Solving Dynamic Constraint Single Objective Functions using a Nature Inspired Technique

We present in this paper a new algorithm based on Particle Swarm Optimization (PSO) for solving Dynamic Single Objective Constrained Optimization (DCOP) problems. We have modified several different parameters of the original particle swarm optimization algorithm by introducing new types of particles for local search and to detect changes in the search space. The algorithm is tested with a known benchmark set and compare with the results with other contemporary works. We demonstrate the convergence properties by using convergence graphs and also the illustrate the changes in the current benchmark problems for more realistic correspondence to practical real world problems.

Optimal control analysis of the dynamic growth behavior of microorganisms

Understanding the growth behavior of microorganisms using modeling and optimization techniques is an active area of research in the fields of biochemical engineering and systems biology. In this paper, we propose a general modeling framework, based on Monad model, to model the growth of microorganisms. Utilizing the general framework, we formulate an optimal control problem with the objective of maximizing a long-term cellular goal and solve it analytically under various constraints for the growth of microorganisms in a two substrate batch environment. We investigate the relation between long term and short term cellular goals and show that the objective of maximizing cellular concentration at a fixed final time is equivalent to maximization of instantaneous growth rate. We then establish the mathematical connection between the generalized framework and optimal and cybernetic modeling frameworks and derive generalized governing dynamic equations for optimal and cybernetic models. We finally illustrate the influence of various constraints in the cybernetic modeling framework on the optimal growth behavior of microorganisms by solving several dynamic optimization problems using genetic algorithms. (C) 2014 Published by Elsevier Inc.

Compiler/Runtime Framework for Dynamic Dataflow Parallelization of Tiled Programs

Task-parallel languages are increasingly popular. Many of them provide expressive mechanisms for intertask synchronization. For example, OpenMP 4.0 will integrate data-driven execution semantics derived from the StarSs research language. Compared to the more restrictive data-parallel and fork-join concurrency models, the advanced features being introduced into task-parallelmodels in turn enable improved scalability through load balancing, memory latency hiding, mitigation of the pressure on memory bandwidth, and, as a side effect, reduced power consumption. In this article, we develop a systematic approach to compile loop nests into concurrent, dynamically constructed graphs of dependent tasks. We propose a simple and effective heuristic that selects the most profitable parallelization idiom for every dependence type and communication pattern. This heuristic enables the extraction of interband parallelism (cross-barrier parallelism) in a number of numerical computations that range from linear algebra to structured grids and image processing. The proposed static analysis and code generation alleviates the burden of a full-blown dependence resolver to track the readiness of tasks at runtime. We evaluate our approach and algorithms in the PPCG compiler, targeting OpenStream, a representative dataflow task-parallel language with explicit intertask dependences and a lightweight runtime. Experimental results demonstrate the effectiveness of the approach.

Adaptive Flight-Control Design Using Neural-Network-Aided Optimal Nonlinear Dynamic Inversion

A neural-network-aided nonlinear dynamic inversion-based hybrid technique of model reference adaptive control flight-control system design is presented in this paper. Here, the gains of the nonlinear dynamic inversion-based flight-control system are dynamically selected in such a manner that the resulting controller mimics a single network, adaptive control, optimal nonlinear controller for state regulation. Traditional model reference adaptive control methods use a linearized reference model, and the presented control design method employs a nonlinear reference model to compute the nonlinear dynamic inversion gains. This innovation of designing the gain elements after synthesizing the single network adaptive controller maintains the advantages that an optimal controller offers, yet it retains a simple closed-form control expression in state feedback form, which can easily be modified for tracking problems without demanding any a priori knowledge of the reference signals. The strength of the technique is demonstrated by considering the longitudinal motion of a nonlinear aircraft system. An extended single network adaptive control/nonlinear dynamic inversion adaptive control design architecture is also presented, which adapts online to three failure conditions, namely, a thrust failure, an elevator failure, and an inaccuracy in the estimation of C-M alpha. Simulation results demonstrate that the presented adaptive flight controller generates a near-optimal response when compared to a traditional nonlinear dynamic inversion controller.

Iterated stochastic filters with additive updates for dynamic system identification: Annealing-type iterations and the filter bank

A nonlinear stochastic filtering scheme based on a Gaussian sum representation of the filtering density and an annealing-type iterative update, which is additive and uses an artificial diffusion parameter, is proposed. The additive nature of the update relieves the problem of weight collapse often encountered with filters employing weighted particle based empirical approximation to the filtering density. The proposed Monte Carlo filter bank conforms in structure to the parent nonlinear filtering (Kushner-Stratonovich) equation and possesses excellent mixing properties enabling adequate exploration of the phase space of the state vector. The performance of the filter bank, presently assessed against a few carefully chosen numerical examples, provide ample evidence of its remarkable performance in terms of filter convergence and estimation accuracy vis-a-vis most other competing filters especially in higher dimensional dynamic system identification problems including cases that may demand estimating relatively minor variations in the parameter values from their reference states. (C) 2014 Elsevier Ltd. All rights reserved.

