Intelligent adaptive observer-based optimal control of overhead transmission line de-icing robot manipulator

In cold season, wet snow ice accretion on overhead transmission lines increases wind load effects which in turn increases line tension. This increased line tension causes undesirable effects in power systems. This paper discusses the design of an observer-based boundary sliding mode control (BSMC) for 3 DOF overhead transmission line de-icing robot manipulator (OTDIRM). A robust radial basis functional neural network (RBFNN) observer-based neural network (NN) controller is developed for the motion control of OTDIRM, which is a combination of BSMC, NN approximation and adaptation law. The RBFNN-based adaptive observer is designed to estimate the positions and velocities. The weights of both NN observer and NN approximator are tuned off-line using particle swarm optimization. Using Lyapunov analysis the closed loop tracking error was verified for a 3 DOF OTDIRM. Finally, the robustness of the proposed neural network-based adaptive observer boundary sliding mode control (NNAOBSMC) was verified against the input disturbances and uncertainties.     

Universal instability modes in internal and external flows

Two prototypical external and internal flows have been studied which display linear temporal instability followed by nonlinear saturation taking the flows to a new equilibrium state. Direct simulation results are obtained using a specific formulation and numerical methods with very high accuracy. These results are analyzed via proper orthogonal decomposition (POD), which reveal similar modes for flow past a circular cylinder and flow inside a lid-driven cavity, indicating universality of such modes. Unlike many other efforts on reduced order modeling via POD, here the emphasis has been on understanding the physical aspect of the flow instability which requires very high accuracy of the simulation. Then, the obtained POD modes are related to the instability modes (in the classical sense of defining the latter) and new generic types of instability modes are identified in the studied external and internal flows. These new modes have been reported for flow past a circular cylinder [Sengupta TK, Singh N, Suman VK. Dynamical system approach to instability of flow past a circular cylinder. J Fluid Mech 2010;656:82–115] which help one in understanding the instability sequence and the relative importance of these modes in the flow evolution starting from an impulsive start. Present comparative study, furthermore, reveals universality of such instability modes by showing their presence for the flow inside a lid-driven cavity as well. Despite seeming dissimilarities between these two flows, similarities between the instability portrait of these two flows suggest universality of such modes. From the equilibrium amplitude of vorticity time-series, we establish the presence of multiple modes and multiple bifurcation sequences for these flows in parameter space. Existing theory due to Landau and Stuart that considers only a single dominant mode and its self-interaction does not explain all these features. We invoke a multi-modal interaction model in the cited reference above, termed as Landau–Stuart–Eckhaus (LSE) equation in recognition of Eckhaus’ work in modifying the classical Stuart–Landau equation. We also show that the new instability modes do not follow either the classical Stuart–Landau or the newly proposed LSE model equations and for this reason we call these as anomalous modes. Two specific classes of anomalous modes are identified and classified in the present work. Empirical expressions for the evolution of these anomalous modes are presented and their unambiguous role during instabilities is discussed.     

Timestamping messages and events in a distributed system using synchronous communication

Determining order relationship between events of a distributed computation is a fundamental problem in distributed systems which has applications in many areas including debugging, visualization, checkpointing and recovery. Fidge/Mattern’s vector-clock mechanism captures the order relationship using a vector of size N in a system consisting of N processes. As a result, it incurs message and space overhead of N integers. Many distributed applications use synchronous messages for communication. It is therefore natural to ask whether it is possible to reduce the timestamping overhead for such applications. In this paper, we present a new approach for timestamping messages and events of a synchronously ordered computation, that is, when processes communicate using synchronous messages. Our approach depends on decomposing edges in the communication topology into mutually disjoint edge groups such that each edge group either forms a star or a triangle. We show that, to accurately capture the order relationship between synchronous messages, it is sufficient to use one component per edge group in the vector instead of one component per process. Timestamps for events are only slightly bigger than timestamps for messages. Many common communication topologies such as ring, grid and hypercube can be decomposed into edge groups, resulting in almost 50% improvement in both space and communication overheads. We prove that the problem of computing an optimal edge decomposition of a communication topology is NP-complete in general. We also present a heuristic algorithm for computing an edge decomposition whose size is within a factor of two of the optimal. We prove that, in the worst case, it is not possible to timestamp messages of a synchronously ordered computation using a vector containing fewer than components when N ≥ 2. Finally, we show that messages in a synchronously ordered computation can always be timestamped in an offline manner using a vector of size at most . An earlier version of this paper appeared in 2002 Proceedings of the IEEE International Conference on Distributed Computing Systems (ICDCS). The author V. K. Garg was supported in part by the NSF Grants ECS-9907213, CCR-9988225, an Engineering Foundation Fellowship. This work was done while the author C. Skawratananond was a Ph.D. student at the University of Texas at Austin.     

