Tripura Institute of Technology

About: Tripura Institute of Technology is a based out in . It is known for research contribution in the topics: Electric power system & Renewable energy. The organization has 63 authors who have published 92 publications receiving 510 citations.

An Efficient VLSI Test Data Compression Scheme for Circular Scan Architecture Based on Modified Ant Colony Meta-heuristic

Abstract: A new test data compression scheme for circular scan architecture is proposed in this paper. A stochastic heuristic based bio-inspired optimization approach namely ant colony algorithm (ACO) is applied after modification and customization to improve compression efficiency. In circular scan architecture, test data compression is achieved by updating the conflicting bits between the most recently captured response and test vector to be applied next. The quantity of conflicting bits also manifests the Hamming distance between the most recently captured response and the next test vector. A significant reduction in test data volume and test application time is achieved by reducing Hamming distance. The problem is renovated as a traveling salesman problem (TSP). The test vectors are presumed as cities and Hamming distance between a pair of test vectors is treated as intercity distance and a modified ACO algorithm in combination with mutation operator is applied here to resolve this combinatorial optimization problem. The experimental results confirm the efficacy of this approach. An average improvement of 6.36% in compression ratio and 4.77% in test application time is achieved. The exhibited technique sustains an optimal level of performance without incurring any extra DFT (design for testability) cost.

Wavelets-Based Clustering Techniques for Efficient Color Image Segmentation

Abstract: This paper introduces efficient and fast algorithms for unsupervised image segmentation, using low-level features such as color and texture. The proposed approach is based on the clustering technique, using 1. Lab color space, and 2. the wavelet transformation technique. The input image is decomposed into two-dimensional Haar wavelets. The features vector, containing the information about the color and texture content for each pixel is extracted. These vectors are used as inputs for the k-means or fuzzy c-means clustering methods, for a segmented image whose regions are distinct from each other according to color and texture characteristics. Experimental result shows that the proposed method is more efficient and achieves high computational speed.

Voltage Stability Prediction on Power Networks using Artificial Neural Networks

Abstract: The objective of this paper is to predict the secure or the insecure state of the power system network using a hybrid technique which is a combination of Artificial Neural Network (ANN) and voltage stability indexes. Voltage collapse or an uncontrollable drop in voltage occurs in a system when there is a change in the condition of the system or a system is overloaded. A Transference Index (TI) which acts as a voltage stability indicator has been formulated from the equivalent two-bus network of a multi-bus power system network, which has been tested on a standard IEEE 30-bus system and the result is validated with a standard Fast Voltage Stability Index (FVSI). FACTS devices in the critical bus have been considered for the improvement of the voltage stability of the system. An ANN based supervised learning algorithm has been conferred in this paper alongside Contingency Analysis (CA) for the prediction of voltage security in an IEEE 30 - bus power system network.

Studies on microstructure and mechanical properties of modified 9Cr–1Mo (P91) steel in submerged arc welding with TiO2-enriched fluxes

Abstract: In the present work is studied the influence of titanium dioxide on the microstructure and mechanical properties of submerged arc weld P91 steel weldment. To manipulate the chemical composition of the weldment, the titanium dioxide (TiO2) powder was added with fresh welding flux in various proportions (2.5–12.5%). The results of optical emission spectroscopy test showed that Ti wt% is systematically increased from 0.00 to 0.021, whereas, other alloying elements were fairly uniform. The micrographs of the scanning electron microscope reveal the presence of finely dispersed precipitates in the weld metals, and the percentage of precipitation increases with respect to the enrichment of titanium dioxide. The hardness profile of weld metal increases significantly with respect to the TiO2 addition. Formation of TiC and TiN could be the possible reason for the enhancement of hardness, which has a positive impact on the creep property of steel weldment. Enrichment of TiO2 reveals the appreciable improvement in the tensile strength properties. However, the highest percentage of TiO2 addition provides the adverse effect on the yield strength and ultimate tensile strength values. The presence of surplus quantity of oxide content into the weld metal could be the possible reason behind this. Toughness value of the weld samples showed the negative trend with the addition of TiO2 which can be increased by increasing the post-weld heating time.