Prof.(Dr.) Jagathy Raj V. P.
@cusat.ac.in
Director and Senior Professor, School of Management Studies
Cochin University of Science and Technology
EDUCATION
Ph. D Industrial Engineering and Management - Logistics and Supply Chain Management, Computer Simulation and Modeling Dept. of Industrial Engg. & Mgmt.
I I T, Kharagpur MAY, 2001
Diploma System Analysis and Data Processing Annamalai University 1992 First Class
M. B. A Operations and Systems Management Cochin University, Kerala 1990 First Class
M. Tech Electronics Cochin University, Kerala 1988 First Class
B. Tech Electrical Engineering Kerala University 1986 First Class with Distinction
RESEARCH INTERESTS
Both Engineering and Management
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
M.R. Reshma, B. Kannan, V.P. Jagathy Raj, and S. Shailesh
Elsevier BV
O.A. Ajilisa, V.P. Jagathy Raj, and M.K. Sabu
IOS Press
Thyroid nodule segmentation is an indispensable part of the computer-aided diagnosis of thyroid nodules from ultrasound images. However, it remains challenging to segment the nodules from ultrasound images due to low contrast, high noise, diverse appearance, and complex thyroid nodules structure. So, it requires high clinical experience and expertise for proper detection of nodules. To alleviate the doctor’s tremendous effort in the diagnosis stage, we utilized several convolutional neural network architectures based on Encoder-Decoder architecture, U-Net architecture, Res-UNet architecture. To handle the complexity of the residual blocks, we also proposed three hybrid Res-UNet architectures by reducing the number of residual connections. The experimental analysis of the segmentation models proves the viability of residual learning in the U-Net architecture. Hybrid models which use minimum residual connections provide efficient segmentation frameworks similar to Res-UNet architecture with a minimum computational requirement. The experimental results indicate that all the segmentation models based on residual learning and U-Net can accurately delineate nodules without human intervention. This model helps to reduce dependencies on operators and acts as a decision tool for the radiologist.
Veena Jose, V. P. Jagathy Raj, and Shine K. George
Springer Singapore
Sariga Raj, B. Kannan, and V. P. Jagathy Raj
Springer Singapore
Shine K. George, V. P. Jagathy Raj, and Santhosh Kumar Gopalan
Springer Singapore
Karunakaran Nair Ajith Kumar and Vettuvila Purushothaman Jagathy Raj
Springer Science and Business Media LLC
V. S. Hari, V. P. Jagathy Raj, and R. Gopikakumari
Springer Science and Business Media LLC
R. Ajith Kumar, M. K. Muhammed Aslam, V. P Jagathy Raj, T. Radhakrishnan, K. Satheesh Kumar, and T K. Manojkumar
IEEE
A statistical analysis was performed on three thousand and eight hundred soil sample data from Thrissur district. Soil pH, Electrical conductivity, Organic Carbon, Phosphorus, Potassium, Calcium, Magnesium, Sulfur, Zinc, Boron, Iron, Copper and Manganese data were analyzed. Correlation analysis, ANOVA and Principal Component analysis were performed on the data set. Analysis indicate that different soil components are significantly correlated with soil properties.
Deny Williams, G. Krishnalal, and V. P. Jagathy Raj
Springer International Publishing
Prasanth S. Poduval, V. R. Pramod, and Jagathy Raj V. P.
Emerald
Purpose – The purpose of this paper is to highlight the application of Interpretive Structural Modeling (ISM) to analyze the barriers in implementation of Total Productive Maintenance (TPM). TPM is explained in brief with emphasis on maintenance programs to improve quality of products, reliability of processes and reduction in cost. Barriers in implementation of TPM are also discussed. Concept of ISM and steps in developing ISM are described in detail. The authors then illustrate the research methodology which involves applying ISM to analyze barriers in TPM. Design/methodology/approach – The paper starts off by describing the concepts of TPM and ISM. Barriers in implementation of TPM are discussed. It explains ISM as a methodology to understand the underlying interrelationship among the inhibiting factors. The authors draw up an action plan to carry out research on the usage of ISM to study the TPM inhibitors, to develop an integrated model to establish the relationship among the different TPM inhibiting factors and to suggest action plan to mitigate these factors. Findings – Interpretive Structural Modeling (ISM) can be used to analyze the driving and dependence power of the variables inhibiting implementation of TPM. The barriers to implement TPM are described with detailed explanation. The complexity of the problem and the degree of interconnection among the variables can be found out. This will help Managers take action on mitigating the barriers. Practical implications – By analyzing the interrelationships among the barriers and their strengths, management can chalk out the strategy to implement TPM in an organization. Management will become aware of the barriers which have the maximum influence and then can act accordingly to mitigate these barriers. This will help in implementing TPM faster and in an organized manner. Originality/value – Many authors have used ISM to study various issues. A couple of authors have used ISM to determine barriers in implementation of TPM. The authors feel that most of the papers describe ISM in brief making it slightly difficult for readers to understand. This paper aims to explain elaborately step-by-step on how to develop an ISM making it easier for researchers to understand the ISM concept. Even though there are papers on TPM and difficulties in implementation of TPM, this paper explains the barriers in implementing TPM based on the experience of the corresponding author having worked in the refinery industry.
