Silicon Institute of Technology

Sambalpur, India

Silicon Institute of Technology

Sambalpur, India
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Biswal B.,GMR Institute of Technology | Biswal M.,Silicon Institute of Technology | Mishra S.,Indian Institute of Technology Delhi
IEEE Transactions on Industrial Electronics | Year: 2014

This paper proposes an empirical-mode decomposition (EMD) and Hilbert transform (HT)-based method for the classification of power quality (PQ) events. Nonstationary power signal disturbance waveforms are considered as the superimposition of various undulating modes, and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMFs). The HT is applied on all the IMFs to extract instantaneous amplitude and frequency components. This time-frequency analysis results in the clear visual detection, localization, and classification of the different power signal disturbances. The required feature vectors are extracted from the time-frequency distribution to perform the classification. A balanced neural tree is constructed to classify the power signal patterns. Finally, the proposed method is compared with an S-transform-based classifier to show the efficacy of the proposed technique in classifying the PQ disturbances. © 1982-2012 IEEE.


Abhishek K.,Silicon Institute of Technology
Proceedings of 2016 Online International Conference on Green Engineering and Technologies, IC-GET 2016 | Year: 2017

Intelligent Transportation Systems are an important aspect of our life and are going to become ubiquitous in the near future. Traffic flow prediction is a key component of any Intelligent Transportation Systems. This report uses Artificial Neural Network based models to predict short term traffic flow. Two new input parameters; temperature and truck flow has been introduced into a multi input parameters model. Accordingly five models have been developed based on different combination of the input parameters. By considering past values of multiple input parameters as compared to past values of traffic flow only, the percentage reduction in RMSE values ranges from four to nineteen percentages. Hence, it is suggested to use multi input parameter models for predicting short term traffic flow. © 2016 IEEE.


Biswal B.,GMR Institute of Technology | Biswal M.,Silicon Institute of Technology | Hasan S.,Siksha ‘O’ Anusandhan University | Dash P.K.,Siksha ‘O’ Anusandhan University
Applied Soft Computing Journal | Year: 2014

A new approach to time-frequency analysis and pattern recognition of non-stationary power signals is proposed in this paper. In this manuscript, visual localization, detection and classification of non-stationary power signals are achieved using wavelet packet decomposition and automatic pattern recognition is carried out through learning vector quantization neural network. The wavelet packet decomposition (WPD) of the non-stationary power signals is carried out to extract the coefficients at multiple level of decomposition. The relevant features for pattern classification are derived from the time-scale information obtained by WPD. The extracted features are used to classify different power quality disturbances by using learning vector quantization neural net. Various non-stationary power signal waveforms are considered to verify the applicability of the proposed technique. © 2014 Elsevier B.V.


Biswal M.,Silicon Institute of Technology | Dash P.K.,Siksha ‘O’ Anusandhan University
Digital Signal Processing: A Review Journal | Year: 2013

This paper proposes fast variants of the discrete S-transform (FDST) algorithm to accurately extract the time localized spectral characteristics of nonstationary signals. Novel frequency partitioning schemes along with band pass filtering are proposed to reduce the computational cost of S-transform significantly. A generalized window function is introduced to improve the energy concentration of the time-frequency (TF) distribution. An application of the proposed algorithms is extended for detection and classification of various nonstationary power quality (PQ) disturbances. The relevant features required for classification were extracted from the time-frequency distribution of the nonstationary power signal patterns. An automated decision tree (DT) construction algorithm was employed to select optimal set of features based on a specified optimality criterion for extraction of the decision rules. The set of decision rules thus obtained were used for identification of the PQ disturbance types. Various single as well as simultaneous power signal disturbances were considered in this paper to prove the efficiency of proposed classification scheme. A comparison of the classification accuracies with techniques proposed earlier, clearly demonstrates the improved performance. The major contributions of this manuscript are new FDST algorithms for fast and accurate time-frequency representation and an efficient classification algorithm for identifying PQ disturbances. The advantages of the classification algorithm are (i) accurate feature derivation from the TF distribution and optimum feature selection by the DT construction algorithm, (ii) robust performance at different signal-to-noise ratios, (iii) simple decision rules for classification, and (iv) recognition of simultaneous PQ events. © 2013 Elsevier Inc. All rights reserved.


Pati N.,Silicon Institute Of Technology
Proceedings of the 2014 IEEE 2nd International Conference on Electrical Energy Systems, ICEES 2014 | Year: 2014

The switched mode dc-dc converters are some of the simplest power electronic circuits which have received an increasing deal of interest in many areas due to their high efficiency and small size. These converters are non-linear and time-variant in nature; hence the analysis, control and stabilization are the main factors that need to be considered. Many control methodology are used for control of switch mode dc-dc converters but the optimum one is always in demand. This paper presents the linearization of the Buck converter model and a comparison between the control algorithms for better output voltage regulation along with robustness to change in input voltage and load parameters. The computer-aided design software tool Matlab/Simulink is used for the simulations and the results are presented. © 2014 IEEE.


