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Articles 1 - 27 of 27
Full-Text Articles in Engineering
Correlation Between Energy Distribution Profile And Lev El Of Consciousness, D. K. Chaturvedi Dr.
Correlation Between Energy Distribution Profile And Lev El Of Consciousness, D. K. Chaturvedi Dr.
D. K. Chaturvedi Dr.
The purpose of this experimental study was to determine the energy levels corresponding to the different focal points (Chakras or centers) of the body, which is related with consciousness. In this experiment, the energy level is measured at four different centers, namely, the navel center, the heart center, the throat center and the eye center of the Human beings. The measurements have been taken using Energy Measurement System (EMS) developed at Department of Electrical Engineering, Dayalbagh Education Institute, Dayalbagh, Agra, India, which works on the principle of tissue resistance of body. The probe was placed at different centers and subsequent …
Matlab Notes, D. K. Chaturvedi Dr.
Matlab Notes, D. K. Chaturvedi Dr.
D. K. Chaturvedi Dr.
Matlab includes: • Math and computation • Algorithm development • Modeling, simulation, and prototyping • Data analysis, exploration, and visualization • Scientific and engineering graphics • Application development, including graphical user interface building
Matlab Examples, D. K. Chaturvedi Dr.
Matlab Examples, D. K. Chaturvedi Dr.
D. K. Chaturvedi Dr.
These examples demonstrates how to use MATLAB to model a simple physics problem faced by a college students.
Virtual Power Lab - D.E.I. Dayalbagh, D. K. Chaturvedi Dr.
Virtual Power Lab - D.E.I. Dayalbagh, D. K. Chaturvedi Dr.
D. K. Chaturvedi Dr.
Electrical Machines and Power systems are the back bone of electrical engineering and play a vital role in industry. Hence, it is essential for electrical engineering students to learn the concepts of power systems and machines. Unfortunately, the students are loosing interest in lab work due to various reasons like unavialability of experimental setups, lack of qualified and motivated lab staff, lab timing, etc. To overcome these problems the virtual Power labs are very important to impart quality experiments.
Dayalbagh Educational Institute Soft Computing Edge Cutting Technology Lab (Deisel), D. K. Chaturvedi
Dayalbagh Educational Institute Soft Computing Edge Cutting Technology Lab (Deisel), D. K. Chaturvedi
D. K. Chaturvedi Dr.
Dayalbagh Educational Institute Soft Computing Edge Cutting Technology Lab (DEISEL) Group consiting of a Professor Incharge, four Teaching Staff members, five Non-Teaching Staff members, five Ph.D. Students, six M. Tech. Students. The objective of DEISEL is to to exploit the tolerance for imprecision uncertainty, approximate reasoning and partial truth to achieve tractability, robustness, low solution cost, and close resemblance with human like decision making to find an approximate solution to an imprecisely/precisely formulated problem. The challenge is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. This, in essence, …
Parameters Estimation Of A Fan System Using Artificial Neural Networks (Anns),, D. K. Chaturvedi
Parameters Estimation Of A Fan System Using Artificial Neural Networks (Anns),, D. K. Chaturvedi
D. K. Chaturvedi Dr.
Electric Fans are very commonly used in the industries, domestic applications and in tunnels for cooling and ventila-tion purposes. Fan parameters estimation is an important task as far as the reliable operation of a fan system is con-cerned. Basically, a fan is mainly consisting of a single phase induction motor and therefore fan system parameters are essentially the electrical parameters e.g. resistances, reactances and some load parameters (fan blades).These parame-ters often change under varying operating conditions and the knowledge of these parameters is necessary to have opti-mum and efficient operation of the system. Therefore, fan system parameters are required to …
Short-Term Load Forecasting Using Soft Computing Techniques, D. K. Chaturvedi
Short-Term Load Forecasting Using Soft Computing Techniques, D. K. Chaturvedi
D. K. Chaturvedi Dr.
Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand is increased with high growth rate. In this paper, a short-term load forecasting realized by a generalized neu-ron–wavelet method is proposed. The proposed method consists of wavelet transform and soft computing technique. The wavelet transform splits up load time series into coarse and detail components to be the fea-tures for soft computing techniques using Generalized Neurons Network (GNN). The soft computing tech-niques forecast each component separately. The …
Programs Of Fuzy Systems, D. K. Chaturvedi
Programs Of Fuzy Systems, D. K. Chaturvedi
D. K. Chaturvedi Dr.
The zip file contains c programs of fuzzy system.
