Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln

Artificial neural networks (ANNs) and neuro-fuzzy systems (NFSs) have been widely used in modeling and control of many practical industrial nonlinear processes. However, most of them have concentrated on single-output systems only. In this paper, we present a comparative study using ANNs and co-active neuro-fuzzy inference system (CANFIS) in modeling a real, complicated multi-input–multi-output (MIMO) nonlinear temperature process of roller kiln used in ceramic tile manufacturing line. Using this study, we prove that CANFIS is better suited for modeling the temperature process in control phase. After that, a neural network (NN) controller has been developed to control the above mentioned temperature process due to a feedback control diagram. The designed controller performance is tested by a Visual C++ project and the resulting numerical data shows that this controller can work accurately and reliably when the roller kiln set-point temperature set changes.     

Stochastic communication for application-specific Networks-on-Chip

In this paper, we have developed analytical stochastic communication technique for inter and intra-Networks-on-Chip (NoC) communication. It not only separates the computation and communication in Networks-in-Package (NiP) but also predicts the communication performance. Moreover, it will help in tracking of the lost data packets and their exact location during the communication. Further, the proposed technique helps in building the Closed Donor Controlled Based Compartmental Model, which helps in building Stochastic Model of NoC and NiP. This model helps in computing the transition probabilities, latency, and data flow from one IP to other IP in a NoC and among NoCs in NiP. From the simulation results, it is observed that the transient and steady state response of transition probabilities give state of data flow latencies among the different IPs in NoC and among the compartments of NoCs in NiP. Furthermore, the proposed technique produces low latency as compared to the latencies being produced by the existing topologies.     

A testbed for performance evaluation of load‐balancing strategies for Web server systems

To improve response time of a Web site, one replicates the site on multiple servers. The effectiveness of a replicated server system will depend on how the incoming requests are distributed among replicas. A large number of load‐balancing strategies for Web server systems have been proposed. In this paper we describe a testbed that can be used to evaluate the performance of different load‐balancing strategies. The testbed uses a general architecture which allows different load‐balancing approaches to be supported easily. It emulates a typical World Wide Web scenario and allows variable load generation and performance measurement. We have performed some preliminary experiments to measure the performance of a few policies for load balancing using this testbed. Copyright © 2003 John Wiley & Sons, Ltd.     

Reputation based probabilistic resource allocation for avoiding free riding and formation of common interest groups in unstructured P2P networks

Free riding is a major problem in peer-to-peer networks. Reputation management systems are generally employed to overcome this problem. In this paper, a new reputation based scheme called probabilistic resource allocation is proposed. This strategy probabilistically decide whether to provide the resource to requesting peer or not. Aforesaid method gives selection preference to higher reputation peers and at the same time provides some finite probability of interaction between those peers who don’t have good reputation about each other. This avoids disconnection between the aforesaid peers. The proposed scheme also introduces a new mechanism for resource distribution which not only allocates resources based on peers’ reputation but simultaneously maximizes network utility also. Algorithm for formation of interest groups based upon both similarity of interests and reputation between peers is also presented.     

Change point detection for compositional multivariate data

Change point detection algorithms have numerous applications in areas of medical condition monitoring, fault detection in industrial processes, human activity analysis, climate change detection, and speech recognition. We consider the problem of change point detection on compositional multivariate data (each sample is a probability mass function), which is a practically important sub-class of general multivariate data. While the problem of change-point detection is well studied in univariate setting, and there are few viable implementations for a general multivariate data, the existing methods do not perform well on compositional data. In this paper, we propose a parametric approach for change point detection in compositional data. Moreover, using simple transformations on data, we extend our approach to handle any general multivariate data. Experimentally, we show that our method performs significantly better on compositional data and is competitive on general data compared to the available state of the art implementations.

     

Role of multiplicity in reactive distillation control system design

The impact of steady-state multiplicities on the control of a simulated industrial scale methyl acetate reactive distillation (RD) column is studied. At a fixed reflux rate, output multiplicity, with multiple output values for the same reboiler duty, causes the column to drift to an undesirable steady-state under open loop operation. The same is avoided for a fixed reflux ratio policy. Input multiplicity, where multiple input values give the same output, leads to “wrong” control action under feedback control severely compromising control system robustness. A new metric, rangeability, is defined to quantify the severity of input multiplicity in a steady-state input–output (IO) relation. Rangeability is used in conjunction with conventional sensitivity analysis for the design of robust control structures for the RD column. Results for the two synthesized control structures show that controlling the most sensitive reactive tray temperature results in poor robustness due to low rangeability causing “wrong” control action for large disturbances. Controlling a reactive tray temperature with acceptable sensitivity but larger rangeability gives better robustness. It is also shown that controlling the difference in the temperature of two suitably chosen reactive trays further improves robustness of both the structures as input multiplicity is avoided. The article brings out the importance of IO relations for control system design and understanding the complex dynamic behavior of RD systems.     

Discontinuity-Preserving Surface Reconstruction Using Stochastic Differential Equations

We address the problem of reconstructing a surface from irregularly spaced sparse and noisy range data while concurrently identifying and preserving the significant discontinuities in depth. It is well known that, starting from either the probabilistic Markov random field model or the mechanical membrane or thin plate model for the surface, the solution of the reconstruction problem can be eventually reduced to the global minimization of a certain “energy” function. Requiring the preservation of depth discontinuities makes the energy function nonconvex and replete with multiple local minima. We present a new method for obtaining discontinuity-preserving reconstruction based on the numerical solution of an appropriate Ito vector stochastic differential equation (SDE). The reconstructed surface is found by following the sample path of the (stochastic) diffusion process that solves the SDE in question. Our central contribution is the demonstration of the efficacy of the stochastic differential equation technique for solving a vision problem. Through comparisions of the results of our method to those of the two well-known existingglobalminimization based reconstruction techniques, we show a significant improvement in the final reconstructions obtained.     

Development of spectral band cloning techniques for soil nutrients estimation

Satellite‐based monitoring is an indispensable tool to guide soil‐specific crop management. However, it has attained little success in the estimation of soil nutrients due to the limitations incurred from inherent spectral characteristics. In this study, spectral band cloning (SBC) is developed and proposed to augment the soil nutrient predictive capabilities of broadband satellite data. Fine‐spectral channels of spectrometers were synchronized with coarse resolution of IRS satellite data to generate nutrient‐sensitive cloned IRS bands. Soil samples, collected at the time of satellite image acquisition in Lop Buri, Thailand, were analyzed both spectrally and chemically, viz., soil organic matter (OM), phosphorus, potassium and iron. The resulting SBC‐based models showed acceptable correlations, which otherwise were unattainable from raw IRS bands through prevailing models. Accuracy and validation measures showed good agreements between the measured and estimated nutrient surfaces. It is concluded that the SBC is a promising method of quantitative soil nutrient mapping, and could further be used for identification and mapping of other indiscernible biophysical parameters.