MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

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School of Electronics & Communication Engineering

2019 IEEE Pune Section International Conference, PuneCon 2019

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Liquid desiccant indirect evaporative cooling system consists of dehumidifier, regenerator, evaporative cooling unit and liquid desiccant. This system become alternative for convectional mechanical vapor compression system, because it reduces the consumption of electrical energy and avoids emission of harmful gases i.e. it is cost effective as well as environment friendly. Liquid desiccant absorb moisture from air in dehumidifier unit. Often solar energy is used to produce thermal energy and this thermal energy is used for regeneration of liquid desiccant in a regenerator. Further indirect evaporator is used for sensible cooling of a dehumidified air. In this paper, a review of dehumidifiers, regenerators, indirect evaporative coolers and also integrated liquid desiccant system with indirect evaporative coolers are studied. Also system configurations, modes of operations are presented. This review designates that liquid desiccant indirect evaporative cooling technology can provide human comfort in hot and humid climatic conditions. © 2019, International Journal of Scientific and Technology Research. All rights reserved.

School of Mechanical Engineering

International Journal of Scientific and Technology Research

Overview of liquid desiccant based indirect evaporative cooling system

Microforming is an emerging technology to manufacture products in the light of miniaturization in several domains of industry. Plastic anisotropy is one of the material characteristics significantly affecting the micro forming process. The crystal orientation influences tensile strength, yield strength and ductility, depending on different grain sizes and principle sliding planes. The present work elaborates on the influence of the plastic anisotropic in microforming for a plane stress state condition. Yield function and constitutive equations for the anisotropic material with consideration of the crystal lattice constants and parameters of crystallography texture are proposed. The crystal orientation is considered in a subroutine VUMAT algorithm, as an internal variable based on the developed mathematical model which is implemented in Abaqus as an user material subroutine. Micro limiting dome height experimental tests for different strain paths are conducted with brass foils. The results are compared with that predicted with numerical analysis, considering critical damage and element deletions. The numerical and experimental results show a good agreement for the Alpha brass ultra-thin foils, using a set of failure criterion.

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Manufacturing Review

Effect of crystallographic texture on the forming limit in microforming of brass

With the advent of technology, Flexible AC Transmission Systems (FACTS) are popularly used to increase the power transmission capacity of transmission lines and for optimum utilization of power system capability which result into the substantial change in system dynamics. Change in system dynamics has an impact on protection of power system as there is drastic change in the net impedance of transmission line due to FACTS devices which gives rise to over-reaching or under-reaching problem in distance protection scheme. In this paper, performance of distance relay in the presence of Static Synchronous Series Compensator (SSSC) is studied under various fault conditions. A 400kV, 300km transmission line is designed and simulated using MATLAB/SIMULINK software and results are presented to show influence of SSSC on mho characteristic of distance relay. © 2019 IEEE.

School of Electrical Engineering

2019 IEEE 2nd International Conference on Power and Energy Applications, ICPEA 2019

Maloperation of Distance Relay under Faulty Conditions in presence of Static Synchronous Series Compensator

Articular cartilage (AC) of knee plays a vital role in the supporting mechanism of the human body. AC is a smooth, slippery surface which composed of white tissue covering the joint shell. Magnetic Resonance (MR) imaging technique is widely adopted in clinical practice to visualize and detect the defect in Knee anatomical structure. Segmentation of AC using knee MR images is an essential process to biomarker analysis during knee osteoarthritis progression. Hence it has been widely investigated by research communities. Osteoarthritis (OA) is a commonly found knee disease, in which AC is slowly and progressively lost. Early diagnosis of OA plays a significant role to look up treatment possibilities and prevent further degradation of knee AC. Segmentation methods of AC have considered various parameters of image, image quality, and tissue structure, etc. Manual segmentation of cartilage for OA diagnosis, from a large amount of MRI images produced in clinical practice, is a time-consuming and challenging process. Thus semi-automated and fully automated cartilage segmentation methods have been adopted by many researchers to provide reasonable accuracy and reliable detection of AC from knee MR images. This paper reviews knee cartilage segmentation methods from MR images. In this paper, we focus on the recent tread of knee AC segmentation techniques from MR images. First, an introduction to osteoarthritis and its causes are given. Then variety of MR imaging modalities suitable for assessment of knee OA. Finally, performance measures are explained followed by conclusion.