Matlab Program Of Genetic Algorithms, D. K. Chaturvedi
Matlab Program Of Genetic Algorithms, D. K. Chaturvedi
D. K. Chaturvedi Dr.
The zip file contains Matlab program of genetic algorithms and their varients.
Ann /Gn Programs, D. K. Chaturvedi
Ann /Gn Programs, D. K. Chaturvedi
D. K. Chaturvedi Dr.
The file contains programms of multi layer feedforward backpropagation ANN, GN and their varients.
Polar Fuzzy Logic Based Power System Stabilizer, D. K. Chaturvedi
Polar Fuzzy Logic Based Power System Stabilizer, D. K. Chaturvedi
D. K. Chaturvedi Dr.
A neuro fuzzy logic based adaptive power system stabilizer (AFPSS) has been developed using angularspeed deviation and angular acceleration as the input variables. It consists of two main parts, namely, fuzzylogic controller (FLC) and generalized neuron (GN) based identifier. The inference mechanism of the fuzzylogic controller is represented by rule-base and a data-base. Three parameters have been introduced to tunethe FPSS. These parameters are decided on the basis of GN-identifier output and present system conditions.This mechanism of tuning the AFPSS makes the fuzzy logic based power system stabilizer adaptive tochanges with the operating conditions. Therefore, the degradation of the system …
Generalized Neuron Based Pss And Adaptive Pss, D. K. Chaturvedi, O. P. Malik
Generalized Neuron Based Pss And Adaptive Pss, D. K. Chaturvedi, O. P. Malik
D. K. Chaturvedi Dr.
Artificial neural networks can be used as intelligent controllers to control non-linear, dynamic systems through learning, which can easily accommodate the non-linearities and time dependencies. However, they require large training time and large number of neurons to deal with complex problems. Taking benefit of the characteristics of a Generalized Neuron that requires much smaller training data and shorter training time, a Generalized Neuron-Based Power System Stabilizer (GNPSS) and an adaptive version of the same have been developed. The objective of this paper is to compare the performance of the GNPSS with that of an adaptive version, the weights of which …
Dynamic Model Of Hiv/Aids Population Of Agra Region, D. K. Chaturvedi
Dynamic Model Of Hiv/Aids Population Of Agra Region, D. K. Chaturvedi
D. K. Chaturvedi Dr.
The Human Immunodeficiency Virus / Acquired Immunodeficiency syndrome (HIV/AIDS) is spreading rapidly in all regions of the world. But in India it is only 20 years old. Within this short period it has emerged as one of the most serious public health problems in the country, which greatly affect the socio-economical growth. The HIV problem is very complex and ill defined from the modeling point of view. Keeping in the view the complexities of the HIV infection and its transmission, it is difficult to make exact estimates of HIV prevalence. It is more so in the Indian context, with its …
A Generalized Neuron Based Adaptive Power System Stabilizer For Multimachine Environment, D. K. Chaturvedi, O. P. Malik
A Generalized Neuron Based Adaptive Power System Stabilizer For Multimachine Environment, D. K. Chaturvedi, O. P. Malik
D. K. Chaturvedi Dr.
Artificial neural networks can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. Taking advantage of the characteristics of a generalized neuron (GN), that requires much smaller training data and shorter training time, a GN-based adaptive power system stabilizer (GNAPSS) is proposed. It consists of a GN as an identifier, which predicts the plant dynamics one step ahead, and a GN as a controller to damp low frequency oscillations. Results of studies with a GN-based PSS on a five-machine power system show that it can provide good damping …
A Generalized Neuron Based Pss In A Multi-Machine Power System, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
A Generalized Neuron Based Pss In A Multi-Machine Power System, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
D. K. Chaturvedi Dr.
An artificial neural network can work as an intelligent controller for nonlinear dynamic systems through learning, as it can easily accommodate the nonlinearities and time dependencies. In dealing with complex problems, most common neural networks have some drawbacks of large training time, large number of neurons and hidden layers. These drawbacks can be overcome by a nonlinear controller based on a generalized neuron (GN) which retains the quick response of neural net. Results of studies with a GN-based power system stabilizer on a five-machine power system show that it can provide good damping over a wide operating range and significantly …
Applications Of Generalised Neural Network For Aircraft Landing Control System, D. K. Chaturvedi, R. Chauhan, P. K. Kalra
Applications Of Generalised Neural Network For Aircraft Landing Control System, D. K. Chaturvedi, R. Chauhan, P. K. Kalra
D. K. Chaturvedi Dr.
It is observed that landing performance is the most typical phase of an aircraft perfromance. During landing operation the stability and controllability are the major considerations. To achieve safe landing, an aircraft has to be controlled in such a way that its wheels touch the ground comfortably and gently within the paved surface of the runway. The conventional control theory found very successful in solving well defined problems, which are described precisely with definite and clearly mentioned boundaries. In real life systems the boundaries can't be defined clearly and conventional controller does not give satisfactory results. Whenever, an aircraft deviates …
Improved Generalized Neuron Model For Short Term Load Forecasting, D. K. Chaturvedi, Ravindra Kumar, P. K. Kalra
Improved Generalized Neuron Model For Short Term Load Forecasting, D. K. Chaturvedi, Ravindra Kumar, P. K. Kalra
D. K. Chaturvedi Dr.
The conventional neural networks consisting of simple neuron models have various drawbacks like large training time for complex problems, huge data requirement to train non linear complex problems, unknown ANN structure, the relatively larger number of hidden nodes required, problem of local minima etc. To make the Artificial Neural Network more efficient and to overcome the above-mentioned problems the new improved generalized neuron model is proposed in this work. The proposed neuron models have both summation and product as aggregation function. The generalized neuron models have flexibility at both the aggregation and activation function level to cope with the non-linearity …
A Generalized Neuron Based Adaptive Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
A Generalized Neuron Based Adaptive Power System Stabilizer, D. K. Chaturvedi, O. P. Malik, P. K. Kalra
D. K. Chaturvedi Dr.
Artificial neural networks (ANNs) can be used as intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies. However, they require long training time and large numbers of neurons to deal with complexproblems. To overcome these drawbacks, a generalised neuron (GN) has been developed that requires much smaller training data and shorter training time. Taking benefit of these characteristics of the GN, a new generalised neuron-based adaptive power system stabiliser (GNPSS) is proposed. The GNPSS consists of a GN as an identifier, which tracks the dynamics of the plant, and a GN …
Neuro-Fuzzy Approach For Development Of New Neuron Model, Manmohan, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
Neuro-Fuzzy Approach For Development Of New Neuron Model, Manmohan, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
The training time of ANN depends on size of ANN (i.e. number of hidden layers and number of neurons in each layer), size of training data, their normalization range and type of mapping of training patterns (like X–Y, X–DY, DX–Y and DX–DY), error functions and learning algorithms. The efforts have been done in past to reduce training time of ANN by selection of an optimal network and modification in learning algorithms. In this paper, an attempt has been made to develop a new neuron model using neuro-fuzzy approach to overcome the problems of ANN incorporating the features of fuzzy systems …
Development Of Hiv Model And Its Simulation, D. K. Chaturvedi, Pritam Singh, S. K. Gaur, D. S. Mishra
Development Of Hiv Model And Its Simulation, D. K. Chaturvedi, Pritam Singh, S. K. Gaur, D. S. Mishra
D. K. Chaturvedi Dr.
The article discusses model developmetn for HIV infected population using the System Dynamics technique. The technique has an advantage over conventional modeling technique as it is dependent more on causal relationships that involve qualitative and quantitative variables to model socioeconomic problems of a complex nature. The model has been simulated and the results haven been compared witht he available data.
