Tariq Samad

Imputation of Missing Data in Industrial Databases

A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records. But every cell in a database is not an independent datum. Statistical relationships will constrain and, often determine, missing values. Data imputation, the filling in of missing values for partially missing data, can thus be an invaluable first step in many IDA projects. New imputation methods that can handle the large-scale problems and large-scale sparsity of industrial databases are needed. To illustrate the incomplete database problem, we analyze one database with instrumentation maintenance and test records for an industrial process. Despite regulatory requirements for process data collection, this database is less than 50% complete. Next, we discuss possible solutions to the missing data problem. Several approaches to imputation are noted and classified into two categories: data-driven and model-based. We then describe two machine-learning-based approaches that we have worked with. These build upon well-known algorithms: AutoClass and C4.5. Several experiments are designed, all using the maintenance database as a common test-bed but with various data splits and algorithmic variations. Results are generally positive with up to 80% accuracies of imputation. We conclude the paper by outlining some considerations in selecting imputation methods, and by discussing applications of data imputation for intelligent data analysis.

Network-Centric Systems for Military Operations in Urban Terrain: The Role of UAVs

Military systems are the motivational driver for much of the technology development conducted at applied research laboratories around the world. As the needs of the worlds militaries change, so does the focus of this research and development. In this paper, we discuss how the fundamental characteristics of military operations in urban terrain (MOUT) impose requirements and constraints on sensing and reconnaissance. We highlight the importance of a new class of small unmanned aerial vehicles (UAVs) for network-centric military urban operations. We review some of the UAVs that have been developed in recent years, and that are under development, with particular attention to their endurance, portability, performance, payload, and communication capabilities. Selected university testbeds are also briefly noted. Over the last few years there has been considerable research focused on how these small UAVs, both individually and collectively, can operate autonomously in urban environments and help capture and communicate needed information. We discuss some of this research; specific topics covered include guidance and control for autonomous operation, multi-UAV coordination and route optimization, and ad-hoc networking with UAV nodes. A new concept of operations is described that relies on coordination and control of a heterogeneous suite of small UAVs for surveillance and reconnaissance operations in urban terrain

Smart grid technologies and applications for the industrial sector

Abstract Smart grids have become a topic of intensive research, development, and deployment across the world over the last few years. The engagement of consumer sectors—residential, commercial, and industrial—is widely acknowledged as crucial for the projected benefits of smart grids to be realized. Although the industrial sector has traditionally been involved in managing power use with what today would be considered smart grid technologies, these applications have mostly been one-of-a-kind, requiring substantial customization. Our objective in this article is to motivate greater interest in smart grid applications in industry. We provide an overview of smart grids and of electricity use in the industrial sector. Several smart grid technologies are outlined, and automated demand response is discussed in some detail. Case studies from aluminum processing, cement manufacturing, food processing, industrial cooling, and utility plants are reviewed. Future directions in interoperable standards, advances in automated demand response, energy use optimization, and more dynamic markets are discussed.

Dynamic optimization strategies for three-dimensional conflict resolution of multiple aircraft

Free flight is an emerging paradigm in air traffic management. Conflict detection and resolution is the heart of any free-flight concept. The problem of optimal cooperative three-dimensional conflict resolution involving multiple aircraft is addressed by the rigorous numerical trajectory optimization methods. The conflict problem is posed as an optimal control problem of finding trajectories that minimize a certain objective function while the safe separation between each aircraft pair is maintained. The initial and final positions of the aircraft are known and aircraft models with detailed nonlinear point-mass dynamics are considered. The protection zone around the aircraft is modeled to be cylindrical in shape. A novel formulation of the cylindrical protection zone is proposed by the use of continuous variables. The optimal control problem is converted to a finite dimensional nonlinear program (NLP) by the use of collocation on finite elements. The NLP is solved by the use of an interior point algorithm that incorporates a novel line search method. A reliable initialization strategy that yields a feasible solution on simple models is also proposed and adapted to detailed models. Several resolution scenarios are illustrated. The practical issue of flyability of the generated trajectories is addressed by the ability of our mathematical programming framework to accommodate detailed dynamic models.

Self–organization with partial data

We show how the kohonen self-organizing feature map model can be extended so that partial training data can be utilized. Given input stimuli in which values for some elements or features are absent, the match computation and the weight updates are performed in the input subspace defined by the available values. Three examples, including an application to student modelling for intelligent tutoring systems in which data is inherently incomplete, demonstrate the effectiveness of the extension.

SEPIA. A simulator for electric power industry agents

Over the last several years, a technological convergence-increasing processing power and memory capacities, component software infrastructure developments, and adaptive agent architectures-has made possible the development of a class of simulation and optimization tools that was unimaginable earlier. With careful design, these tools can be easy to use for nonexpert users, and they can provide a level of decision support and insight that can potentially have tremendous impact in terms of problem understanding and performance. This technology is particularly relevant to the electricity enterprise as it undertakes the difficult transition toward deregulation and open competition. Our current work, as realized in SEPIA, constitutes little more than a first step in this context, but we hope it can serve as an initial demonstration of the power of a complex adaptive systems approach for challenging practical applications.