School of Computer Engineering & Technology

International Journal of Advanced Science and Technology

A Comprehensive Survey of Articular Cartilage Segmentation Methods on Knee MRI

The abundant availability of food is extremely essential for human existence on this planet, thus making agriculture a mandatory practice throughout the world. Within two decades, a severe complication of food will arise. Hence, the advancement and progress of agriculture is essential. With the help of trending technologies like IoT, the environmental factors that affect the crops are easily monitored, and decisions are taken based on them so that crop monitoring is done remotely, which is a magnificent IoT application. Today, agriculture in the world is partially automated, and farmers mostly rely on traditional methods because of which they suffer from scarcity of rainfall and dearth of water. A plethora of techniques exist that provide automation of agricultural activities and hence increase their efficiency so that better crop output is obtained. The automation of agriculture enables reduction in human involvement. The main purpose of this review paper is to present an overview of various techniques adopted in automated agriculture systems which save the farmers' time, money and power. This paper also extracts key features of existing automated agriculture systems based on their advantages and disadvantages. This paper will assist in the development of a system which will overcome the drawbacks of existing automated agriculture systems. © 2019 IEEE.

Automated Systems for Smart Agriculture

Several finite element models of bone-implant prosthetic derived with geometrical topology and material properties. Most of them adopted linear isotropic material properties to predict stresses around bone-implant interface. The objective of the present study is to compare stress distribution around bone-implant interface between two material models. In order to understand the biomechanical stress behavior at bone-implant interface, four different implant models were selected for the study. Mandibular bone section material models for isotropic and orthotropic material is defined with the contact between bone and implant surface to predict the von Mises stresses in the cancellous and cortical bone under the influence of vertical load of 100 N (coronal-apical), lateral load of 40 N (mesial-distal), and oblique load of 100 N at 45° to the axis of implant on crown surface. A nonlinear Abaqus CAE code is used to predict stresses distribution comparison between two material models.The current study compares the result of stress in cancellous and cortical bone with isotropic and orthotropic material models. The stress distribution along the interface was presented for vertical, lateral, and oblique loading of selected implant models. Finite element (FE) numerical simulation result shows that the orthotropic material model is more acceptable than the isotropic material model to predict stress along bone-implant interface.

BioNanoScience

Significance of Orthotropic Material Models to Predict Stress Around Bone-Implant Interface Using Numerical Simulation

In an era where customer relationship management plays an important role in business success, firms endeavour to develop profitable associations with their existing and future consumer base. With customer-driven industries becoming highly competitive, there is a huge demand for efficient business intelligence (BI) approaches to facilitate high performance results while optimising on the costs invested, mainly in marketing activities. Therefore, researchers around the world are exploring BI techniques to maximise customer satisfaction and customer loyalty. Recently, machine learning models are being adopted to guide businesses for investing on new marketing strategies through intelligent mining of their existing customer data. This paper adopts a neural network approach to analyse and develop a model of business rules establishing relationships that can exist between customer demographics and the final purchase made by the customers. Then this model can be employed for the prediction of the purchasing patterns that might occur in the future. The dataset obtained from Kaggle's 'Allstate Purchase Prediction challenge' consisting of a car insurance customer base is employed for developing our prediction model. The experimental results confirm that the neural network approach adopted is able to extract the purchasing rules exhibited by the customers of a firm by discovering the customer behaviour patterns and their relationships with different demographic variables. An efficient prediction of the customer purchasing patterns would help organisations to make appropriate business proposals to their prospective customers in order to achieve higher customer satisfaction. © 2019 IEEE.

Prediction Model Development using Neural Network Approach

Object tracking and monitoring is used for detection purpose. This research is found since last two decades. However, the scope is there for further improvement. Cur-rent deep neural network model is found suitable in this area exclusively. We examine the problem of vehicle discov-ery by the deep learning method. In addition, we have shown that the use of deep features extracted from a pre-formed network produces a more efficient and ac-curate means of monitoring. Fast Recurrent CNN, the use of these systems becomes bottleneck process with relevance to CNN's operation. Fast R-CNN solves this downside by applying the projected mechanism to use the region and then CNN. An RPN is a fully convex network that predicts object boundaries and object scores at each location simultaneous-ly. © 2019 IEEE.

2019 International Conference on Applied Machine Learning (ICAML)

Application of Recurrent Convolution Neural Network for Vehicle Detection

Heavy vehicles develop technical snag and traffic jam on streets. Accidents between heavy vehicle and road users, for example, pedestrians often result in severe injuries of the weaker street users. The highway safety and traffic jams can be secured with detection of heavy and overloaded vehicles on the highway to facilitate light motor vehicles like cars, scooters. A model for heavy vehicle detection using fine-tuned based on deep learning is proposed to deal with entangled transportation scene. This model comprises two parts, vehicle detection model and vehicle fine-grained detection. This step provides data for the next classification model. Experiments show that vehicle’s make and model can be recognized from transportation images effectively by using our method. Experimental results demonstrate that the proposed detection system performs accurately with other simple and complex scenarios in detecting heavy vehicles in comparison with past vehicle detection systems. © Springer Nature Switzerland AG 2019.