Jyoti Sharma | M.D.S.University - Academia.edu (original) (raw)

Papers by Jyoti Sharma

Archives of Virology, 2004

The coat protein (CP) sequences of eleven Papaya ringspot virus (PRSV) isolates originating from ... more The coat protein (CP) sequences of eleven Papaya ringspot virus (PRSV) isolates originating from different locations in India were determined, analysed and compared with the sequences of other isolates of PRSV. The virus isolates from India exhibited considerable heterogeneity in the CP sequences. The CP-coding region varied in size from 840–858 nucleotides, encoding protein of 280–286 amino acids. Comparative sequence analysis revealed that the PRSV isolates originating from India were divergent up to 11%. Though the PRSV isolates were differentiated in to two clusters, yet the sequence variation could not be correlated with the geographical origin of the isolates. Implication of the sequence variation in the coat protein derived transgenic resistance in papaya is discussed.

Plant Disease, 2004

Papaya (Carica papaya L.) is an important fruit crop in Bangladesh. During surveys conducted in D... more Papaya (Carica papaya L.) is an important fruit crop in Bangladesh. During surveys conducted in Dhaka and Mymensingh regions from April to June 2003, >50% of papaya plants were observed to have leaf mottling, mosaic and mild distortion, and water-soaked streaks on petioles ...

Current Anthropology, 1981

RefDoc Bienvenue - Welcome. Refdoc est un service / is powered by. ...

Bulletin of The Seismological Society of America, 2008

ABSTRACT In the present study the attenuation of seismic-wave energy in and around the source are... more ABSTRACT In the present study the attenuation of seismic-wave energy in and around the source area of the Chamoli Earthquake of 29 March 1999 is estimated using aftershock data. Most of the analyzed events are from the vicinity of the main central thrust (MCT), which is a well-defined tectonic discontinuity in the Himalayas. The method of a single backscattering model is employed to calculate frequency dependent values of coda Q (Q(c)). A total of 30 aftershock events are used for Q(c) estimation at central frequencies 1.5, 3, 6, 9, 12, 18, and 24 Hz through five lapse-time windows from 10 to 50 see starting at double the travel time of the S wave. The observed Q(c) is strongly dependent on frequency, which indicates that the region is seismically and tectonically active with high heterogeneities. The variation of Q(c) has also been estimated at different lapse times to observe its effect with depth. The variation of Q(c) with frequency and lapse time shows that the lithosphere becomes more homogeneous with depth. Q(c)-values for higher frequencies increase very fast with depth within about the top 63 km of the lithosphere and then become more or less constant beyond this depth. This indicates that turbidity at higher frequency decays very fast with depth, and the mantle may be transparent to high-frequency waves. The variation of Q(c) at 1.5 Hz with lapse time matches quite well with those predicted by Gusev (1995). However, the frequency parameter n in the relation Q(c) = Q(0)f(n), where Q(0) = Q(c) at 1 Hz, does not follow the expected pattern given in his model. This could be due to faster depth decay of turbidity as mentioned previously.

Bulletin of The Seismological Society of America, 2009

The existing seismological network in the Kangra–Chamba sector has been upgraded with 12 three-co... more The existing seismological network in the Kangra–Chamba sector has been upgraded with 12 three-component digital seismometers to obtain new insight on the nature and sources of continued clustered seismicity in this part of northwest Himalaya. A combination of ...

Iee Proceedings-generation Transmission and Distribution, 1999

Voltage contingency screening and ranlung is performed to choose the contingencies that cause the... more Voltage contingency screening and ranlung is performed to choose the contingencies that cause the worst voltage problems. A knowledge-based conceptual neural network (KBCNN) is developed for fast voltage contingency selection and ranking. A recognised shortcoming of the ANN based approach is that the problem solving knowledge of ANN is represented in the connection weights, and hence is dlffcult for a human user to comprehend. One way to provide an understanding of the behaviour of neural networks is to extract rules that can be provided to the users. The rules extracted are used to build a knowledge-based connectionist network for learning and revision of rules. The knowledge-based neural network is applied for voltage contingency selection and ranlung in an IEEE 30-bus system and a practical 75-bus Indian system. Once trained, the KBCNN gives accurate selection and ranking for unknown patterns. At the same time, the system user is able to validate the output of the ANN under all possible input conditions. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.

