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Chinchanikar S.,Vishwakarma Institute of Information Technology | Choudhury S.K.,Indian Institute of Technology Kanpur
International Journal of Advanced Manufacturing Technology | Year: 2015

This study attempts to develop a cutting force model under oblique cutting conditions considering tool wear effect during turning of hardened steel with coated carbide tools to address metal cutting issues such as tool life, dimensional accuracy, and surface finish. Forces in worn out tool have been modeled by summing up forces in sharp tool and the forces due to flank wear alone. Waldorf’s orthogonal force modeling approach is extended to 3-D cutting force analysis to model cutting forces due to flank wear alone. Forces in sharp tool and average interface temperature generated during machining are modeled using regression analysis. Shear flow stress, shear angle, and other model prerequisites are determined using equivalent cutting edge geometry in place of the actual cutting edge of tools having nose radius. Worn tool cutting force model is validated in turning of hardened AISI 4340 steel at different levels of hardness 35 and 45 HRC, respectively, using multi-layer TiCN/Al2O3/TiN-coated carbide tools. The quantitative agreement between experimental result and that of a developed model of worn tool cutting forces is favorably good with an average error of less than ±5 % with a maximum error of 11 %, which showed that the developed model is reliable and could be used effectively for predicting the forces in worn out tool within the domain of the cutting parameters. © 2015 Springer-Verlag London Source


Londhe S.,Vishwakarma Institute of Information Technology | Charhate S.,Datta Meghe College of Engineering
Hydrological Sciences Journal | Year: 2010

Accurate forecasting of streamflow is essential for the efficient operation of water resources systems. The streamflow process is complex and highly nonlinear. Therefore, researchers try to devise alterative techniques to forecast streamflow with relative ease and reasonable accuracy, although traditional deterministic and conceptual models are available. The present work uses three data-driven techniques, namely artificial neural networks (ANN), genetic programming (GP) and model trees (MT) to forecast river flow one day in advance at two stations in the Narmada catchment of India, and the results are compared. All the models performed reasonably well as far as accuracy of prediction is concerned. It was found that the ANN and MT techniques performed almost equally well, but GP performed better than both these techniques, although only marginally in terms of prediction accuracy in normal and extreme events. © 2010 IAHS Press. Source


Patil K.,Vishwakarma Institute of Information Technology | Frederik B.,Mozilla Corporation
International Journal of Network Security | Year: 2016

Content Security Policy (CSP) is a browser security mechanism that aims to protect websites from content injection attacks. To adopt CSP, website developers need to manually compile a list of allowed content sources. Nearly all websites require modifications to comply with CSP's default behavior, which blocks inline scripts and the use of the eval() function. Alternatively, websites could adopt a policy that allows the use of this unsafe functionality, but this opens up potential attack vectors. In this paper, our measurements on a large corpus of web applications provide a key insight on the amount of efforts web developers required to adapt to CSP. Our results also identified errors in CSP policies that are set by website developers on their websites. To address these issues and make adoption of CSP easier and error free, we implemented UserCSP a tool as a Firefox extension. The UserCSP uses dynamic analysis to automatically infer CSP policies, facilitates testing, and gives savvy users the authority to enforce client-side policies on websites. Source


Prasad J.R.,Vishwakarma Institute of Information Technology | Kulkarni U.V.,Shri Guru Gobind Singhji Institute of Engineering and Technology
Proceedings - International Conference on Electronic Systems, Signal Processing, and Computing Technologies, ICESC 2014 | Year: 2014

This paper presents implementation of an Adaptive Neuro Fuzzy Classifier (ANFC) for recognition of isolated handwritten characters of Gujrati based on [2]. Authors aim to compare the performance of ANFC with weighted k-NN classifier proposed in [1] by them. Fuzzy classification is the task of partitioning a feature space into fuzzy classes. Authors exploit the method of employing adaptive networks based on [2] to solve a fuzzy classification problem. System parameters, such as the membership functions defined for each feature and the parameterized t-norms used to combine conjunctive conditions are calibrated with back propagation. Towards this aim, authors use a supervised learning procedure based on Scaled Conjugate Gradient (SCG) algorithm to update parameters in an adaptive network. Next, this architecture is deployed for the character recognition problem. From the experimental results, it is summarized that although adaptively adjusted classifier performs well as far as time complexity is concerned but fails to achieve better recognition rates than weighted k-NN. The results are discussed from the viewpoint of feature extraction methods discussed in [1] and their effectiveness on neuro fuzzy classifiers. © 2014 IEEE. Source


Prasad J.R.,Vishwakarma Institute of Information Technology | Kulkarni U.,Shri Guru Gobind Singhji Institute of Engineering and Technology
International Journal of Machine Learning and Cybernetics | Year: 2015

Recognition of Indian scripts is a challenging problem and work towards development of an OCR for handwritten Gujarati, an Indian script is still in infancy. This paper implements an Adaptive Neuro Fuzzy Classifier (ANFC) for Gujarati character recognition using fuzzy hedges (FHs). FHs are trained with other network parameters by scaled conjugate gradient training algorithm. The tuned fuzzy hedge values of fuzzy sets improve the flexibility of fuzzy sets; this property of FH improves the distinguishability rates of overlapped classes. This work is further extended for feature selection based on FHs. The values of fuzzy hedges can be used to show the importance of degree of fuzzy sets. According to the FH value, the redundant, noisily features can be eliminated, and significant features can be selected. An FH-based feature selection algorithm is implemented using ANFC. This paper aims to demonstrate recognition of ANFC-FH and improved results of the same with feature selection. © 2014, Springer-Verlag Berlin Heidelberg. Source

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