Efficient computation of Morse-Smale complexes for three-dimensional scalar functions

The Morse-Smale complex is an efficient representation of the gradient behavior of a scalar function, and critical points paired by the complex identify topological features and their importance. We present an algorithm that constructs the Morse-Smale complex in a series of sweeps through the data, identifying various components of the complex in a consistent manner. All components of the complex, both geometric and topological, are computed, providing a complete decomposition of the domain. Efficiency is maintained by representing the geometry of the complex in terms of point sets.     

Residual phosphate fertilizer compounds in soils

A variety of P compounds can accumulate in soils as residues of fertilizer and may influence soil test versus plant yield relationships. This work evaluates specific chemical extractants for their capacity to identify such Al, Fe and Ca phosphates in soils as a basis for increasing the precision of yield prediction. Aluminium phosphate, iron phosphate, calcium phosphate (apatite) and P sorbed onto gibbsite, goethite and calcite were added to four Western Australian lateritic soils. These soils were then subjected to sequential selective extraction using a modified Chang and Jackson procedure in order to evaluate the selectivity of these extractants for the different forms of P with the sequence of extraction: 1 M NH₄Cl, 0.5 M NH₄F, 0.1 M NaOH + 1 M NaCl, citrate-dithionite-bicarbonate (CDB), 1 M NaOH and 1 M HCl. The results show that the procedure is not sufficiently specific and thus might be of little value for estimating the forms and amounts of residues of phosphate rock fertilizers in soils.     

Uncertainty visualization using HDR volume rendering

In this paper, we explore a novel idea of using high dynamic range (HDR) technology for uncertainty visualization. We focus on scalar volumetric data sets where every data point is associated with scalar uncertainty. We design a transfer function that maps each data point to a color in HDR space. The luminance component of the color is exploited to capture uncertainty. We modify existing tone mapping techniques and suitably integrate them with volume ray casting to obtain a low dynamic range (LDR) image. The resulting image is displayed on a conventional 8-bits-per-channel display device. The usage of HDR mapping reveals fine details in uncertainty distribution and enables the users to interactively study the data in the context of corresponding uncertainty information. We demonstrate the utility of our method and evaluate the results using data sets from ocean modeling.     

Effect of temperature and α-irradiation on gas permeability for polymeric membrane

In the present study the polyethersulphone (PES) membranes of thickness (35 ±2) μm were prepared by solution cast method. The permeability of these membranes was calculated by varying the temperature and by irradiation of α ions. For the variation of temperature, the gas permeation cell was dipped in a constant temperature water bath in the temperature range from 303–373 K, which is well below the glass transition temperature (498 K). The permeability of H₂ and CO₂ increased with increasing temperature. The PES membrane was exposed by a-source (₉₅Am₂₄₁) of strength (1 μ Ci) in vacuum of the order of 10⁻⁶ torr, with fluence 2.7 × 10⁷ ions/cm². The permeability of H₂ and CO₂ has been observed for irradiated membrane with increasing etching time. The permeability increases with increasing etching time for both gases. There was a sudden change in permeability for both the gases when observed at 18 min etching. At this stage the tracks are visible with optical instrument, which confirms that the pores are generated. Most of pores seen in the micrograph are circular cross-section ones.     

Linear stochastic approximation driven by slowly varying Markov chains

We study a linear stochastic approximation algorithm that arises in the context of reinforcement learning. The algorithm employs a decreasing step-size, and is driven by Markov noise with time-varying statistics. We show that under suitable conditions, the algorithm can track the changes in the statistics of the Markov noise, as long as these changes are slower than the rate at which the step-size of the algorithm goes to zero.     

A flexible and recoverable client/server database event notification system

A software architecture is presented that allows client application programs to interact with a DBMS server in a flexible and powerful way, using either direct, volatile messages, or messages sent via recoverable queues. Normal requests from clients to the server and replies from the server to clients can be transmitted using direct or recoverable messages. In addition, an application event notification mechanism is provided, whereby client applications running anywhere on the network can register for events, and when those events are raised, the clients are notified. A novel parameter passing mechanism allows a set of tuples to be included in an event notification. The event mechanism is particularly useful in an active DBMS, where events can be raised by triggers to signal running application programs. Received July 21, 1995 / Accepted May 30, 1996     

Look Left, Look Right, Look Left Again: An Application of Fractal Symbolic Analysis to Linear Algebra Code Restructuring

Fractal symbolic analysis is a symbolic analysis technique for verifying the legality of program transformations. It is strictly more powerful than dependence analysis; for example, it can be used to verify the legality of blocking LU factorization with pivoting, a task for which dependence analysis is inadequate. In this paper, we show how fractal symbolic analysis can be used to convert between left- and right-looking versions of three kernels of central importance in computational science: triangular solve, Cholesky factorization, and LU factorization with pivoting.