A Synchrophasor-Based Line Protection for Single Phase-Ground Faults

Babu N. V. Phanendra,Babu P. Suresh,Roy Saptarshi,Babu T. Sudhakar,Bharadwaj Anil 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3

Synchrophasor measurement data enhances the transmission line protection. This paper proposes an improved line protection against single phase-ground faults using synchronized phasor data. This algorithm prevents the relay mal-operation caused by high fault resistance. This algorithm calculates the phase difference between relay point voltage and fault point voltage based on the relation between negative sequence of relay point current and fault point current. After, the calculated phase difference between relay point voltage and fault point voltage will be compared with set point voltage phase referred from the relay point voltage phase. The fault detection action will be taken according to a certain phase difference relation between fault point voltage and set point voltage. This method is then applied to a practical single machine single line system. The results show that the suggested algorithm could determine in-line faults accurately with less computational time. It also has proved that this method is immune to the fault resistance, system conditions.

Existence of common fixed points of generalized almost weakly contractive maps

G. V. R. Babu,P. D. Sailaja 장전수학회 2013 Proceedings of the Jangjeon mathematical society Vol.16 No.1

We introduce the concept of `generalized almost weakly contractivemaps', which include both the class of `generalized weakly contractivemaps' and the class of all maps satisfying `generalized condition (B)',and we prove a common ¯xed point theorem for a pair of selfmapssatisfying generalized almost weakly contractive condition. Thistheorem generalizes the result of Abbas, Babu and Alemayehu [2]. Wealso answer an open question posed by Abbas, Babu and Alemayehu[2] in an a±rmative way.

An Optimized Deep Learning Techniques for Analyzing Mammograms

Satish Babu Bandaru,Natarajasivan. D,Rama Mohan Babu. G International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.7

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

Clinicopathological Study of 18 Cases of Inflammatory Myofibroblastic Tumors with Reference to ALK-1 Expression: 5-Year Experience in a Tertiary Care Center

Ramesh Babu Telugu,Anne Jennifer Prabhu,Nobin Babu Kalappurayil,John Mathai,Birla Roy Gnanamuthu,Marie Therese Manipadam 대한병리학회 2017 Journal of Pathology and Translational Medicine Vol.51 No.3

Background: Inflammatory myofibroblastic tumor is a histopathologically distinctive neoplasm of children and young adults. According to World Health Organization (WHO) classification, inflammatory myofibroblastic tumor is an intermediate-grade tumor, with potential for recurrence and rare metastasis. There are no definite histopathologic, molecular, or cytogenetic features to predict malignant transformation, recurrence, or metastasis. Methods: A 5-year retrospective study of histopathologically diagnosed inflammatory myofibroblastic tumors of various anatomic sites was conducted to correlate anaplastic lymphoma kinase-1 (ALK-1) expression with histological atypia, multicentric origin of tumor, recurrence, and metastasis. Clinical details of all the cases were noted from the clinical work station. Immunohistochemical stains for ALK-1 and other antibodies were performed. Statistical analysis was done using Fisher exact test. Results: A total of 18 cases of inflammatory myofibroblastic tumors were found during the study period, of which 14 were classical. The female-male ratio was 1:1 and the mean age was 23.8 years. Histologically atypical (four cases) and multifocal tumors (three cases, multicentric in origin) were noted. Recurrence was noted in 30% of ALK-1 positive and 37.5% of ALK-1 negative cases, whereas metastasis to the lung, liver, and pelvic bone was noted in the ALK-1 positive group only. Conclusions: Overall, ALK-1 protein was expressed in 55.6% of inflammatory myofibroblastic tumors. There was no statistically significant correlation between ALK-1 expression, tumor type, recurrence and metastasis. However, ALK-1 immunohistochemistry is a useful diagnostic aid in the appropriate clinical and histomorphologic context.

A Hybrid Optimized Deep Learning Techniques for Analyzing Mammograms

Bandaru, Satish Babu,Deivarajan, Natarajasivan,Gatram, Rama Mohan Babu International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.10

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

A Review on Advanced Methodologies to Identify the Breast Cancer Classification using the Deep Learning Techniques

Bandaru, Satish Babu,Babu, G. Rama Mohan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

Existence of common ¯xed points for weakly biased maps satisfying weakly contractive condition involving rational expressions

G. V. R. BABU,G. N. ALEMAYEHU 장전수학회 2011 Proceedings of the Jangjeon mathematical society Vol.14 No.1

We prove a common ¯xed point theorem for two pairs of selfmaps in a complete metric space in which one pair is compatible and reciprocally continuous and the other one is a pair of weakly biased maps, satisfying weakly contractive condition involving rational expressions. Also, it is proved that the same is true when `compatibile maps' is replaced by `compatible maps of type (A)'. These results partially generalize the results of Babu and Kameswari [3] and Jeong and Rhoades [5].

Common fixed point theorems for a pair of compatible maps using a control function

G. V. R. Babu,K. N. V. V. Vara Prasad 장전수학회 2007 Proceedings of the Jangjeon mathematical society Vol.10 No.1

The purpose of this paper is to establish a common fixed point theorem for a pair of selfmaps of a complete metric space by applying the technique altering distances between the points, using a control function, which is our main result. Also it is extended to a sequence of selfmaps. These results generalize the results of Babu and Ismail [1], Is´eki [2], Singh [17] and Som [18].

AN ASYMPTOTIC FINITE ELEMENT METHOD FOR SINGULARLY PERTURBED HIGHER ORDER ORDINARY DIFFERENTIAL EQUATIONS OF CONVECTION-DIFFUSION TYPE WITH DISCONTINUOUS SOURCE TERM

Babu, A. Ramesh,Ramanujam, N. Korean Society of Computational and Applied Mathem 2008 Journal of applied mathematics & informatics Vol.26 No.5

We consider singularly perturbed Boundary Value Problems (BVPs) for third and fourth order Ordinary Differential Equations(ODEs) of convection-diffusion type with discontinuous source term and a small positive parameter multiplying the highest derivative. Because of the type of Boundary Conditions(BCs) imposed on these equations these problems can be transformed into weakly coupled systems. In this system, the first equation does not have the small parameter but the second contains it. In this paper a computational method named as 'An asymptotic finite element method' for solving these systems is presented. In this method we first find an zero order asymptotic approximation to the solution and then the system is decoupled by replacing the first component of the solution by this approximation in the second equation. Then the second equation is independently solved by a fitted mesh Finite Element Method (FEM). Numerical experiments support our theoritical results.

Adaptive Firefly Algorithm based OPF for AC/DC Systems

Babu, B. Suresh,Palaniswami, S. The Korean Institute of Electrical Engineers 2016 Journal of Electrical Engineering & Technology Vol.11 No.4

Optimal Power Flow (OPF) is an important operational and planning problem in minimizing the chosen objective functions of the power systems. The recent developments in power electronics have enabled introduction of dc links in the AC power systems with a view of making the operation more flexible, secure and economical. This paper formulates a new OPF to embrace dc link equations and presents a heuristic optimization technique, inspired by the behavior of fireflies, for solving the problem. The solution process involves AC/DC power flow and uses a self adaptive technique so as to avoid landing at the suboptimal solutions. It presents simulation results of IEEE test systems with a view of demonstrating its effectiveness.