This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.

A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

Theory and applications of HVAC control systems – A review of model predictive control (MPC)

This survey provides a structured and comprehensive overview of research on security and privacy in computer and communication networks that use game-theoretic approaches. We present a selected set of works to highlight the application of game theory in addressing different forms of security and privacy problems in computer networks and mobile applications. We organize the presented works in six main categories: security of the physical and MAC layers, security of self-organizing networks, intrusion detection systems, anonymity and privacy, economics of network security, and cryptography. In each category, we identify security problems, players, and game models. We summarize the main results of selected works, such as equilibrium analysis and security mechanism designs. In addition, we provide a discussion on the advantages, drawbacks, and future direction of using game theory in this field. In this survey, our goal is to instill in the reader an enhanced understanding of different research approaches in applying game-theoretic methods to network security. This survey can also help researchers from various fields develop game-theoretic solutions to current and emerging security problems in computer networking.

/pdf/game-theory-meets-network-security-and-privacy-1r8g3nc948.pdf

Game theory meets network security and privacy

Since digital control systems were introduced to the market more than 30 years ago, the operational efficiency and stability gained through their use have fueled our migration and ultimate dependence on them for the monitoring and control of critical infrastructure. While these systems have been designed for functionality and reliability, a hostile cyber environment and uncertainties in complex networks and human interactions have placed additional parameters on the design expectations for control systems.

/pdf/resilient-control-systems-next-generation-design-research-43oz294p5b.pdf

Resilient control systems: Next generation design research

Revealing occupancy patterns in an office building through the use of occupancy sensor data

Security of control systems is becoming a pivotal concern in critical national infrastructures such as the power grid and nuclear plants. In this paper, we adopt a hierarchical viewpoint to these security issues, addressing security concerns at each level and emphasizing a holistic cross-layer philosophy for developing security solutions. We propose a bottom-up framework that establishes a model from the physical and control levels to the supervisory level, incorporating concerns from network and communication levels. We show that the game-theoretical approach can yield cross-layer security strategy solutions to the cyber-physical systems.

/pdf/a-hierarchical-security-architecture-for-cyber-physical-25eed3bxul.pdf

A hierarchical security architecture for cyber-physical systems

Building Energy Management Systems (BEMSs) are essential components of modern buildings that are responsible for minimizing energy consumption while maintaining occupant comfort. However, since indoor environment is dependent on many uncertain criteria, performance of BEMS can be suboptimal at times. Unfortunately, complexity of BEMSs, large amount of data, and interrelations between data can make identifying these suboptimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD)-based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior; and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm-based BEMS. Six different scenarios were tested, and the presented Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more) than the alarm based BEMS. Furthermore, the Fuzzy-ADLD method identified cases that were missed by the alarm-based system, thus demonstrating potential for increased state-awareness of abnormal building behavior.

/pdf/mining-building-energy-management-system-data-using-fuzzy-19hf3tuzik.pdf

Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

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.

Craig Rieger

Papers

Resilient control systems: Next generation design research

Revealing occupancy patterns in an office building through the use of occupancy sensor data

A hierarchical security architecture for cyber-physical systems

Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions

Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting