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End date: 2017 × Start date: 2017 × Thesis Type: M.Tech ×
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    ThesisItemOpen Access
    Optimal reservoir operation for hydropower generation considering turbine characteristics
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-10) Purohit, Chetna; Mahar, P.S.
    In today's world distribution and use of water being a major issue of concern, optimal reservoir operation has become necessary to accommodate water according to various needs. In reservoir operation, optimal release is determined with time period. Hydropower generation is one of the major activity for which the releases from a reservoir are used. Reservoir operation can be achieved in two ways, standard operating policy and optimal operating policy. In this study, a mathematical model has been developed using nonlinear programming for determining optimal reservoir release with maximizing the generated hydropower. Equations representing the continuity, release, storage, head, water elevation and the turbine characteristics have been imposed as constraints in the optimization model. The solution of the developed model provides generated hydropower and optimal reservoir releases. Applicability of the developed optimization model has been illustrated for the reservoir created by Tehri dam in Uttarakhand. The effect of the turbine characteristics has been investigated in the optimal generation of hydropower. The optimal reservoir release and generated hydropower were obtained for ten years as the desired output. From the results, it is inferred that the generated hydropower is more for the same value of release when turbine characteristics are considered.
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    ThesisItemOpen Access
    An ensemble based classification approach for credibility analysis of online news by detecting clickbait news headlines
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Agarwal, Parul; Samantaray, S.D.
    The present work proposes a methodology for detecting clickbait news headlines in online news media using Ensemble based classification Technique. In this era of Digitization, presenting news now became online. Everyone is accessing online news by one or other medium. When online news is so popular and easily accessible, it also makes online news vulnerable too. Anyone can write anything in the name of news and it becomes viral whether it is informative or not. Due to the high competition and thrust of clicks, clickbait headlines are manufactured just to attract readers to click. These headlines generate enough curiosity by using some tactics so that readers compelled to click on the link to fill the knowledge gap. Clickbait headlines are compromising the meaning of true journalism. The present work is aimed to develop a clickbait detection system for analyzing the credibility of online news. So that the readers become aware and do not click on these links. News headlines are a piece of text, hence the proposed task is divided into two subtasks; Text analysis and classification. Text analysis is done for the transformation of text into numerical features usable for machine learning. These numerical features are then used for training the ensemble based classifier. The training dataset contains 10000 clickbait and 10000 non-clickbait headlines. Python 2.7 is used for the programming and system is tested for 10600 news headlines which are in an even distribution of 5800 clickbait and nonclickbait headlines and gained 93.13% accuracy. This system is also validated using k-fold cross validation technique.
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    ThesisItemOpen Access
    Classification of Glaucoma and Bright Lesions in Retinal Fundus Images using SVM
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Upadhyaya, Himanshu; Negi, Chetan Singh
    In terms of population, India stands at the second position in the world, and with such a huge population it is very difficult to provide medical assistance to each and every one especially to those living in the remote areas. As these retinal diseases require regular check-ups and timely intervention to control the progress of disease, an ophthalmologist with all the medical equipment is required which could be highly expansive. Therefore an automated clinical support system should be developed for the diagnosis of retinal diseases like glaucoma and diabetic retinopathy which could be used to make the screening of real time population easy and efficient and also identify those who are at risk in the early stages. This technique would minimize the cost, estimation time and also assist the ophthalmologist to perform the treatment plan. This thesis presents a classification system for the diagnosis of Glaucoma and Bright Lesions in retinal fundus images where different anatomical and statistical features are extracted and classified using SVM. It has been observed that the anatomical features proved to be a promising features as compared to the other statistical features and a good accuracy is achieved using SVM classification. In this thesis work the performance analysis of this classification system over different feature sets is reported and discussed.
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    ThesisItemOpen Access
    An additive noise suppression in noisy signal using spectral subtraction
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-07) Malik, Medha; Negi, Chetan Singh
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    ThesisItemOpen Access
    Fetching of texture from image using enhanced morphological component analysis
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Giri, Kapil; Pandey, Binay Kumar
    The Morphological Component Analysis (MCA) is a method which allows us to separate features contained in an image when these features present different morphological aspects. MCA can be used for image inpainting and image separation task. MCA can be very useful for decomposing images into texture and piecewise smooth (cartoon) parts. MCA followed by TV regularization scheme described by J-L Starck, M.Elad, D. L. Donoho is a very efficient method for separating image into its piecewise smooth content and its texture. Due to the use of curvelet dictionary in MCA piecewise smooth content part suffers from ringing artifact . TV regularization scheme was used to remove ringing artifact from the piecewise smooth part. In this work we aim at improving the MCA algorithm so that it can separate the piece wise smooth part and texture part more efficiently. The TV regularization scheme is approximated with Daubechies wavelet. Daubechies wavelet transform is applied over the cartoon part of the image then soft thresholding of the coefficient is done and then again the image is reconstructed by taking inverse Daubechies wavelet transform. Simulation result has shown that proposed method is giving better performance than the previous method in terms of various qualities metric such as Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM).
