Control of a process with unmeasured disturbances that change its steady-state gain sign

Because the sign of the steady-state gain of an industrial NOx reduction unit changes according to the magnitude of the main external disturbance (hydrogen flow rate), designing an appropriate controller is particularly challenging. To address this issue, theoretical analysis suggested that a simple linear controller can provide adequate control for this process. Interestingly, tuning of this controller deviates from well known guidelines of linear control theory, which dictate that closed-loop stability is maintained by sluggish enough control of a stable linear process. It turns out, that for this nonlinear process, controller tuning must be neither too sluggish nor too aggressive, in an intermediate range suggested by a variant of the small-gain theorem proven here for corresponding nonlinear operators. The operator-based analysis was confirmed via computer simulation on a simple first-principles model, calibrated on real plant data. It is expected that control performance and robustness may improve if a number of ideas suggested in the text are explored further.     

Optimal coverage of an infrastructure network using sensors with distance-decaying sensing quality

Motivated by recent applications of wireless sensor networks in monitoring infrastructure networks, we address the problem of optimal coverage of infrastructure networks using sensors whose sensing performance decays with distance. We show that this problem can be formulated as a continuous p-median problem on networks. The literature has addressed the discrete p-median problem   on networks and in continuum domains, and the continuous p$p$-median problem in continuum domains extensively. However, in-depth analysis of the continuous p$p$-median problem on networks has been lacking. With the sensing performance model that decays with distance, each sensor covers a region equivalent to its Voronoi partition on the network in terms of the shortest path distance metric. Using Voronoi partitions, we define a directional partial derivative of the coverage metric with respect to a sensor’s location. We then propose a gradient descent algorithm to obtain a locally optimal solution with guaranteed convergence. The quality of an optimal solution depends on the choice of the initial configuration of sensors. We obtain an initial configuration using two approaches: by solving the discrete p$p$-median problem on a lumped   network and by random sampling. We consider two methods of random sampling: uniform sampling and D²$D^2$-sampling. The first approach with the initial solution of the discrete p$p$-median problem leads to the best coverage performance for large networks, but at the cost of high running time. We also observe that the gradient descent on the initial solution with the D²$D^2$-sampling method yields a solution that is within at most 7% of the previous solution and with much shorter running time.     

Artificial intelligence applications in Permanent Magnet Brushless DC motor drives

Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structure and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous impact on electric motor drives. The brushless DC motor is a multivariable and non-linear system. In conventional PMBLDC drives speed and position sensing of brushless DC motors require high degree of accuracy. Unfortunately, traditional methods of control require detailed modelling of all the motor parameters to achieve this. The Intelligent control techniques like, fuzzy logic control/Neural network control etc. uses heuristic input–output relations to deal with vague and complex situations. This paper presents a literature survey on the intelligent control techniques for PMBLDC motor drives. Various AI techniques for PMBLDC motor drives are described. Attempt is made to provide a guideline and quick reference for the researchers and practicing engineers those are working in the area of PMBLDC motor drives.     

Linearizer for pulse-shaping of received pulse in ultra-wideband radio systems

This paper presents a novel concept of pulse-shaping in an ultra-wide band radio system. Instead of attempting to design an antenna with flat group delay, the emphasis has been shifted to the signal processing aspect of pulse shaping. The received pulse which has broadened due to non-linear phase response of the propagating channel is shaped back closer to the ideal pulse using a linearizer. The block schematic of the linearizer is presented along with simulated results. Though only a Rayleigh first order pulse has been used for simulation purpose, the method can easily be generalized to incorporate other pulse shapes.     

Repeated detection of conjunctive predicates in distributed executions

Given a conjunctive predicate ? over a distributed execution, this paper gives an algorithm to detect all interval sets, each interval set containing one interval per process, in which the local values satisfy the Definitely(?) modality. The time complexity of the algorithm is O(n³p), where n is the number of processes and p is the bound on the number of times a local predicate becomes true at any process. The paper also proves that unlike the Possibly(?) modality which admits O(pn) solution interval sets, the Definitely(?) modality admits O(np) solution interval sets. The paper also gives an on-line test to determine whether all solution interval sets can be detected in polynomial time under arbitrary fine-grained causality-based modality specifications.     