Biswal M.,Silicon Institute of Technology | Dash P.K.,Siksha ‘O’ Anusandhan University
IET Science, Measurement and Technology | Year: 2012

The S-transform (ST) finds widespread application in non-stationary signal analysis. However, the relatively high computational complexity of the ST remains as a challenge. Further, the optimum choice of the window function and the discretisation side effects of the ST need to be addressed for accurate time-frequency localisation. This study proposes a fast adaptive discrete generalised ST (FDGST) algorithm based on a new frequency scaling named selective frequency scaling, window cropping and an adaptive window function. The proposed algorithm optimises the shape of the window function for each analysis frequency to improve the energy concentration of the time-frequency distribution, and applies folded window functions to minimise aliasing affect owing to discretisation. Further, the algorithm is applied for analysis and measurement of parameters in various types of power quality waveforms. Standard transient and steady-state indices calculation from the FDGST analysis is also illustrated. The improved performance of the proposed algorithm is supported by simulations using synthetic as well as practical signals. © 2012 The Institution of Engineering and Technology.


Nanda U.,Silicon Institute of Technology
Journal of Low Power Electronics | Year: 2016

In an all-digital phase-locked loop (ADPLL), time-to-digital converter (TDC) is a paramount block. Nevertheless, design issues and solutions to resolve them always increase the complexity of the system. A novel strategy to handle the frequency error detection in a type-II ADPLL architecture is analyzed in this work. This strategy has several advantages of scalability and integration and reduces complexity up to a greater extent than conventional AD-PLLs. Consequently, this strategy enhances its applicability in less stringent applications like PLL based frequency synthesizers. Mathematical analysis showing the advantages in choosing this strategy to detect the phase error is demonstrated. The theoretical and simulated phase noise analysis is also provided and compared with the conventional technique. The power consumption of the PLL using this proposed strategy is as low as 1.9 mW. Copyright © 2016 American Scientific Publishers All rights reserved.


Mohapatra A.G.,Silicon Institute of Technology
Sensors and Transducers | Year: 2011

Collision Avoidance System solves many problems caused by traffic congestion worldwide and a synergy of new information technologies for simulation, real-time control and communications networks. The above system is characterized as an intelligent vehicle system. Traffic congestion has been increasing world-wide as a result of increased motorization, urbanization, population growth and changes in population density. Congestion reduces utilization of the transportation infrastructure and increases travel time, air pollution, fuel consumption and most importantly traffic accidents. The main objective of this work is to develop a machine vision system for lane departure detection and warning to measure the lane related parameters such as heading angle, lateral deviation, yaw rate and sideslip angle from the road scene image using standard image processing technique that can be used for automation of steering a motor vehicle. The exact position of the steering wheel can be monitored using a steering wheel sensor. This core part of this work is based on Hough transformation based edge detection technique for the detection of lane departure parameters. The prototype designed for this work has been tested in a running vehicle for the monitoring of real-time lane related parameters. © 2011 IFSA.


Biswal M.,Silicon Institute of Technology | Dash P.K.,Siksha ‘O’ Anusandhan University
IEEE Transactions on Industrial Informatics | Year: 2013

This paper proposes a new scheme for measurement, identification, and classification of various types of power quality (PQ) disturbances. The proposed method employs a fast variant of S-Transform (ST) algorithm for the extraction of relevant features, which are used to distinguish among different PQ events by a fuzzy decision tree (FDT)-based classifier. Various single as well as simultaneous power signal disturbances have been simulated to demonstrate the efficiency of the proposed technique. The simulation result implies that the proposed scheme has a higher recognition rate while classifying simultaneous PQ faults, unlike other methods. The Fast dyadic S-transform (FDST) algorithm for accurate time-frequency localization, Decision Tree algorithms for optimal feature selection, Fuzzy decision rules to complement overlapping patterns, robust performance under different noise conditions and a relatively simple classifier methodology are the strengths of the proposed scheme. © 2013 IEEE.


Routray M.,Silicon Institute of Technology
ICACCS 2015 - Proceedings of the 2nd International Conference on Advanced Computing and Communication Systems | Year: 2015

Physical interactions between the proteins in a living organism helps in identification of most protein-protein interaction data. The annotated proteins are previously known by their functions. Their knowledge is definite. The un-Annotated proteins are annotated based on estimation of such similar functions. Generally a cluster containing annotated nodes with their adjacent unlabeled nodes is assumed to have homogeneity of functions within. Though the interaction data are generally very noisy, a Bayesian model is presented to predict protein functions after a series of known experiments or several hypotheses over neighborhood properties are conducted or assumed. The experimental results in this effort have shown that there is a better performance in evaluation of weighted accuracy of functions over prediction of data set. © 2015 IEEE.

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