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

The three HPHX (heat pipe heat exchanger) configurations in the present investigation include HPHX with single wick structure (HPHX 1), HPHX with composite wick structure (HPHX 2) and hybrid HPHX (HPHX 3) which is the combination of HPHX 1 and HPHX 2. The experimental correlations obtained are used for investigating annual energy savings in air conditioning using HPHX integrated with and without evaporative cooling, for Indian climatic zones. A representative city for each Indian climatic zone is selected for the analysis involving sensible heat recovery between outdoor air and conditioned return air. The investigation reveals that for HPHX without evaporative cooling, HPHX 1 becomes favourable choice for cities such as Bengaluru (representing temperate Indian climatic zone), Chennai (warm and humid Indian climatic zone) and Guwahati (cold Indian climatic zone). For cities such as Ahmedabad (hot and dry Indian climatic zone), Gwalior (composite Indian climatic zone) and Chennai, the use of HPHX integrated with evaporative cooling is found more promising for HPHX 3 followed by HPHX 2, whereas for cities such as Bengaluru and Guwahati, HPHX 1 becomes the popular choice. The present research gives an insight towards the performance and selection of HPHX with different configurations, for Indian climatic zone.

World University root node

School of Mechanical Engineering

Energy, Pergamon

Analysis of annual energy savings in air conditioning using different heat pipe heat exchanger configurations integrated with and without evaporative cooling

The assembly that serves an engineering function is called as structure and structure that exhibits intelligence, efficiency, adaptability, accurate response and optimum performance is termed as a smart structure. Though, due to these advantages smart structures are used in many high end applications like aerospace engineering, microelectronics productions, precise positioning and automation industries, active vibration control of these structures is extremely important. This is because; mechanical vibrations in the structure can have detrimental effects as they can damage sensitive components of the structure or even the whole structure. Vibration control basically suppresses the effect of external disturbances and keeps the structure in its equilibrium position. In this paper, vibration control analysis of the structure for forced vibrations is carried out. The smart structure considered here, is a finite and flat beam-like fiber cantilever structure with PZT patches attached to its surface. Along with vibration control analysis effect of changing positions of PZT patches on this analysis is also discussed in this paper. © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

School of Electrical Engineering

Fulltext

Procedia Computer Science

Active Vibration Control of Smart Structure Using PZT Patches

Non verbal approaches of emotion detection plays vital role in various applications like E-learning, automatic pain monitoring, driver alert system, cognitive assessment etc. which are developed to enhance quality of human life. Facial expressions based approach is one of the very effective approaches which is used widely on standalone basis or combined with other approaches known as multimodal techniques of emotion detection. The paper discusses facial expressions based emotion detection which uses patch based face features and SVM Classifier. The experimentation carried out on JAFFE database provides 90.65 % average accuracy for emotion detection for basic emotions happy, anger, sad, surprise, disgust, fear and neutral. The other performance parameters through experimentation are obtained as Average True positive rate is 90.5942 %, average false positive rate is 9.3671 % and average false negative rate is 9.4 %. The average feature extraction time is 18.49 s and emotion detection time through these extracted features is 1.1 s.

School of Computer Engineering & Technology

Studies in Computational Intelligence

Non verbal approach for emotion detection

School of Electronics & Communication Engineering

INTERNATIONAL JOURNAL OF ADVANCED COMPUTING AND ELECTRONICS TECHNOLOGY {\ldots}

FARM MONITORING AND CROP DISEASE DIAGNOSIS

School of Petroleum Engineering

National Conference on Emerging Trends in Engineering, Technology and {\ldots}

Flow Assurance in Petrochemical Industry

Process piping play an extremely important rolein Petroleum refineries throughout the world as a means forcarrying Hydrocarbons from one location to other. Theseprocess piping are subjected to Corrosion over a period ofoperational time, which is the destructive attack of materialby reaction with its service fluids containing corrodents suchas Sulphur, H2S, HCl, CO2, organic and inorganic acids andexternal environments thus causing a natural potentialhazard associated with oil and gas transportation facilities.Various potential damage mechanisms are identified for preheated crude inlet piping to atmospheric distillation columnusing American Petroleum Institute standardssuch as API 581and API 571. These damage mechanisms consist mainly ofHigh Temperature Sulfidic and Naphthenic acid corrosion.Corrosion rates for this damage mechanism is identified fortwo crudes namely Kuwait crude and Arab Extra Light Crudeusing API 581. Retirement thickness for this piping iscalculated using ASME B 31.3. Further remaining life of thispiping is estimated using corrosion rate, present thickness andretirement thickness of the piping.