Reliable before-fabrication forecasting of MEMS piezoresistive pressure sensor: mathematical modelling and numerical simulation

Microsystem Technologies - Micro-mechanical systems (MEMS) based piezoresistive pressure sensors have significant importance in several pressure sensor devices in real world, i.e., aviation, IoT...     

Pervasive Personal Computing in an Internet Suspend/Resume System

The Internet suspend/resume model of mobile computing cuts the tight binding between PC state and PC hardware. By layering a virtual machine on distributed storage, ISR lets the VM encapsulate execution and user customization state; distributed storage then transports that state across space and time. This article explores the implications of ISR for an infrastructure-based approach to mobile computing. It reports on experiences with three versions of ISR and describes work in progress toward the OpenISR version     

Effect of bandwidth modifications on the quality of speech imitated by Alexandrine and Indian Ringneck parrots

Alexandrine and Indian Ringneck parrots are known for imitating the voice of other animals. The objective of this paper is to estimate the spectral limits of the imitated sounds produced by parrots and quantify the quality. The investigations showed that 500–3000 Hz spectral band is adequate for retaining the important perceptual information in the phrases uttered by human speakers and imitated by parrots. Investigations confirmed that the Indian Ringneck parrots are capable of following the formant structure and pitch contour of the phrases uttered by the human subjects. The dynamic range of the pitch of Indian Ringneck parrots was observed as higher than that of the human subjects. A rise of about 1000 Hz in the formant F1 of the parrots was observed, indicating the tongue height small and beak opening, relatively large, as compared to that of human subjects. The quality of some of the synthesized and processed phrases was found slightly better as compared to that of the original phrases because of the inherent enhancement capability of the Harmonic plus noise model (HNM). The average Mean opinion score (MOS) score of the Indian Ringnech parrots for the original, synthesized, and processed phrases was observed as 2.65, 2.59, and 2.77, respectively. The investigations may be beneficial for studying the behavior of endangered birds, defense related activities, safeguarding the crashes with aero planes, and safeguard of the birds from wind power generator etc.     

DECODE: a new method for discovering clusters of different densities in spatial data

When clusters with different densities and noise lie in a spatial point set, the major obstacle to classifying these data is the determination of the thresholds for classification, which may form a series of bins for allocating each point to different clusters. Much of the previous work has adopted a model-based approach, but is either incapable of estimating the thresholds in an automatic way, or limited to only two point processes, i.e. noise and clusters with the same density. In this paper, we present a new density-based cluster method (DECODE), in which a spatial data set is presumed to consist of different point processes and clusters with different densities belong to different point processes. DECODE is based upon a reversible jump Markov Chain Monte Carlo (MCMC) strategy and divided into three steps. The first step is to map each point in the data to its mth nearest distance, which is referred to as the distance between a point and its mth nearest neighbor. In the second step, classification thresholds are determined via a reversible jump MCMC strategy. In the third step, clusters are formed by spatially connecting the points whose mth nearest distances fall into a particular bin defined by the thresholds. Four experiments, including two simulated data sets and two seismic data sets, are used to evaluate the algorithm. Results on simulated data show that our approach is capable of discovering the clusters automatically. Results on seismic data suggest that the clustered earthquakes, identified by DECODE, either imply the epicenters of forthcoming strong earthquakes or indicate the areas with the most intensive seismicity, this is consistent with the tectonic states and estimated stress distribution in the associated areas. The comparison between DECODE and other state-of-the-art methods, such as DBSCAN, OPTICS and Wavelet Cluster, illustrates the contribution of our approach: although DECODE can be computationally expensive, it is capable of identifying the number of point processes and simultaneously estimating the classification thresholds with little prior knowledge.