Intelligent optimal control with dynamic neural networks

The application of neural networks technology to dynamic system control has been constrained by the non-dynamic nature of popular network architectures. Many of difficulties are-large network sizes (i.e. curse of dimensionality), long training times, etc. These problems can be overcome with dynamic neural networks (DNN). In this study, intelligent optimal control problem is considered as a nonlinear optimization with dynamic equality constraints, and DNN as a control trajectory priming system. The resulting algorithm operates as an auto-trainer for DNN (a self-learning structure) and generates optimal feed-forward control trajectories in a significantly smaller number of iterations. In this way, optimal control trajectories are encapsulated and generalized by DNN. The time varying optimal feedback gains are also generated along the trajectory as byproducts. Speeding up trajectory calculations opens up avenues for real-time intelligent optimal control with virtual global feedback. We used direct-descent-curvature algorithm with some modifications (we called modified-descend-controller-MDC algorithm) for the optimal control computations. The algorithm has generated numerically very robust solutions with respect to conjugate points. The adjoint theory has been used in the training of DNN which is considered as a quasi-linear dynamic system. The updating of weights (identification of parameters) are based on Broyden-Fletcher-Goldfarb-Shanno BFGS method. Simulation results are given for an intelligent optimal control system controlling a difficult nonlinear second-order system using fully connected three-neuron DNN.

Software-Enabled Control: Information Technology for Dynamical Systems

Contributors. Preface. Introduction. The Sec Vision (H. Gill & J. Bay). Trends and Technologies For Unmanned Aerial Vehicles (D. Van Cleave). Previewing the Software-Enabled Control Research Portfolio (T. Samad & G. Balas). II: SOFTWARE ARCHITECTURES FOR REAL-TIME CONTROL. Open Control Platform: A Software Platform Supporting Advances in UAV Control Technology (J. Paunicka, et al.). A Prototype Open Control Platform For Reconfigurable Control Systems (L. Wills, et al.). Real-Time Adaptive Resource Management for Multimodel Control (M. Agrawal, et al.). Heterogeneous Modeling and Design of Control Systems (X. Liu, et al.). Embedded Control Systems Development with Giotto (T. Henzinger, et al.). III: ONLINE MODELING AND CONTROL. Online Control Customization Via Optimization-Based Control (R. Murray, et al.). Model Predictive Neural Control For Aggressive Helicop ter Maneuvers (E. Wan, et al.). Active Model Estimation For Complex Autonomous Systems (M. Campbell, et al.). An Intelligent Methodology For Real-Time Adaptive Mode Transitioning and Limit Avoidance of Unmanned Aerial Vehicles (G. Vachtsevanos, et al.). Implementation of Online Control Customization Within the Open Control Platform (R. Bhattacharya & G. Balas). IV: HYBRID DYNAMICAL SYSTEMS. Hybrid Systems: Review and Recent Progress (P. Antsaklis & X. Koutsoukos). A Maneuver-Based Hybrid Control Architecture for Autonomous Vehicle Motion Planning (E. Frazzoli, et al.). Multimodal Control of Constrained Nonlinear Systems (T. Koo, et al.). Towards Fault-Adaptive Control of Complex Dynamical Systems (G. Karsai, et al.). Computational Tools For the Verification of Hybrid Systems (C. Tomlin, et al.). V: CONCLUSIONS. The Outlook For Software-Enabled Control (T. Samad & G. Balas). Index. About the Editors.

Energy management for buildings and microgrids

Intelligent consumer energy management systems will become important elements at the delivery points of the smart grid inside homes, buildings, and industrial plants. The end users will be able to better monitor and manage their energy consumption, while utilities will gain more flexible mechanisms for management of peak demands that will extend beyond demand response initiatives as they are implemented today. With a broader use of distributed generation many buildings and campuses will become microgrids interconnecting multiple generation, storage, and consumption devices of one or several end users. We discuss how energy management and control for such facilities can be viewed as a large-scale optimization problem. Specific supply-side and demand-side aspects include on-site renewable generation, storage technologies, electric cars, dynamic pricing, and load management. Technical challenges related to the optimization formulation are noted - in general, mixed-integer, nonlinear, constrained optimization is needed. We also describe an implementation of optimization-based energy management solution for a hospital in the Netherlands, providing economic details and an analysis of the savings achieved.

A Hierarchical Transactive Control Architecture for Renewables Integration in Smart Grids: Analytical Modeling and Stability

In this paper, we propose a hierarchical transactive control architecture that combines market transactions at the higher levels with inter-area and unit-level control at the lower levels. A model of this architecture is introduced, with dynamics at primary, secondary, and tertiary levels. With a goal of ensuring frequency regulation using optimal allocation of resources in the presence of uncertainties in renewables and load, a hierarchical control methodology is presented. Global asymptotic stability of the overall system is established in the presence of uncertainties at all three time-scales. An IEEE 30-bus as well as simulations of practical examples of realistic sizes are used to validate the approach where it is shown that the proposed control architecture has the potential to reduce cost of reserves and to increase social welfare.