Browsing by Author "Aravinthan, V"

Now showing 1 - 4 of 4

item: Conference-Extended-Abstract
Design of a low cost hand operated yarn trimming device for conventional lock stitch machines
(2007) Gopura, RARC; Attanayake, N; Arumapperuma, C; Aravinthan, V
Yarn trimming is' carried out by using a pair of scissors in conventional lock stitch machines. Three steps should be followed to trim the yarn. First, the presser foot is lifted by using the knee lifter (hand lifter in some household old lock stitch machines). Then the fabric is moved out and ultimately the yarn is trimmed using a pair of scissors. When the pair of scissor is used, it should be taken to hand and then trimming is done, which is a time consuming task.
item: Thesis-Abstract
Intelligent sensor system for humanitarian demining
(11/30/2011) Aravinthan, V; Nanayakkara, T; Dayawansa, I; Dias, D
In any post conflict country, landmines have become a major concern to civilians especially during the resettlement period. Biological Sensors and Metal Detectors are the most common detection technologies used commercially. One of the major drawbacks with the commercially available metal detectors is that they don’t have very good discriminating power and in practice they give very high rate of false alarms. Generally the ratio between the detection of a landmine and getting a false alarm varies in the region of 1:100 to 1:1000 depending on the location. The aim of this study is to introduce an Intelligent Discriminating System so that the false alarms would be reduced. Detailed study on Very Low Frequency (VLF) type Metal Detectors show that, the presence of a ferromagnetic object changes the phase of the signal induced in the receiving coil. This phase change heavily depends on the type of the alloy. This property is used to discriminate ferromagnetic alloys in the proposed methodology. The field survey shows that the processing speed of the system should be fast and accurate. The aliased signal from the detector receiver coil is used in this study to reduce the processing time. The received signal is further processed using Discrete Wavelet Analysis. The First Level High Frequency Sub-band signal, with Meyer Wavelet, depends highly on the type of material, sweep frequency, and the depth of the material. This processed data is used to classify the object into different classes. Modified version of Adaptive Resonance Theory (ART-1) is used in the classifying process. The results show that different metals could be classified with 5% significance, same material but different size could be classified with 10% significance and Alloys could be classified into user defined classes; this depends on the templates used in the ART- 1. Further by changing the classification algorithm, the objects could be classified into user defined groups.
item: Conference-Full-text
Local detection of distribution level faults in a distributed sensor monitoring network using HMM
(Institute of Electrical and Electronics Engineers, Inc., 2016-12) Balachandran, T; Aravinthan, V; Thiruvaran, T; Rajapakse, A; Prasad, WD
The Smart distribution system initiative requires an increased usage of the distribution feeder-level communication infrastructure to improve automation. Using a distributed sensor network for monitoring the distribution system is proposed by various researchers. Such distributed sensor communication architecture requires information to be received within an allowable delay and a minimum processing time at the control center. Increasing the number of sensors in the network also increases the data flow in the communication medium. Therefore, to reduce the burden in the communication medium, an event driven communication protocol could be utilized. This communication architecture assumed that the sensors used a local fault detection system to detect the abnormal event before communicating with the control center. This work focusses on local detection of faults in a distributed sensor network using a Hidden Markov Model considering a minimum processing time.
item: Conference-Full-text
Sub-system based reliability assessment for distribution transformer
(Institute of Electrical and Electronics Engineers, Inc., 2016-12) Rahman, MRU; Aravinthan, V; Rajapakse, A; Prasad, WD
Implementation of Smart Grid into the power system has made our grid more robust and reliable. Due to integration of two-way, real time communication it is possible to monitor equipment through sensors. Continuous monitoring will help to predict the state of the equipment. Due to this fact and also to ensure the system performance, time based maintenance can be replaced by condition or reliability based maintenance. Contribution of this work is to identify the appropriate standards for to be used for the transformer subcomponents and develop appropriate Weibull distribution parameters for each subcomponent for a modified series-parallel topology of distribution transformer. This modification is done based on the available measurements and standards. Using the developed reliability model, Monte Carlo simulation was performed to evaluate the behavior of these parameters.