Limited Lookahead Policies for Robust Supervisory Control of Discrete Event Systems

In this thesis, Limited Lookahead Policies (LLP) have been developed for Robust Nonblocking Supervisory Control Problem (RNSCP) of discrete event systems. In the robust control problem considered here, the plant model is assumed to belong to a given finite set of DES models. The introduced supervisor computes the control action in online fashion and it is named Robust Limited Lookahead (RLL) supervisor. In comparison with offline supervisory control, RLL supervisor can reduce the complexity associated with the computation of control law as it looks at the behavior of system at the current state and of a limited depth in future. Since a conservative policy is adopted here, the behavior of the system under supervision of the RLL supervisor is generally more restrictive than the optimal offline supervisor. A sufficient condition is presented under which a limited lookahead window can guarantee the optimality (maximal permissiveness) of the RLL supervisor. In some problems, the required window length for maximally permissive RLL supervisor may become unbounded. To overcome this limitation RNSCP with State information (RNSCP-S) is studied and solved resulting in a state-based RLL (RLL-S) supervisor. The results of this thesis can be regarded as an extension of previous work in the literature on limited lookahead policies for (non-robust) supervisory control to the case of nonblocking robust supervisory control. The robust limited lookahead design procedures are implemented in MATLAB environment and applied to two examples involving spacecraft propulsion systems

Digital Product Architecture and Customer Agility: Evidence from New Digital Ventures

New digital ventures are transforming the world around us. Born-digital companies (such as Uber) that were initially established to serve a specific market can quickly detect new opportunities in other markets and respond to these opportunities by reassembling their resources with speed and ease. Limited research has investigated how product architecture enables or hinders the ability of the firm to sense customer-related opportunities and respond to them effectively. By examining two new digital ventures, this study sheds light on new digital ventures’ customer agility. Specifically, we address how the characteristics of new digital ventures’ product architectures facilitate or hinder the development of the customer-sensing and customer-responding capability dimensions of customer agility. We present theoretical and managerial implications regarding how to leverage digital technologies to foster customer agility

Digital Product Architecture and Customer Agility: Evidence from New Digital Ventures

New digital ventures are transforming the world around us. Born-digital companies (such as Uber) that were initially established to serve a specific market can quickly detect new opportunities in other markets and respond to these opportunities by reassembling their resources with speed and ease. Limited research has investigated how product architecture enables or hinders the ability of the firm to sense customer-related opportunities and respond to them effectively. By examining two new digital ventures, this study sheds light on new digital ventures’ customer agility. Specifically, we address how the characteristics of new digital ventures’ product architectures facilitate or hinder the development of the customer-sensing and customer-responding capability dimensions of customer agility. We present theoretical and managerial implications regarding how to leverage digital technologies to foster customer agility

AAHES: A hybrid expert system realization of Adaptive Autonomy for smart grid

Abstract--Smart grid expectations objectify the need for optimizing power distribution systems greater than ever. Distribution Automation (DA) is an integral part of the SG solution; however, disregarding human factors in the DA systems can make it more problematic than beneficial. As a consequence, Human-Automation Interaction (HAI) theories can be employed to optimize the DA systems in a human-centered manner. Earlier we introduced a novel framework for the realization of Adaptive Autonomy (AA) concept in the power distribution network using expert systems. This research presents a hybrid expert system for the realization of AA, using both Artificial Neural Networks (ANN) and Logistic Regression (LR) models, referred to as AAHES, respectively. AAHES uses neural networks and logistic regression as an expert system inference engine. This system fuses LR and ANN models' outputs which will results in a progress, comparing to both individual models. The practical list of environmental conditions and superior experts' judgments are used as the expert systems database. Since training samples will affect the expert systems performance, the AAHES is implemented using six different training sets. Finally, the results are interpreted in order to find the best training set. As revealed by the results, the presented AAHES can effectively determine the proper level of automation for changing the performance shaping factors of the HAI systems in the smart grid environment

Cyber security for smart grid: a human-automation interaction framework

Abstract-- Power grid cyber security is turning into a vital concern, while we are moving from the traditional power grid toward modern Smart Grid (SG). To achieve the smart grid objectives, development of Information Technology (IT) infrastructure and computer based automation is necessary. This development makes the smart grid more prone to the cyber attacks. This paper presents a cyber security strategy for the smart grid based on Human Automation Interaction (HAI) theory and especially Adaptive Autonomy (AA) concept. We scheme an adaptive Level of Automation (LOA) for Supervisory Control and Data Acquisition (SCADA) systems. This level of automation will be adapted to some environmental conditions which are presented in this paper. The paper presents a brief background, methodology (methodology design), implementation and discussions. Index Terms—smart grid, human automation interaction, adaptive autonomy, cyber security, performance shaping facto

Technology Affordances in Digital Innovation Research: Quo Vadis?

Technology affordance theory has been repeatedly mentioned as a viable lens to study implications of digital technologies for innovation processes and practices. In this research article, we highlight one of the challenges of applying technology affordance theory in its current form to digital innovation research. We address the relationships between individuals and organization within innovation ecosystems. Based on insights generated from the extant literature on technology affordances as well as on digital innovation, we seek to explore the challenges of studying digital innovation through the lens of technology affordance theory. Our research integrates and expands existing theoretical perspectives on affordances to better address the needs of research on complex, emergent socio-technical phenomena such as digital innovation

An intelligent expert system for realization of adaptive autonomy using logistic regression

1 Abstract— We have introduced a novel framework for2 realization of Adaptive Autonomy (AA) in human-automation interaction (HAI) systems, as well as several expert system realizations of that. This study presents an expert system for realization of AA, using logistic regression (LR), referred to as Adaptive Autonomy Logistic Regression Expert System (AALRES). The proposed system prescribes proper Levels of Automation (LOAs) for various environmental conditions, here modeled as Performance Shaping Factors (PSFs), based on the extracted rules from the experts’ judgments. LR is used as the expert system's inference engine. The practical list of PSFs and the judgments of GTEDC’s (the Greater Tehran Electric Distribution Company) experts are used as expert system database. The results of implementing AALRES to GTEDC’s network are evaluated against the exact predictions of the presented expert system. Evaluations show that AALRES can predict the proper LOA for GTEDC’s Utility Management Automation (UMA) system, which change according to changes in PSFs; thus providing an adaptive LOA scheme for UMA