Sanjeev Arora

Artificial neural network models for predicting degradation trends in system components and sensors

A prediction model based-on artificial neural network technology was developed for trend forecasting of a given degradation process in a system component. The model utilizes the engineering analysis of the degradation process under study with the analysis of process field data and information to predict future trend in the degradation. The neural network prediction models were applied to simulated degradation data of a typical system component. The prediction results showed that the neural network models were capable of recognizing the correct future degradation trends in data even with a limited amount of input data. In addition, the models were able to capture the dynamics and nonlinearities associated with the degradation process data.

Performance analysis of a high-end CPU under a heavy computational load and varying RAM amount using thermal imaging techniques

The purpose of this research was to investigate the effects of different RAM amounts on the CPU surface temperature distribution while operating under a heavy computational load. Two experiments corresponding to different RAM amounts were carried out under relatively poor cooling conditions. A personal computer with a high-end Intel Pentium IV CPU was used to conduct the experiments. The computational load was the multiplication of two dimensional matrices (6000 by 6000) containing double precision numbers. In order to monitor temperature distributions, a high resolution thermal imaging camera was utilized. A data acquisition system was interfaced with the camera to acquire thermal images of CPU at pre-defined computational stages. The analysis of the thermal images showed that the CPU surface temperature increases as more RAM becomes available to perform the computation. The surface temperature distribution was used to identify the location of possible hotspots on the CPU

Visualizing conductive and convective heat transfer using thermographic techniques

This work explores the educational applications of thermography in teaching conductive and convective heat transfer concepts to undergraduate students. Thermography helps students effectively visualize heat transfer phenomena particularly in two and three dimensions. Experiments were designed to demonstrate one-dimensional heat transfer in metal rods, two dimensional heat transfer in a metal plate subject to given boundary conditions and convective heat transfer in fluid. The data obtained through these experiments, including thermal imaging video clips, was made available to students enrolled in instrumentation and physics courses. Using these thermographic data, students could physically observe heat transfer phenomena that otherwise would have been difficult to visualize.

Effect of RAM amount on the thermal behavior of CPU operating under a heavy computational load

The purpose of this research was to investigate the effects of different RAM amount and fan failure on bulk CPU temperature rises while operating under a heavy computational load. Two sets of experiments, each with varying amounts of RAM were carried out under CPU cooling fan-on and fan-off conditions. A personal computer with an Intel Pentium III CPU was used to conduct the experiments. The computational load was the multiplication of two dimensional matrices (3100 by 3100) containing double precision numbers. To monitor temperature rises, sensitive thermocouples were installed on the CPU heat-sink, the RAM module, and the hard disk. This paper demonstrates that CPU temperature increases as more RAM becomes available to perform the computation. The authors hypothesize that the increase in CPU temperature is correlated to full CPU utilization to carry out a heavy computational load.

Operating, monitoring and controlling plant components over cyberspace

In recent years, nearly every industry has increasingly implemented computer based measurement, instrumentation and automation technologies to control, operate, and/or monitor various plant components of industrial equipment. This has also resulted in a paradigm shift from analog to digital technologies that are suitable for communications over the Internet, Web or networked computer systems. This paper presents a methodology for remote operation and monitoring of plant components through the Internet/Web. The Internet interfacing technologies have been examined through an experimental setup used at Fort Valley State University to perform remote experiments via the Internet. The setup is a motor-generator station that can be fully operated, monitored and controlled by computer systems using Virtual Instrument programs written in LabVIEW. It also features on-line capabilities that allow users to fully operate and monitor it remotely through the Internet. The paper also addresses typical technological concerns and challenges regarding safety and security measures as well as real-time operation.

Selection of principal components based on Fisher discriminant ratio

Principal component analysis transforms a set of possibly correlated variables into uncorrelated variables, and is widely used as a technique of dimensionality reduction and feature extraction. In some applications of dimensionality reduction, the objective is to use a small number of principal components to represent most variation in the data. On the other hand, the main purpose of feature extraction is to facilitate subsequent pattern recognition and machine learning tasks, such as classification. Selecting principal components for classification tasks aims for more than dimensionality reduction. The capability of distinguishing different classes is another major concern. Components that have larger eigenvalues do not necessarily have better distinguishing capabilities. In this paper, we investigate a strategy of selecting principal components based on the Fisher discriminant ratio. The ratio of between class variance to within class variance is calculated for each component, based on which the principal components are selected. The number of relevant components is determined by the classification accuracy. To alleviate overfitting which is common when there are few training data available, we use a cross-validation procedure to determine the number of principal components. The main objective is to select the components that have large Fisher discriminant ratios so that adequate class separability is obtained. The number of selected components is determined by the classification accuracy of the validation data. The selection method is evaluated by face recognition experiments.