A multiple model approach for predictive control of nonlinear hybrid systems

This paper presents modeling and control of nonlinear hybrid systems using multiple linearized models. Each linearized model is a local representation of all locations of the hybrid system. These models are then combined using Bayes theorem to describe the nonlinear hybrid system. The multiple models, which consist of continuous as well as discrete variables, are used for synthesis of a model predictive control (MPC) law. The discrete-time equivalent of the model predicts the hybrid system behavior over the prediction horizon. The MPC formulation takes on a similar form as that used for control of a continuous variable system. Although implementation of the control law requires solution of an online mixed integer nonlinear program, the optimization problem has a fixed structure with certain computational advantages. We demonstrate performance and computational efficiency of the modeling and control scheme using simulations on a benchmark three-spherical tank system and a hydraulic process plant.     

A gap metric based multiple model approach for nonlinear switched systems

Many applications in chemical engineering often exhibit a switching character due to the presence of discrete modes in the course of their operation. First principles models of such systems constructed using process simulators are far too complex for use in online applications, especially in model-based control. For such systems, numerous control-relevant modeling approaches have been reported in the literature such as mixed logic dynamical (MLD) models [1] and piece wise affine (PWA) [2] models among others. These models describe the evolution of states in each discrete mode using linear equations. Fewer control-relevant models have been reported that address the nonlinear behavior of switched systems. To model nonlinear hybrid systems, Nandola and Bhartiya [3] proposed a multiple linear model approach wherein multiple linear models are used to describe the dynamic behavior in each mode of the hybrid system. However, no guidelines were provided to select the number of models necessary in each mode and their region of validity. In this work, we address these lacunae by presenting a systematic multiple model approach to describe nonlinear switched systems. The method involves a trajectory based linearization and employs a model bank with a set of local linear models for each discrete operational mode. The model bank is generated by linearizing the first principles model across a carefully designed trajectory based on accuracy of multi-step ahead predictions. The numerous models thus obtained are clustered using the gap metric as the distance measure and representative models are selected. The selected linear models are aggregated using Bayesian or Fuzzy approaches to obtain the global model for the nonlinear switched system. A simulation case study of spherical two-tank system and an experimental case study of a benchmark problem consisting of three tanks are used to validate the proposed modeling strategy.     

Context-based person identification framework for smart video surveillance

Smart video surveillance (SVS) applications enhance situational awareness by allowing domain analysts to focus on the events of higher priority. SVS approaches operate by trying to extract and interpret higher “semantic” level events that occur in video. One of the key challenges of SVS is that of person identification where the task is for each subject that occurs in a video shot to identify the person it corresponds to. The problem of person identification is especially challenging in resource-constrained environments where transmission delay, bandwidth restriction, and packet loss may prevent the capture of high-quality data. Conventional person identification approaches which primarily are based on analyzing facial features are often not sufficient to deal with poor-quality data. To address this challenge, we propose a framework that leverages heterogeneous contextual information together with facial features to handle the problem of person identification for low-quality data. We first investigate the appropriate methods to utilize heterogeneous context features including clothing, activity, human attributes, gait, people co-occurrence, and so on. We then propose a unified approach for person identification that builds on top of our generic entity resolution framework called RelDC, which can integrate all these context features to improve the quality of person identification. This work thus links one well-known problem of person identification from the computer vision research area (that deals with video/images) with another well-recognized challenge known as entity resolution from the database and AI/ML areas (that deals with textual data). We apply the proposed solution to a real-world dataset consisting of several weeks of surveillance videos. The results demonstrate the effectiveness and efficiency of our approach even on low-quality video data.     

Specification and encoding of transaction interaction properties

Transaction-level modeling is used in hardware design for describing designs at a higher level compared to the register-transfer level (RTL) (e.g. Cai and Gajski in CODES+ISSS ’03: proceedings of the 1st IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis, pp. 19–24, 2003; Chen et al. in FMCAD ’07: proceedings of the formal methods in computer aided design, pp. 53–61, 2007; Mahajan et al. in MEMOCODE ’07: proceedings of the 5th IEEE/ACM international conference on formal methods and models for codesign, pp. 123–132, 2007; Swan in DAC ’06: proceedings of the 43rd annual conference on design automation, pp. 90–92, 2006). Each transaction represents a unit of work, which is also a useful unit for design verification. In such models, there are many properties of interest which involve interactions between multiple transactions. Examples of this are ordering relationships in sequential processing and hazard checking in pipelined circuits. Writing such properties on the RTL design requires significant expertise in understanding the higher-level computation being done in a given RTL design and possible instrumentation of the RTL to express the property of interest. This is a barrier to the easy use of such properties in RTL designs.     

Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach

It is costly and takes a lot of time for disaster employees to execute several evacuation drills for a building. One cannot glean information to advance the plan and blueprint of forthcoming buildings without executing many drills. We have developed a multi-agent system simulation application to aid in running several evacuation drills and theoretical situations. This paper combines the genetic algorithm (GA) with neural networks (NNs) and fuzzy logic (FL) to explore how intelligent agents can learn and adapt their behavior during an evacuation. The adaptive behavior focuses on the specific agents changing their behavior in the environment. The shared behavior of the agent places an emphasis on the crowd-modeling and emergency behavior in the multi-agent system. This paper provides a fuzzy individual model being developed for realistic modeling of human emotional behavior under normal and emergency conditions. It explores the impact of perception and emotions on the human behavior. We have established a novel intelligent agent with characteristics such as independence, collective ability, cooperativeness, and learning, which describes its final behavior. The contributions of this paper lie in our approach of utilizing a GA, NNs, and FL to model learning and adaptive behavior of agents in a multi-agent system. The planned application will help in executing numerous evacuation drills for what-if scenarios for social and cultural issues such as evacuation by integrating agent characteristics. This paper also compares our proposed multi-agent system with existing commercial evacuation tools as well as real-time evacuation drills for accuracy, building traffic characteristics, and the cumulative number of people exiting during evacuation. Our results show that the inclusion of GA, NNs, and fuzzy attributes made the evacuation time of the agents closer to the real-time evacuation drills.     