Artificial Neural Network Learning Using Improved Genetic Algorithms, D. K. Chaturvedi
Artificial Neural Network Learning Using Improved Genetic Algorithms, D. K. Chaturvedi
D. K. Chaturvedi Dr.
The feedforward back-propagation artificial neural networks (ANN) are widely used to control the various industrial process, for modelling, simulation of systems and forecasting. The backpropagation learning has various drawbacks such as slowness in learning, stuck in local minima, requies functional derivative of aggregation function and thresholding function to minimize error function. Various researchers have suggested a number of improvement in simple back-propagation learning algorithm developed by Widrow and Holf in 1956. In this paper, a program is developed for feedforward artificial neural network with genetic algorithm (GA) as the learning mechanism to overcome some of the disadvantages of back-propagation learning …
Fuzzified Neural Network Approach For Load Forecasting Problems, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
Fuzzified Neural Network Approach For Load Forecasting Problems, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
In load forecasting, the operator or the concerned person uses his or her experience and intuitions to obtain a good guess of the load demand. This guess is normally supported by sophisticated mathematical prediction techniques. The short term load not only varies from hour to hour, but is also influenced by the nature of events, load demand, the type of the load considered, seasonal variations, weekend day or holidays, and also by sudden demand and loss of load. Accordingly, it is quite clear that the electrical load-forecasting problem is quite difficult to model with mathematical difference or differential equations. In …
Load Frequency Control: A Generalized Neural Network Approach, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
Load Frequency Control: A Generalized Neural Network Approach, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
Variation in load frequency is an index for normal operation of power systems. When load perturbation takes place anywhere in any area of the system, it will affect the frequency at other areas also. To control load frequency of power systems various controllers are used in different areas. but due to non-linearities in the system components and alternators, these controllers cannot control the frequency quickly and efficiently. Simple neural networks which are in common use at present have various drawbacks like large training time, requirement of large number of neurons, etc. The present work deals with the developmetn of a …
A Fuzzy Simulation Model Of Basic Commutating Electrical Machines, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
A Fuzzy Simulation Model Of Basic Commutating Electrical Machines, D. K. Chaturvedi, P. S. Satsangi, P. K. Kalra
D. K. Chaturvedi Dr.
Fuzzy Logic as applied to a great extent in controlling the process, plants and various complex systems, due to its inherent advantages like simplicity, ease in design, robustness and adaptivity. Aslo it is established that this approach works very well especially when the systems are not transparent. In this paper the approach is used for the modelling and simulation of an electrical machine to predict the behaviour of the machine under running conditionsas well as unde starting conditions. The starting characteristics of electrical machines are non-linear in nature, and it is very difficult to model them accurately. Also the developed …
Load Forecasting Using Genetic Algorithms, D. K. Chaturvedi, R. K. Mishra, A. Agarwal
Load Forecasting Using Genetic Algorithms, D. K. Chaturvedi, R. K. Mishra, A. Agarwal
D. K. Chaturvedi Dr.
Genetic Algorithms (GAs) are gaining popularity in many engineering and scientific applications due to their enormous advantages such as adaptibility, ability to handle non-linear, ill defined and probabilistic problems. In this paper load forecasting problem on long term basis is formulated in the frame work of Genetic Algorithms. The results of GAs are compared with the central Electricity Authority (CEA) forecasted data to demonstrate the effectiveness of the proposed algorithms.
Simulation Of Temperature Variation In Parachute Inflation, D. K. Chaturvedi
Simulation Of Temperature Variation In Parachute Inflation, D. K. Chaturvedi
D. K. Chaturvedi Dr.
In this paper, the variation of temperature during parachute inflation is simulated by system dynamics methodology. In this methodology, causal loops for system have been identified and flow diagram is drawn. Flow diagram consists of flow rate variables, level variables and auxiliary variables. In causal mechanism principal feedback loops are identified. It also simplfies illustration due to various influencing factors such as pressure, rate of pressure, mass of parachute, textile characteristics, motion of folded parachute to compresion under pressure and interaction between parachute and its container/bag.
Possible Applications Of Neural Nets To Power System Operation And Control, P. K. Kalra, Alok Srivastava, D. K. Chaturvedi
Possible Applications Of Neural Nets To Power System Operation And Control, P. K. Kalra, Alok Srivastava, D. K. Chaturvedi
D. K. Chaturvedi Dr.
Problems related to power system operation and control are complex and time consuming because of the non-linearities involved in their formulation and solution. Fast solutions to these problems can be obtained only through parallel processing. Neural nets provide massive parallel processing facilities and may also be used efficiently to model systems with non-linearities. The capabilities of neural nets can, therefore, be well utilized in modelling and processing problems related to power systems. In order to reduce the burden on computers, algorithms involving optimization and complex equations can be converted to heuristics. These heuristics can then be represented in terms of …