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    ThesisItemOpen Access
    Encoder-decoder based integrity verification for video surveillance
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Panwar, Amit; Singh, Rajeev
    Surveillance cameras are widely used anywhere to record video data. In order to counter the editing or copying by adversaries, assuring the integrity of the extracted video is one of the fundamental issues in this area. Video surveillance is increasing significance as organizations seek to safe guard physical and capital assets. At the same time, the necessity to observe more people, places, and things coupled with a desire to pull out more useful information from video data is motivating new demands for scalability, capabilities, and capacity. Two improved system are described for verifying video content integrity, one uses frame level integrity and other uses digital watermarking. Existing verification systems are unable to distinguish between attacks and regular modifications and are thus unsuitable countermeasures against actual threats. The first proposed method helps in identifying the distortion in the video data at the frame level. The second proposed method distinguishes attacks against video content from regular modifications by extracting time codes and header hash values embedded in the content itself and comparing them with the actual ones, making it well suited for content storage services. Evaluation showed that second method is more effective than the one using the digital signature scheme.
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    ThesisItemOpen Access
    Analysis of fraudulent in graph database for identification and prevention
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Rawat, Deepak Singh; Singh, Rajesh Shyam
    Because of the amazing addition of extortion which results in incredible loss of billions of cash far and wide consistently, a couple of current systems in perceiving distortion are reliably made and associated with various business fields. Extortion revelation incorporates watching the direction of customers with a particular ultimate objective to assess, distinguish, or counteract undesirable lead. Undesirable direct is a wide term including wrongdoing, deception, intrusion, and record defaulting. This examination displays a graph-based technique executed with graph database itself used protection from fraudulent. The purpose of this investigation is to perceive and maintain a strategic distance from deception if there ought to be an event of on the web and disconnected keeping money from the net transaction with a record using graph database. Meanwhile, we have endeavored to ensure that real exchanges are not dismissed by adjusting them to past example of fraudulent. These case studies are also providing information by matching pattern of fraudulent case with database entry. Banks are looking to minimize big data through misrepresentation identification and prevention frameworks. A wide range of cutting edge fraud innovations are being connected to fraudulent Internet banking transactions recognition and protection. In any case, they have no successful identification instrument to distinguish honest to goodness clients and follow their unlawful exercises. We consider a prototype to vanquish each one of these difficulties using the graph database.
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    ThesisItemOpen Access
    Performance and emission characteristics of a CI engine operating on pine oil-n-butanol-diesel blends
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Singh, Kailash; Gupta, V.K.
    In the present investigation, pine oil and n-butanol blends are used as an alternative fuel for diesel engine. The pine oil is non-edible forest based less viscous oil, which is produced by steam distillation of pine resin and pine needles. Pine oil is rich in high energy content and the production of pine oil does not affect main cultivation land and edible supply. n-butanol is a primary alcohol which has better fuel properties like energy content, kinematic viscosity and cold flow properties compared to methanol and ethanol. In the present study n-butanol is used because of its higher oxygen content and cold flow properties. Diesel fuel is taken as reference fuel in present study. The measured fuel properties of test fuels are found comparable with the diesel fuel. Performance and emission characteristics of a single cylinder, four stroke diesel engine of rated 1500 rpm, are investigated at different engine loads. The engine performance parameters such as fuel consumption and brake specific fuel consumption are decreased with addition of pine oil, however, average brake power and brake thermal efficiency has been increased. Exhaust gas temperature is found higher, while smoke emissions are improved for pine oil-diesel blends, as compared to diesel. When n-butanol is added to pine oil-diesel blends fuel consumption rate, BSFC, average brake power and brake thermal efficiency is increased. Exhaust gas temperature is reported higher at higher percentage of n-butanol, while smoke density reduced significantly for pine oil-n-butanoldiesel blends. From the present investigation, P20B30 fuel is found to give optimum engine performance and emission characteristics.
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    ThesisItemOpen Access
    A novel approach for mapping of a boolean function using artificial neural network
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-07) Sharma, Jalaj; Samantaray, S.D.
    Boolean functions have enormous importance in the field of Computer Engineering. These functions are important not only because of the fact that computer hardware architecture is based on them but also because of ever-increasing automation using devices like programmable logical controllers that utilize such functions in ladder programming. Another aspect of the importance of Boolean functions is the ability to transform any number to binary form for the purpose of any kind of processing or analysis of the data. Owing to this fact, various methods for representation of Boolean functions have been suggested in the literature. The input-output relationship for the devices based on Boolean functions can be mapped using trained artificial neural networks. Artificial Neural Network (ANN) is an intelligent tool with parallel computational capability. Conventional ANN once designed needs to be trained using iterative training process. The proposed method for mapping of Boolean functions is advantageous because of its generality and ease of implementation. This method is based on a novel neural architecture known as Pi-Sigma neuron model. Although Pi-Sigma neuron model is complex but the proposed new Pi-Sigma neuron model named as Simplified Pi-Sigma neuron model reduces the complexity and makes the learning process simple and non-iterative. While the conventional neuron models have summation operation for aggregation, the proposed neuron model has multiplication as well as summation operations for representing aggregation of the dendritic inputs. Incorporation of the multiplication operation along with the summation operation is based on some biological evidences as observed by researchers in the field of computational neuroscience. As any Boolean function can be represented in terms of sum of products, the proposed neuron model is capable of representing any Boolean function because of its inherent nature of performing multiplication operations before performing summation operations. The advantage with this method is that it does not require a long process of iterations for training. Weights and biases are directly calculated by presenting the training data in a single stroke. The proposed model works primarily for Boolean functions, but it can be extended to any kind of functions by using the conversion of number systems along with this method of functional mapping.