College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Computer science & Sliding mode control. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

Data Analytics: COVID-19 Prediction Using Multimodal Data

Abstract: Globally, there is massive uptake and explosion of data, and the challenge is to address issues like scale, pace, velocity, variety, volume, and complexity of this big data. Considering the recent epidemic in China, modeling of COVID-19 epidemic for cumulative number of infected cases using data available in early phase was big challenge. Being COVID-19 pandemic during very short time span, it is very important to analyze the trend of these spread and infected cases. This chapter presents medical perspective of COVID-19 toward epidemiological triad and the study of state of the art. The main aim of this chapter is to present different predictive analytics techniques available for trend analysis, different models and algorithms, and their comparison. Finally, this chapter concludes with the prediction of COVID-19 using Prophet algorithm indicating more faster spread in short term. These predictions will be useful to government and healthcare communities to initiate appropriate measures to control this outbreak in time.

An optimized key frame extraction for detection of near duplicates in content based video retrieval

Abstract: Due to the availability of increased Internet bandwidth and transfer media, the multimedia objects usage is widely used. In multimedia the text files, scripts, images and videos, audios are used by almost every end of users. Daily around millions of videos are being uploaded through various sources. It is becoming very tedious to manage with these multimedia objects specially related to videos. It needs some kind of retrieval technique which will relieve users from tracking videos relatively similar manually. So users are shifted from text based video retrieval to content based video retrieval (CBVR). CBVR has steps as key frame extraction, feature vector formation, similarity and template matching and finally get the retrieved relatively correct expected copies of videos approximately matching with query video. Key frame extraction play very vital role in whole process of CBVR as it decides the search region for search engines affecting the performance. In videos large number of frames forms a scene. This scene includes repetition of nearly same frames with slight differences, which increases the storage space and decreases the performance in video processing. Here instead of searching the whole set of frames in videos, only selected key frame are used for further processing. Automated extraction of key frames has recently gained momentum in the field of video content summarization. Key frames are extracted by computing the consecutive frame differences. Then their relative neighboring frames which nearly match with the key frames are taken aside as near duplicate copies. Therefore the near duplicate copies detection may help to improve the performance and speed of near duplicate detection in content based video retrieval.

Optimizing the size of a CFRP patch to repair a crack in a thin sheet

Abstract: One of the several techniques to repair cracks in structural sheets consist in bonding polymer composite patches. The effectiveness of the repair for restoring the quasistatic strength of the structure depends largely on the adhesively bonded interface. The interface fails due to interfacial separation caused by the high peeling and shearing stresses. The geometrical dimensions, that is, patch length and width, have significant effect on the interface separation and they need to be optimized. The failure strength of the patch was determined by a numerical analysis using the cohesive zone model. Twenty-five numerical analyses were carried out as per the L-25 Taguchi orthogonal array followed by ANOVA which indicated the greater contribution of the patch width toward the failure of the patch. The failure stresses thus obtained were used to generate a response surface in ANSYS Design Explorer Module. A design criterion in terms of the percentage increase of the failure stress over the yield stress of...

Multi-objective optimal sizing of distributed generation by application of Taguchi desirability function analysis

Abstract: Distributed Generation (DG) is modular power generating technology. DG has beneficial impact on tail end voltages, line losses and operating cost. The sizing and location of DG in distribution system is a vital undertaking. In this paper Taguchi Desirability Function Analysis (TDFA) is presented. The remarkable highlight of TDFA is the skillful handling of simultaneous optimization of nearly thousand objectives and its ability to generate multiple optimal solutions. It facilitates the effortless addition of each objective. TDFA is used for finding the optimal size of single as well as multiple DG. Size estimation is carried out for multiple objectives of minimizing the line losses, improving the voltage profile and improving voltage stability index (VSI). The objectives may be minimized, maximized or assigned target values simultaneously. In the scope of this paper more than nine objectives have been optimized simultaneously. TDFA has been implemented to determine optimal size of DG for different load conditions. The proposed approach is tested and verified on IEEE 33-bus and IEEE 85- bus radial distribution system (RDS).