Nonlinear inferential multi-rate control of Kappa number at multiple locations in a continuous pulp digester

The continuous pulp digester represents a large-scale, distributed parameter system. Control of the spatial profile of degree of cooking, characterized by the Kappa number, rather than its endpoint value can effectively control properties that are dependent on the history of cooking. However, profile control of such large-scale distributed parameter systems throws up new challenges in estimation and control. We design a nonlinear model predictive controller using a multi-rate extended Kalman filter to infer and control discrete points along the Kappa number profile. Both, the plant and controller models are based on first principles. The design is tested for significant mismatches in parameters, initial state errors, and stochastic disturbances in the entering wood composition.     

Overcoming Heterogeneity and Autonomy in Multidatabase Systems

A multidatabase system (MDBS) is a software system for integration of preexisting and independent local database management systems (DBMSs). The transaction management problem in MDBSs consists of designing appropriate software, on top of local DBMSs, such that users can execute transactions that span multiple local DBMSs without jeopardizing database consistency. The difficulty in transaction management in MDBSs arises due to the heterogeneity of the transaction management algorithms used by the local DBMSs, and the desire to preserve their local autonomy. In this paper, we develop a framework for designing fault-tolerant transaction management algorithms for MDBS environments that effectively overcomes the heterogeneity- and autonomy-induced problems. The developed framework builds on our previous work. It uses the approach described in S. Mehrotra et al. (1992, in “Proceedings of ACM–SIGMOD 1992 International Conference on Management of Data, San Diego, CA”) to overcome the problems in ensuring serializability that arise due to heterogeneity of the local concurrency control protocols. Furthermore, it uses a redo approach to recovery for ensuring transaction atomicity (Y. Breitbart et al., 1990, in “Proceedings of ACM–SIGMOD 1990 International Conference on Management of Data, Atlantic City, NJ;” Mehrotra et al., 1992, in “Proceedings of the Eleventh ACM SIGACT–SIGMOD–SIGART Symposium on Principles of Database Systems, San Diego, CA;” and A. Wolski and J. Veijalainen, 1990, in “Proceedings of the International Conference on Databases, Parallel Architectures and Their Applications”, pp. 321–330), that strives to ensure atomicity of transactions without the usage of the 2PC protocol. We reduce the task of ensuring serializability in MDBSs in the presence of failures to solving three independent subproblems, solutions to which together constitute a complete strategy for failure-resilient transaction management in MDBS environments. We develop mechanisms with which each of the three subproblems can be solved without requiring any changes be made to the preexisting software of the local DBMSs and without compromising their autonomy.     

Validation of RF power standard in the frequency range of 100 MHz to 18 GHz by an inter laboratory data comparison

Radio frequency (RF) power is one of the most important quantities in RF metrology. An inter laboratory data comparison of RF power for the validation of a coaxial microcalorimeter has been carried out between Physikalisch-Technische Bundesanstalt (PTB-Germany) and National Physical Laboratory India (NPLI). A coaxial thermistor mount equipped with type N connector is used for this intercomparison. The results show good agreement in measuring the effective efficiency of the coaxial thermistor mount between the two laboratories within their claimed expanded uncertainty. It confirms the equivalence of national standards for RF power in the frequency range of 100 MHz to 18 GHz.     

Development of Integrated Sediment Rating Curves Using ANNs

Correct estimation of sediment volume being carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. Artificial neural networks (ANNs) are a simplied mathematical representation of the functioning of the human brain. Three-layer feed-forward ANNs have been shown to be a powerful tool for input-output mapping and have been widely used in water resources problems. The ANN approach is used to establish an integrated stage-discharge-sediment concentration relation for two sites on the Mississippi River. Based on the comparison of the results for two gauging sites, it is shown that the ANN results are much closer to the observed values than the conventional technique.     

Instruction level power analysis and optimization of software

The increasing popularity of power constrained mobile computers and embedded computing applications drives the need for analyzing and optimizing power in all the components of a system. Software constitutes a major component of today's systems, and its role is projected to grow even further. Thus, an ever increasing portion of the functionality of today's systems is in the form of instructions, as opposed to gates. This motivates the need for analyzing power consumption from the point of view of instructions—something that traditional circuit and gate level power analysis tools are inadequate for. This paper describes an alternative, measurement based instruction level power analysis approach that provides an accurate and practical way of quantifying the power cost of soft-ware. This technique has been applied to three commercial, architecturally different processors. The salient results of these analyses are summarized. Instruction level analysis of a processor helps in the development of models for power consumption of software executing on that processor. The power models for the subject processors are described and interesting observations resulting from the comparison of these models are highlighted. The ability to evaluate software in terms of power consumption makes it feasible to seach fow low power implementations of given programs. In addition, it can guide the development of general tools and techniques for low power software. Several ideas in this regard as motivated by the power analysis of the subject processors are also described.