Any event deemed as being out-of-the-ordinary may be called an anomaly. Anomalies by virtue of their definition are
events that occur spontaneously with no prior indication of their existence or appearance. Effects of anomalies are
typically unknown until they actually occur, and their effects aggregate in time to show noticeable change from the
original behavior. An evolved behavior would in general be very difficult to correct unless the anomalous event that
caused such behavior can be detected early, and any consequence attributed to the specific anomaly. Substantial time and
effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to abnormal
behavior. There is a critical need therefore to automatically detect anomalous behavior as and when they may occur, and
to do so with the operator in the loop. Human-machine interaction results in better machine learning and a better
decision-support mechanism. This is the fundamental concept of intelligent control where machine learning is enhanced
by interaction with human operators, and vice versa. The paper discusses a revolutionary framework for the
characterization, detection, identification, learning, and modeling of anomalous behavior in observed phenomena arising
from a large class of unknown and uncertain dynamical systems.
Proc. SPIE. 3068, Signal Processing, Sensor Fusion, and Target Recognition VI
KEYWORDS: Mathematical modeling, Digital signal processing, Genetic algorithms, Detection and tracking algorithms, Control systems, Linear filtering, System identification, Servomechanisms, Genetics, Systems modeling
Automation is the future trend for the target tracking systems of the Smart Munitions Test Suite at White Sands Missile Range. Resonances often appear in electro-mechanical systems and tend to reduce the performance of the control tracking algorithms. Hence, automatic resonant cancellation is one of the algorithms that should be considered. In this paper the concept and implementation of automatic resonant cancellation using Genetic Algorithms (GAs) in conjunction with the system identification package and the notch filter developed by the DSP Control Group is presented. A simple GA is used to search for the highest resonant peaks caused by imperfect coupling between motor and load. The search is guided by a fitness function which is the transfer function obtained from a system identification method.
In this paper, a fuzzy logic based approach to the cooling of laser materials is presented. The controller design is based on the performance characteristics of commercially available thermoelectric coolers. Simulation results are presented and discussed. The feasibility of implementing such a controller is evaluated.
The development of high resolution spectrophotometers and colorimeters, combined with its portability and large data processing abilities, has made the color evaluation process easier and faster. Although these instruments are very useful for rapid pass or fail color inspections in many industries such as, the automotive industry, textile industry, etc., the final decision depends primarily upon a subjective visual assessment. Besides spectral analysis, which is useful in colorant selection, the interrelationship between various environmental factors, metamerism, and texture and composition of the material (substrate), has made visual coordination an acceptable methodology to obtain a repeatable finish and color quality. Subjective assessment in color matching, especially in colors that closely resemble one another, leads to laborious and time consuming adjustments that have to be performed to obtain the right concentration of the colorants. Color evaluation and color mixing for a given material surface are interdependent. Although there are analytical methods that provide a means for colorant analysis, their application is cumbersome and involves complex calculations. In this paper we develop a fuzzy approach to obtain optimal color correlation between visual assessment, computed color differences, and colorant composition.
This paper discusses the development of an automatic focus control unit (AFCU) for optical tracking instrumentation at White Sands Missile Range, New Mexico. The telescope system is known as the distant object attitude measurement system (DOAMS) and is used for optical data collection including attitude and miss-distance information. The AFCU will be given only target range and will provide a highly accurate focus. Fuzzy logic was chosen as the control method for this project.
Liquid crystal televisions (LCTVs) have become very popular spatial light modulators. Their polarization and phase modulation capabilities allow them to be used as inexpensive spatial light modulators in a wide variety of applications. The design of a dynamic Hermann wavefront sensor system is described. A LCTV is used as an aberration generator in an optical system. A LCTV is also used as a Harmann wavefront sensor to measure the aberrations. Experimental results characterizing the LCTVs performance as an aberration generator and Harmann wavefront sensor are presented.
This report summarizes the Threat Object Map (TOM) handover analysis that the ODA team has performed during the past year. The areas of study include evaluating data from the STORM 4 and STORM 6 missions to determine: (1) performance of a radar to optical interceptor TOM handover, (2) sensitivities to data latency both above and within the atmosphere, (3) platform sensitivities to closely spaced objects, and (4) sensitivity to N objects handed up from the radar to M objects on the interceptor focal plane. This analysis is limited to metric only TOM handover and does not include generalized TOM evaluation. The analysis uses the OMEGA and TOMAHOC codes. OMEGA models the radar noising. TOMAHOC (Threat Object Map and Handover Code) performs the metric handover. TOMAHOC contains bias removal algorithms and a Sparse Munkre's algorithm.
The measurement of laser beam quality is of prime importance for various applications. M2 factor has been widely accepted as a standard for characterizing the quality of real laser beams. The inaccuracies present in the specifications of resonator elements, variations occurring due to various competing physical processes inside the lasing medium, and offsets in the cavity configuration make the beam quality deviate from the desired value. Since the beam quality can be improved by manipulating the cavity parameters, fine tuning of mirror separation distance can offer considerable modification in the beam quality. In this paper, a fuzzy logic based controller to obtain and monitor desired laser beam characteristics for stable resonators is discussed. The simulation results indicate that the proposed fuzzy logic controller will dynamically adapt to real laser beams and can offer superior performance over conventional proportional-integral-derivative (PID) controllers. The principle advantage of the present approach is that it provides a versatile means for automatic control over the beam characteristics without relying on detailed mathematical modeling techniques.
The beam characteristics of a laser depend on various factors such as temperature, mechanical deficiencies of mounts, tolerance specifications, etc. As such, there is a tendency for the beam characteristics to deviate from the desired characteristics. This paper describes the development of a fuzzy-logic based controller to obtain and maintain specific output beam characteristics of an optical resonator.
An optical parametric oscillator (OPO) produces coherent optical radiation which is tunable over a wide range. In this paper, a novel technique for angle tuning an OPO by an acousto-optic Bragg cell is discussed. It is shown that the proposed scheme provides course as well as fine tuning of signal wavelengths with high speed, and hence is a promising alternative to the conventional tuning techniques.
This paper demonstrates the successful application of a pattern recognition technique to detect abnormal operating conditions in power systems. Specifically, the paper discusses the application of the minimum entropy method to derive a 2-class classifier system that enables classification of power system behavior into either the secure or insecure class. Security violations are detected on the basis of a line-overload criteria. Classifier results for the New England Power test system are provided. The major benefits obtained by the application pattern recognition techniques are the rapid detection of abnormal operating conditions and the substantial reduction in computation as compared to traditional methods.