Richard W. Conners

Wide-Area Frequency Monitoring Network (FNET) Architecture and Applications

Recent developments in smart grid technology have spawned interest in the use of phasor measurement units to help create a reliable power system transmission and distribution infrastructure. Wide-area monitoring systems (WAMSs) utilizing synchrophasor measurements can help with understanding, forecasting, or even controlling the status of power grid stability in real-time. A power system frequency monitoring network (FNET) was first proposed in 2001 and was established in 2004. As a pioneering WAMS, it serves the entire North American power grid through advanced situational awareness techniques, such as real-time event alerts, accurate event location estimation, animated event visualization, and post event analysis. Several papers published in the past several years discussed the FNET structure and its functionality. This paper presents some of the latest implementations of FNETs applications, which add significant capacities to this system for observing power system problems.

A prototype vision system for analyzing CT imagery of hardwood logs

To fully optimize the value of material produced from a hardwood log requires information about type and location of internal defects in the log. This paper describes a prototype vision system that automatically locates and identifies certain classes of defects in hardwood logs. This system uses computer tomograph (CT) imagery. The system uses a number of processing steps. A set of basic features are defined to capture basic 3-D characteristics of wood defects. For 3-D object (defect) recognition, a set of hypothesis tests are employed that use this set of features. To further help cope with the above mentioned variability, the Dempster-Shafer theory of evidential reasoning is used to classify defect objects. Results of preliminary experiments employing two different types of hardwood logs are given.

Frequency Disturbance Recorder Design and Developments

Frequency is one of the most important parameters for determining the performance of an electrical power system. To study and gain a better understanding of the power system dynamics, the concept of building a frequency monitoring network (FNET) was proposed in 2000 and now has been realized. The FNET system consists of many high dynamic precision frequency estimation devices, also known as frequency disturbance recorders (FDR). These FDRs can be used at any 110 V or 220 V wall outlet and transmit measured frequency data remotely via the Ethernet. There are currently more than 40 FDRs placed strategically in different locations within the United States to monitor the entire power network. This paper summarizes the implementation of several FDR design and discusses some of ongoing hardware developments conducted in FNET.

A MOdular and Reprogrammable Real-time Processing Hardware, MORRPH

The MORRPH architecture is a general purpose reconfigurable processing unit, primarily designed to solve real time 2D image processing problems. Its robust architecture allows it to be used for other applications including 1D signal processing, 2D cellular automata problems, and 3D image processing. The modular, open ended architecture consists of an M/spl times/N rectangular mesh of processing elements (PEs), called the processing array. Each PE contains a single field programmable gate array (FPGA) chip and interconnections for several support chips. The FPGA chips within the PEs provide an array of logic resources, consisting of combinational logic functions, flip flops and internal chip routing resources. The types of support chips which are included in the PEs are not fixed, they are determined by the requirements of the computational task performed by the MORRPH. These memory, arithmetic, or processing support chips are specified and assembled on the MORRPH board for each particular application that is developed. Currently, the MORRPH architecture is implemented as an adapter card for the Industry Standard Architecture (ISA) computer bus. A constructed prototype with a 23 array of PEs is used in a current machine vision system to perform low level image processing functions. A significant performance increase is obtained by using the MORRPH as a preprocessing unit for the host processing computer. The MORRPH architecture is shown to be an inexpensive solution for relatively simple or very complex real time processing tasks.

A machine vision system forautomatically grading hardwood lumber

Abstract Any automatic system for grading hardwood lumber can conceptually be dividedinto two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has been made on developing the first component, the machine vision component, will be reported in this paper. The machine vision system being developed is made up of a subsystem for imaging rough lumber surfaces, a computer vision subsystem for analyzing the image data and identifying grading defects, a materials handling subsystem for moving boards through the imaging devices, a computer for executing the algorithms comprising the computer vision sub-system and, finally, another small computer for controlling all the other components. This paper will describe the progress that has been made on developing all of these components. It will also indicate the directions for future research. A major goal of this research activity is to create a vision technology that will be applicable to not only the grading of hardwood lumber but a number of other forest products related applications as well.

A computer vision system for automated grading of rough hardwood lumber using a knowledge-based approach

A computer vision system that locates and identifies grading defects in rough hardwood lumber in a species-independent manner is described in detail. It consists of a low-level module that performs segmentation and extracts region properties, and a high-level module that identifies the type of defect present in each of the regions passed from the low-level module and extracts the appropriate characteristics associated with each defect. The system has been designed using a knowledge-based approach using a blackboard framework and has been tested on a number of boards from four hardwood species. The current system can detect four of the most common types of defects: knots, holes, wane, and splits/checks. Although it has limited recognition capabilities, the results suggest that species-independent methods can be found for accomplishing the required tasks.<<ETX>>

A computer vision system for analyzing images of rough hardwood lumber

A computer vision system for locating and identifying defects on rough hardwood lumber in a species-independent manner is described. The system consists of three modules: a low-level module, a mid-level module, and a high-level module. The low-level module is a segmentation module that segments an input board image using a histogram-based thresholding method. The mid-level module eliminates small noise regions, merges similar adjacent regions, and computes region properties of merged regions. The high-level module identifies the defects present in each of the regions passed to it using a rule-based approach. The system is designed to be used in an automatic edger and trimmer for hardwood lumber in order to optimize the value of the hardwood boards processed.<<ETX>>

Machine vision technology for the forest products industry

From forest to finished product, wood is moved from one processing stage to the next, subject to the decisions of individuals along the way. While this process has worked for hundreds of years, the technology exists today to provide move complete information to the decision makers. Virginia Tech has developed this technology, creating a machine vision prototype for wood products manufacturing.

Using multiple FPGA architectures for real-time processing of low-level machine vision functions

In this paper, we investigate the use of multiple field programmable gate array (FPGA) architectures for real-time machine vision processing. The use of FPGAs for low level processing represents an excellent tradeoff between software and special purpose hardware implementations. A library of modules that implement common low-level machine vision operations is presented. These modules are designed with gate-level hardware components that are compiled into the functionality of the FPGA chips. A common input/output interface is created for use in each of the modules, allowing the interconnection of several image processing modules in a parallel or pipelined manner. This new synchronous, unidirectional interface establishes a protocol for the transfer of image and result data between modules. This reduces the design complexity and allows several different low-level operations to be applied to the same input image. A method is developed to partition and compile the design into the hardware resources of multiple FPGA chips. Experimental results verify the efficiency of using common multiple FPGA architectures to implement real-time machine vision processing.

Computer Vision Hardware System for Automating Rough Mills of Furniture Plants

The rough mill of a hardwood furniture or fixture plant is the place where dried lumber is cut into the rough parts that will be used in the rest of the manufacturing process. Approximately a third of the cost of operating the rough mill is the cost of the raw material. Hence any increase in the number of rough parts produced from a given volume of raw material can markedly affect profit margins of a company. To automate this initial cutup requires a computer vision system that can locate and identify surface defects on boards. This paper describes continuing research aimed at developing such a vision system. An important part of this research activity is the design effort going into creating a prototype hardware system, a system that will be able to scan variable width, variable length hardwood boards at industrial speeds of two to three linear feet per second. This system is being designed to handle full length boards up to sixteen feet long. The components of the prototype are a materials handling system, an imaging system, a image processing hardware system, and a software system for performing the necessary recognition tasks and for performing all the necessary control functions. The design of each of these components will be described with the emphasis placed on hardware development.