Antony Thomas, G. Krishnalal, and V.P. Jagathy Raj
Elsevier BV
V.S. Hari, V.P. Jagathy Raj, and R. Gopikakumari
Elsevier BV
Balakrishnan Menon and Jagathy Raj V. P.
Associated Management Consultants, PVT., Ltd.
With globalization and liberalization, many world leaders in automobile manufacturing such as Ford, General Motors, Honda, Toyota, Suzuki, Hyundai, Renault, Mitsubishi, Benz, BMW, Volkswagen and Nissan set up their manufacturing units in India in joint venture with their Indian counterpart companies, by making use of the Foreign Direct Investment policy of the Government of India. With a multiplicity of choices available to the Indian car buyers, it drastically changed the car purchase scenario in India, and particularly in the State of Kerala. This transformed the automobile scene from a sellers' market to a buyers' market. The main purpose of this paper was to develop a model with major variables that influenced the consumer purchase behaviour of passenger car owners in the State of Kerala. The results of the research contribute to the practical knowledge base of the automobile industry, specifically to the passenger car segment. It would also lend a great contributory value addition to the manufacturers and dealers for customizing their marketing plans in the State.
Shine K George, V P Jagathy Raj, and G Santhosh Kumar
IEEE
Anticipating the increase in video information in future, archiving of news is an important activity in the visual media industry. When the volume of archives increases, it will be difficult for journalists to find the appropriate content using current search tools. This paper provides the details of the study we conducted about the news extraction systems used in different news channels in Kerala. Semantic web technologies can be used effectively since news archiving share many of the characteristics and problems of WWW. Since visual news archives of different media resources follow different metadata standards, interoperability between the resources is also an issue. World Wide Web Consortium has proposed a draft for an ontology framework for media resource which addresses the intercompatiblity issues. In this paper, the w3c proposed framework and its drawbacks is also discussed.
K. Pramelakumari, S. R. Anand, V. P. Jagathy Raj, and E. A. Jasmin
IEEE
Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year.
R. P. Praveen, M. H. Ravichandran, V. T. Sadasivan Achari, V. P. Jagathy Raj, G. Madhu, and G. R. Bindu
Institute of Electrical and Electronics Engineers (IEEE)
This paper presents the design and analysis of a novel machine family-the enclosed-rotor Halbach-array permanent-magnet brushless dc motors for spacecraft applications. The initial design, selection of major parameters, and air-gap magnetic flux density are estimated using the analytical model of the machine. The proportion of the Halbach array in the machine is optimized using finite element analysis to obtain a near-trapezoidal flux pattern. The machine is found to provide uniform air-gap flux density along the radius, thus avoiding circulating currents in stator conductors and thereby reducing torque ripple. Furthermore, the design is validated with experimental results on a fabricated machine and is found to suit the design requirements of critical spacecraft applications.
R.P. Praveen, M.H. Ravichandran, V.T. Sadasivan Achari, V.P. Jagathy Raj, G. Madhu, G.R. Bindu, and F. Dubas
IEEE
This paper presents the optimal design of a surface mounted permanent-magnet (PM) Brushless direct-current (BLDC) motor meant for spacecraft applications. The spacecraft applications requires the choice of a motor with high torque density, minimum cogging torque, better positional stability and high torque to inertia ratio. Performance of two types of machine configurations viz Slotted PMBLDC and Slotless PMBLDC with Halbach array are compared with the help of analytical and finite element (FE) methods. It is found that unlike a Slotted PMBLDC motor, the Slotless type with Halbach array develops zero cogging torque without reduction in the developed torque. Moreover, the machine being coreless provides high torque to inertia ratio and zero magnetic stiction.
E.A. Jasmin, T.P. Imthias Ahamed, and V.P. Jagathy Raj
Elsevier BV
V. S Hari, V. P Jagathy Raj, and R. Gopikakumari
IEEE
Modeling nonlinear systems using Volterra series is a century old method but practical realizations were hampered by inadequate hardware to handle the increased computational complexity stemming from its use. But interest is renewed recently, in designing and implementing filters which can model much of the polynomial nonlinearities inherent in practical systems. The key advantage in resorting to Volterra power series for this purpose is that nonlinear filters so designed can be made to work in parallel with the existing LTI systems, yielding improved performance. This paper describes the inclusion of a quadratic predictor (with nonlinearity order 2) with a linear predictor in an analog source coding system. Analog coding schemes generally ignore the source generation mechanisms but focuses on high fidelity reconstruction at the receiver. The widely used method of differential pulse code modulation (DPCM) for speech transmission uses a linear predictor to estimate the next possible value of the input speech signal. But this linear system do not account for the inherent nonlinearities in speech signals arising out of multiple reflections in the vocal tract. So a quadratic predictor is designed and implemented in parallel with the linear predictor to yield improved mean square error performance. The augmented speech coder is tested on speech signals transmitted over an additive white gaussian noise (AWGN) channel.