International Research Journal of Engineering and Technology (IRJET)

Determination of corrosion rates and remaining life of piping using API and ASME standards in oil and gas industries.

This paper proposes conjugate gradient back-propagation based artificial neural network for real time power quality assessment. The novel real time voltage sag and swell detection, classification scheme using artificial neural network is presented. The ANN is trained in MATLAB using voltage sampled signal data and corresponding parameters of the neural network are utilized to implement in LabVIEW in real time sag swell detection and classification. The Levenberg Marquardt, the resilient back-propagation and conjugate back-propagation algorithm performance is evaluated in MATLAB to find the best neural network for real time application. Among these three back-propagation algorithms, conjugate gradient algorithm has better performance for real time power quality monitoring. The mathematical model of Conjugate gradient back-propagation neural network is implemented in LabVIEW. Real time voltage signals of sag and swell for different time duration and magnitude, intensity are acquired from hardware experimental setup. Hardware setup mainly consists of single phase 230 V voltage source, microcontroller, dimmerstat and solid state relays. Voltage signals of sag and swell are sensed using high precision voltage sensor. Data acquisition system is used to acquire the signal from voltage sensor. The output of data acquisition system is given to the personal computer with LabVIEW. The proposed monitoring system also detects odd and even harmonic components in the voltage signal acquired using FFT. Real time hardware results obtained using proposed power quality monitoring system for detection of voltage sag, swell and harmonics claims the suitability, robustness and adaptability to monitor power quality issues.

International Journal of Electrical Power & Energy Systems

Conjugate gradient back-propagation based artificial neural network for real time power quality assessment

Classification is important task of data mining used to extract knowledge from huge volume of data. By nature, classification is multi-objective problem, as it required optimization of multiple objectives simultaneously like accuracy, sensitivity, squared error, precision etc. Traditionally, evolutionary algorithms were used to solve multi-objective classification problem by considering it as single-objective problem, but this approach gives single solution to problem. Therefore, multi-objective evolutionary algorithms are used to solve classification problem. In this paper, we have used Pareto approach to optimize neural network to solve classification problem. Non-dominated sorting genetic algorithm is used to simultaneously optimize accuracy and mean squared error objectives of neural network along with local search. As slow convergence to optimal solutions is major disadvantage of evolutionary algorithm. To speed up convergence to optimal solutions hybrid technique is adopted by augmenting evolutionary technique with local search algorithm. This proposed approach gives set of Pareto optimal solutions which represent different solutions for given classification problem. © 2016 IEEE.

International Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT 2016

Classification problem solving using multi-objective optimization approach and local search

Today central topic in science and engineering is parallel and distributed computing, research performing in the area of development of new approaches for the modeling, design, analysis, evaluation, and programming of future parallel and distributed computing systems and its applications. To detect the presence of dependency between tasks, analytic methodology is required. This review theoretically analyzes what is dependency, different types of dependencies, various applications which require dependency analysis and different approaches towards dependency analysis. This research classifies dependency analysis approaches on the basis of input source; for different input source different methodology is available, this review considers an input source as a component base system with dependent components. This research gives review of dependency analysis solutions for various areas like parallel processing, high performance computing, security and vulnerability in software's where various approaches are used for dependency analysis. This review concludes that there are various methods available for dependency analysis. Most of the methods use graph base approach, conceptual graph approach and matrix based approach, out of which this review puts forward the best possible approach. This research proposes a new approach for dependency analysis which results in the reduced dependent analysis time, low memory consumption and less cost sensitive. © 2016 The Authors.

Physics Procedia

Dependency Analysis for Secured Code Level Parallelization

Physician use medical images to find abnormalities in human bodies and to locate the discontinuities. In transmission process sometimes medical images are corrupted due to external noise. Therefore need to improve the image quality, put down the computational complexity and signal to noise ratio. There is need of segmentation to improve performance analysis and image quality. Segmentation method is use to identify important regions in medical images, it is a unique technique for partitioning image into meaningful sub regions or object with same attribute. Proposed method state Principle component analysis and k-means clustering method for segmentation of medical images and extracts meaningful part from medical image in efficient manner. K-means Clustering is the process of extracting meaningful part from image. Adding Principle Component Analysis for feature extraction and formation of precise number of cluster to increase the accuracy. To develop a system which will perform segmentation of MRI images to locate disorder in better way. © 2015 IEEE.