Electric Power Systems Research, 2000

Fast estimation of bus voltage magnitude is essential for security monitoring and analysis of pow... more Fast estimation of bus voltage magnitude is essential for security monitoring and analysis of power system. An approach based on a parallel self-organising hierarchical neural network (PSHNN) is proposed to estimate bus voltage magnitudes at all the PQ buses of a power system in an efficient manner. PSHNN is a multi-stage neural network in which stages operate in parallel rather than in series during testing. The revised back-propagation algorithm is used for learning input non-linearities along with forward-backward training of stage neural networks. A method based on Euclidean distance clustering is proposed for feature selection. Effectiveness of the proposed method is compared with two existing methods of feature-selection entropy based and angular distance based clustering methods for bus voltage magnitude estimation at different loading conditions in the IEEE 30-bus system and a practical 75-bus Indian system. The PSHNN based on Euclidean distance based clustering method is found to be superior in terms of training time and error performance.

Electric Power Systems Research, 2000

Voltage contingency selection and ranking is performed to choose the contingencies that cause the... more Voltage contingency selection and ranking is performed to choose the contingencies that cause the worst voltage problems. In this paper, a cascade neural network based approach is proposed for fast voltage contingency screening and ranking. The developed cascade neural network is a combination of a filter module and a ranking module. All the selected contingency cases are applied to the filter module, which is trained to classify them either in critical contingency class or in non-critical contingency class using a modified BP algorithm. The screened critical contingencies are passed to the ranking module (four-layered feed-forward ANN) for their further ranking. The effectiveness of the proposed method is demonstrated by applying it for contingency screening and ranking at different loading conditions for IEEE 30-bus system and a practical 75-bus Indian system. Once trained, the cascade neural network gives fast and accurate screening and ranking for unknown patterns and will be suitable for on-line applications at energy management centre.

International Journal of Electrical Power & Energy Systems, 2000

In this paper, a hybrid neural network based approach is proposed for fast voltage contingency sc... more In this paper, a hybrid neural network based approach is proposed for fast voltage contingency screening and ranking. The developed hybrid neural network is a combination of a filter module and ranking modular neural network. All the selected contingency cases are applied to the filter module, which is trained to classify them either in critical contingency class or in non-critical

Computers & Electrical Engineering, 2000

Voltage stability problems have been one of the major concerns for electric power utilities due t... more Voltage stability problems have been one of the major concerns for electric power utilities due to increased interconnections and loading of the present day power system. Fast estimation of loadability margin is essential for evaluating on-line voltage stability condition of a power system. In this paper, an approach based on parallel self-organizing hierarchical neural network is presented to predict a maximum loadability margin which is an indication of the power system's proximity to voltage collapse. Parallel self-organizing hierarchical neural network (PSHNN) are multi-stage neural networks in which stages operate in parallel rather than in series during testing. The revised back-propagation algorithm is used, along with forward±backward training of stage neural networks. Input features for PSHNN are selected on the basis of entropy concept in one method while in the other method, real and reactive loads at critical buses are considered as the inputs for PSHNN. The proposed PSHNN based methods are compared by estimating the maximum loadability margin at dierent loading conditions in IEEE 30bus and a practical 75-bus Indian system. Entropy based PSHNN learns faster, at the same time it provides more accurate loadability margin estimation as compared to that based on critical buses. It is found to be suitable for on-line applications in energy management systems. #

Iee Proceedings-generation Transmission and Distribution, 1998

Increased interconnections and loading of power systems, sometimes, lead to insecure operation. S... more Increased interconnections and loading of power systems, sometimes, lead to insecure operation. Since insecure cases often represent the most severe threats to secure system operation, it is important that the user be provided with a measure for quantifying the severity of the cases both in planning and operational stages of a power system. The Euclidean distance to the closest secure operating point has been used as a measure of the degree of insecurity. Recently, artificial neural networks are proposed increasingly for complex and time-consuming problems of power system. This paper presents a parallel self-organised hierarchical neural network based approach for estimation of the degree of voltage insecurity. Angular distance based clustering is used to select the input features. The proposed method has been tested on IEEE 30-bus system and a practical 75-bus Indian system and found to be suitable for real time implementation in Energy management centre.

International Journal of Electrical Power & Energy Systems, 2001

On-line monitoring of the power system voltage security has become a vital factor for electric ut... more On-line monitoring of the power system voltage security has become a vital factor for electric utilities. This paper proposes a voltage contingency ranking approach based on parallel self-organizing hierarchical neural network (PSHNN). Loadability margin to voltage collapse following a contingency has been used to rank the contingencies. PSHNN is a multi-stage neural network where the stages operate in parallel rather than in series during testing. The number of ANNs required is drastically reduced by adopting a clustering technique to group contingencies of similar severity into one cluster. Entropy based feature selection has been employed to reduce the dimensionality of the ANN. Once trained, the proposed ANN model is capable of ranking the voltage contingencies under varying load conditions, on line. The effectiveness of the proposed method has been demonstrated by applying it for contingency ranking of IEEE 30-bus system and a practical 75-bus Indian system. q