Influence of dynamic operating conditions on the performance of metal hydride based solid sorption cooling systems

The performance of metal hydride based solid sorption cooling systems depends on the driving pressure differential, and the rate of hydrogen transfer between coupled metal hydride beds during cooling and regeneration processes. Conventionally, the mid-plateau pressure difference obtained from `static' equilibrium PCT data are used for the thermodynamic analysis. It is well known that the processes are `dynamic' because the pressure and temperature, and hence the concentration of the hydride beds, are continuously changing. Keeping this in mind, the pair of La0.9Ce0.1Ni5 - LaNi4.7Al0.3 metal hydrides suitable for solid sorption cooling systems were characterised using both static and dynamic methods. It was found that the PCT characteristics, and the resulting enthalpy (Delta H) and entropy (Delta S) values, were significantly different for static and dynamic modes of measurements. In the present study, the solid sorption metal hydride cooling system is analysed taking in to account the actual variation in the pressure difference (Delta P) and the dynamic enthalpy values. Compared to `static' property based analysis, significant decrease in the driving potentials and transferrable amounts of hydrogen, leading to decrease in cooling capacity by 57.8% and coefficient of performance by 31.9% are observed when dynamic PCT data at the flow rate of 80 ml/min are considered. Copyright 2014 (C) Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

Trace Driven Dynamic Deadlock Detection and Reproduction

Dynamic analysis techniques have been proposed to detect potential deadlocks. Analyzing and comprehending each potential deadlock to determine whether the deadlock is feasible in a real execution requires significant programmer effort. Moreover, empirical evidence shows that existing analyses are quite imprecise. This imprecision of the analyses further void the manual effort invested in reasoning about non-existent defects. In this paper, we address the problems of imprecision of existing analyses and the subsequent manual effort necessary to reason about deadlocks. We propose a novel approach for deadlock detection by designing a dynamic analysis that intelligently leverages execution traces. To reduce the manual effort, we replay the program by making the execution follow a schedule derived based on the observed trace. For a real deadlock, its feasibility is automatically verified if the replay causes the execution to deadlock. We have implemented our approach as part of WOLF and have analyzed many large (upto 160KLoC) Java programs. Our experimental results show that we are able to identify 74% of the reported defects as true (or false) positives automatically leaving very few defects for manual analysis. The overhead of our approach is negligible making it a compelling tool for practical adoption.

Fabrication and Experimentation of a Cantilever Beam Based Piezoelectric Actuator and Sensor for Vibration Energy Harvesting

In the immediate surroundings of our daily life, we can find a lot of places where the energy in the form of vibration is being wasted. Therefore, we have enormous opportunities to utilize the same. Piezoelectric character of matter enables us to convert this mechanical vibration energy into electrical energy which can be stored and used to power other device, instead of being wasted. This work is done to realize both actuator and sensor in a cantilever beam based on piezoelectricity. The sensor part is called vibration energy harvester. The numerical analyses were performed for the cantilever beam using the commercial package ANSYS and MATLAB. The cantilever beam is realized by taking a plate and fixing its one end between two massive plates. Two PZT patches were glued to the beam on its two faces. Experiments were performed using data acquisition system (DAQ) and LABVIEW software for actuating and sensing the vibration of the cantilever beam.

Effects of temperature and clay content on water absorption characteristics of modified MMT clay/cyclic olefin copolymer nanocomposite films: Permeability, dynamic mechanical properties and the encapsulated organic device performance

In the present study, amino-silane modified layered organosilicates were used to reinforce cyclic olefin copolymer to enhance the thermal, mechanical and moisture impermeable barrier properties. The optimum clay loading (4%) in the nanocomposite increases the thermal stability of the film while further loading decreases film stability. Water absorption behavior at 62 degrees C was carried out and compared with the behavior at room temperature and 48 degrees C. The stiffness of the matrix increases with clay content and the recorded strain to failure for the composite films was lower than the neat film. Dynamic mechanical analysis show higher storage modulus and low loss modulus for 2.5-4 wt% clay loading. Calcium degradation test and device encapsulation also show the evidence of optimum clay loading of 4 wt% for improved low water vapor transmission rates compared to other nanocomposite films. (C) 2014 Elsevier Ltd. All rights reserved.

Bayesian parameter identification in dynamic state space models using modified measurement equations

When Markov chain Monte Carlo (MCMC) samplers are used in problems of system parameter identification, one would face computational difficulties in dealing with large amount of measurement data and (or) low levels of measurement noise. Such exigencies are likely to occur in problems of parameter identification in dynamical systems when amount of vibratory measurement data and number of parameters to be identified could be large. In such cases, the posterior probability density function of the system parameters tends to have regions of narrow supports and a finite length MCMC chain is unlikely to cover pertinent regions. The present study proposes strategies based on modification of measurement equations and subsequent corrections, to alleviate this difficulty. This involves artificial enhancement of measurement noise, assimilation of transformed packets of measurements, and a global iteration strategy to improve the choice of prior models. Illustrative examples cover laboratory studies on a time variant dynamical system and a bending-torsion coupled, geometrically non-linear building frame under earthquake support motions. (C) 2015 Elsevier Ltd. All rights reserved.

