AE), and Stacked Convolutional Autoencoder (CAE). Experimental results showed that the E-DSOM outperformed DSOM with improvements of classification accuracy up to 15% while saving training time up to 19% on all datasets. Moreover, E-DSOM evidenced better generalization capability compared to the DSOM by showing superior performance on all datasets with induced noise. Further, E-DSOM showed comparable performance to the AE and the CAE while outperforming them on two datasets.