Archives of Virology, 2004

The coat protein (CP) sequences of eleven Papaya ringspot virus (PRSV) isolates originating from ... more The coat protein (CP) sequences of eleven Papaya ringspot virus (PRSV) isolates originating from different locations in India were determined, analysed and compared with the sequences of other isolates of PRSV. The virus isolates from India exhibited considerable heterogeneity in the CP sequences. The CP-coding region varied in size from 840–858 nucleotides, encoding protein of 280–286 amino acids. Comparative sequence analysis revealed that the PRSV isolates originating from India were divergent up to 11%. Though the PRSV isolates were differentiated in to two clusters, yet the sequence variation could not be correlated with the geographical origin of the isolates. Implication of the sequence variation in the coat protein derived transgenic resistance in papaya is discussed.

Plant Disease, 2004

Papaya (Carica papaya L.) is an important fruit crop in Bangladesh. During surveys conducted in D... more Papaya (Carica papaya L.) is an important fruit crop in Bangladesh. During surveys conducted in Dhaka and Mymensingh regions from April to June 2003, >50% of papaya plants were observed to have leaf mottling, mosaic and mild distortion, and water-soaked streaks on petioles ...

Current Anthropology, 1981

RefDoc Bienvenue - Welcome. Refdoc est un service / is powered by. ...

Bulletin of The Seismological Society of America, 2008

ABSTRACT In the present study the attenuation of seismic-wave energy in and around the source are... more ABSTRACT In the present study the attenuation of seismic-wave energy in and around the source area of the Chamoli Earthquake of 29 March 1999 is estimated using aftershock data. Most of the analyzed events are from the vicinity of the main central thrust (MCT), which is a well-defined tectonic discontinuity in the Himalayas. The method of a single backscattering model is employed to calculate frequency dependent values of coda Q (Q(c)). A total of 30 aftershock events are used for Q(c) estimation at central frequencies 1.5, 3, 6, 9, 12, 18, and 24 Hz through five lapse-time windows from 10 to 50 see starting at double the travel time of the S wave. The observed Q(c) is strongly dependent on frequency, which indicates that the region is seismically and tectonically active with high heterogeneities. The variation of Q(c) has also been estimated at different lapse times to observe its effect with depth. The variation of Q(c) with frequency and lapse time shows that the lithosphere becomes more homogeneous with depth. Q(c)-values for higher frequencies increase very fast with depth within about the top 63 km of the lithosphere and then become more or less constant beyond this depth. This indicates that turbidity at higher frequency decays very fast with depth, and the mantle may be transparent to high-frequency waves. The variation of Q(c) at 1.5 Hz with lapse time matches quite well with those predicted by Gusev (1995). However, the frequency parameter n in the relation Q(c) = Q(0)f(n), where Q(0) = Q(c) at 1 Hz, does not follow the expected pattern given in his model. This could be due to faster depth decay of turbidity as mentioned previously.

Bulletin of The Seismological Society of America, 2009

The existing seismological network in the Kangra–Chamba sector has been upgraded with 12 three-co... more The existing seismological network in the Kangra–Chamba sector has been upgraded with 12 three-component digital seismometers to obtain new insight on the nature and sources of continued clustered seismicity in this part of northwest Himalaya. A combination of ...

Iee Proceedings-generation Transmission and Distribution, 1999

Voltage contingency screening and ranlung is performed to choose the contingencies that cause the... more Voltage contingency screening and ranlung is performed to choose the contingencies that cause the worst voltage problems. A knowledge-based conceptual neural network (KBCNN) is developed for fast voltage contingency selection and ranking. A recognised shortcoming of the ANN based approach is that the problem solving knowledge of ANN is represented in the connection weights, and hence is dlffcult for a human user to comprehend. One way to provide an understanding of the behaviour of neural networks is to extract rules that can be provided to the users. The rules extracted are used to build a knowledge-based connectionist network for learning and revision of rules. The knowledge-based neural network is applied for voltage contingency selection and ranlung in an IEEE 30-bus system and a practical 75-bus Indian system. Once trained, the KBCNN gives accurate selection and ranking for unknown patterns. At the same time, the system user is able to validate the output of the ANN under all possible input conditions. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.