PriDyn: Enabling Differentiated I/O Services in Cloud Using Dynamic Priorities

Virtualization is one of the key enabling technologies for Cloud computing. Although it facilitates improved utilization of resources, virtualization can lead to performance degradation due to the sharing of physical resources like CPU, memory, network interfaces, disk controllers, etc. Multi-tenancy can cause highly unpredictable performance for concurrent I/O applications running inside virtual machines that share local disk storage in Cloud. Disk I/O requests in a typical Cloud setup may have varied requirements in terms of latency and throughput as they arise from a range of heterogeneous applications having diverse performance goals. This necessitates providing differential performance services to different I/O applications. In this paper, we present PriDyn, a novel scheduling framework which is designed to consider I/O performance metrics of applications such as acceptable latency and convert them to an appropriate priority value for disk access based on the current system state. This framework aims to provide differentiated I/O service to various applications and ensures predictable performance for critical applications in multi-tenant Cloud environment. We demonstrate through experimental validations on real world I/O traces that this framework achieves appreciable enhancements in I/O performance, indicating that this approach is a promising step towards enabling QoS guarantees on Cloud storage.

Thermally switchable metamaterial absorber with a VO2 ground plane

A tri-layer metamaterial absorber, composed of a metal structure/dielectric spacer/vanadium dioxide (VO2) ground plane, is shown to switch reversibly between reflective and absorptive states as a function of temperature. The VO2 film, which changes its conductivity by four orders of magnitude across a insulator-metal transition at about 68 degrees C, enables the switching by forming a resonant absorptive structure at high temperatures while being inactive at low temperatures. The fabricated metamaterial shows a modulation of the reflectivity levels of 58% at a frequency of 22.5 THz and 57% at a frequency of 34.5 THz. (C) 2015 Elsevier B.V. All rights reserved.

Spatio-temporal correlations in aqueous systems: computational studies of static and dynamic heterogeneity by 2D-IR spectroscopy

Local heterogeneity is ubiquitous in natural aqueous systems. It can be caused locally by external biomolecular subsystems like proteins, DNA, micelles and reverse micelles, nanoscopic materials etc., but can also be intrinsic to the thermodynamic nature of the aqueous solution itself (like binary mixtures or at the gas-liquid interface). The altered dynamics of water in the presence of such diverse surfaces has attracted considerable attention in recent years. As these interfaces are quite narrow, only a few molecular layers thick, they are hard to study by conventional methods. The recent development of two dimensional infra-red (2D-IR) spectroscopy allows us to estimate length and time scales of such dynamics fairly accurately. In this work, we present a series of interesting studies employing two dimensional infra-red spectroscopy (2D-IR) to investigate (i) the heterogeneous dynamics of water inside reverse micelles of varying sizes, (ii) supercritical water near the Widom line that is known to exhibit pronounced density fluctuations and also study (iii) the collective and local polarization fluctuation of water molecules in the presence of several different proteins. The spatio-temporal correlation of confined water molecules inside reverse micelles of varying sizes is well captured through the spectral diffusion of corresponding 2D-IR spectra. In the case of supercritical water also, we observe a strong signature of dynamic heterogeneity from the elongated nature of the 2D-IR spectra. In this case the relaxation is ultrafast. We find remarkable agreement between the different tools employed to study the relaxation of density heterogeneity. For aqueous protein solutions, we find that the calculated dielectric constant of the respective systems unanimously shows a noticeable increment compared to that of neat water. However, the `effective' dielectric constant for successive layers shows significant variation, with the layer adjacent to the protein having a much lower value. Relaxation is also slowest at the surface. We find that the dielectric constant achieves the bulk value at distances more than 3 nm from the surface of the protein.

Direct measurements of growing amorphous order and non-monotonic dynamic correlations in a colloidal glass-former

The transformation of flowing liquids into rigid glasses is thought to involve increasingly cooperative relaxation dynamics as the temperature approaches that of the glass transition. However, the precise nature of this motion is unclear, and a complete understanding of vitrification thus remains elusive. Of the numerous theoretical perspectives(1-4) devised to explain the process, random first-order theory (RFOT; refs 2,5) is a well-developed thermodynamic approach, which predicts a change in the shape of relaxing regions as the temperature is lowered. However, the existence of an underlying `ideal' glass transition predicted by RFOT remains debatable, largely because the key microscopic predictions concerning the growth of amorphous order and the nature of dynamic correlations lack experimental verification. Here, using holographic optical tweezers, we freeze a wall of particles in a two-dimensional colloidal glass-forming liquid and provide direct evidence for growing amorphous order in the form of a static point-to-set length. We uncover the non-monotonic dependence of dynamic correlations on area fraction and show that this non-monotonicity follows directly from the change in morphology and internal structure of cooperatively rearranging regions(6,7). Our findings support RFOT and thereby constitute a crucial step in distinguishing between competing theories of glass formation.