Electric Power Systems Research, 2000

Fast estimation of bus voltage magnitude is essential for security monitoring and analysis of pow... more Fast estimation of bus voltage magnitude is essential for security monitoring and analysis of power system. An approach based on a parallel self-organising hierarchical neural network (PSHNN) is proposed to estimate bus voltage magnitudes at all the PQ buses of a power system in an efficient manner. PSHNN is a multi-stage neural network in which stages operate in parallel rather than in series during testing. The revised back-propagation algorithm is used for learning input non-linearities along with forward-backward training of stage neural networks. A method based on Euclidean distance clustering is proposed for feature selection. Effectiveness of the proposed method is compared with two existing methods of feature-selection entropy based and angular distance based clustering methods for bus voltage magnitude estimation at different loading conditions in the IEEE 30-bus system and a practical 75-bus Indian system. The PSHNN based on Euclidean distance based clustering method is found to be superior in terms of training time and error performance.

Electric Power Systems Research, 2000

Voltage contingency selection and ranking is performed to choose the contingencies that cause the... more Voltage contingency selection and ranking is performed to choose the contingencies that cause the worst voltage problems. In this paper, a cascade neural network based approach is proposed for fast voltage contingency screening and ranking. The developed cascade neural network is a combination of a filter module and a ranking module. All the selected contingency cases are applied to the filter module, which is trained to classify them either in critical contingency class or in non-critical contingency class using a modified BP algorithm. The screened critical contingencies are passed to the ranking module (four-layered feed-forward ANN) for their further ranking. The effectiveness of the proposed method is demonstrated by applying it for contingency screening and ranking at different loading conditions for IEEE 30-bus system and a practical 75-bus Indian system. Once trained, the cascade neural network gives fast and accurate screening and ranking for unknown patterns and will be suitable for on-line applications at energy management centre.

International Journal of Electrical Power & Energy Systems, 2000

In this paper, a hybrid neural network based approach is proposed for fast voltage contingency sc... more In this paper, a hybrid neural network based approach is proposed for fast voltage contingency screening and ranking. The developed hybrid neural network is a combination of a filter module and ranking modular neural network. All the selected contingency cases are applied to the filter module, which is trained to classify them either in critical contingency class or in non-critical

Computers & Electrical Engineering, 2000

Voltage stability problems have been one of the major concerns for electric power utilities due t... more Voltage stability problems have been one of the major concerns for electric power utilities due to increased interconnections and loading of the present day power system. Fast estimation of loadability margin is essential for evaluating on-line voltage stability condition of a power system. In this paper, an approach based on parallel self-organizing hierarchical neural network is presented to predict a maximum loadability margin which is an indication of the power system's proximity to voltage collapse. Parallel self-organizing hierarchical neural network (PSHNN) are multi-stage neural networks in which stages operate in parallel rather than in series during testing. The revised back-propagation algorithm is used, along with forward±backward training of stage neural networks. Input features for PSHNN are selected on the basis of entropy concept in one method while in the other method, real and reactive loads at critical buses are considered as the inputs for PSHNN. The proposed PSHNN based methods are compared by estimating the maximum loadability margin at dierent loading conditions in IEEE 30bus and a practical 75-bus Indian system. Entropy based PSHNN learns faster, at the same time it provides more accurate loadability margin estimation as compared to that based on critical buses. It is found to be suitable for on-line applications in energy management systems. #

Iee Proceedings-generation Transmission and Distribution, 1998

Increased interconnections and loading of power systems, sometimes, lead to insecure operation. S... more Increased interconnections and loading of power systems, sometimes, lead to insecure operation. Since insecure cases often represent the most severe threats to secure system operation, it is important that the user be provided with a measure for quantifying the severity of the cases both in planning and operational stages of a power system. The Euclidean distance to the closest secure operating point has been used as a measure of the degree of insecurity. Recently, artificial neural networks are proposed increasingly for complex and time-consuming problems of power system. This paper presents a parallel self-organised hierarchical neural network based approach for estimation of the degree of voltage insecurity. Angular distance based clustering is used to select the input features. The proposed method has been tested on IEEE 30-bus system and a practical 75-bus Indian system and found to be suitable for real time implementation in Energy management centre.

International Journal of Electrical Power & Energy Systems, 2001

On-line monitoring of the power system voltage security has become a vital factor for electric ut... more On-line monitoring of the power system voltage security has become a vital factor for electric utilities. This paper proposes a voltage contingency ranking approach based on parallel self-organizing hierarchical neural network (PSHNN). Loadability margin to voltage collapse following a contingency has been used to rank the contingencies. PSHNN is a multi-stage neural network where the stages operate in parallel rather than in series during testing. The number of ANNs required is drastically reduced by adopting a clustering technique to group contingencies of similar severity into one cluster. Entropy based feature selection has been employed to reduce the dimensionality of the ANN. Once trained, the proposed ANN model is capable of ranking the voltage contingencies under varying load conditions, on line. The effectiveness of the proposed method has been demonstrated by applying it for contingency ranking of IEEE 30-bus system and a practical 75-bus Indian system. q