College of Engineering, Pune

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

Support vector regression method for predicting temperatures of heat sources cooled by forced convection in a horizontal channel

Abstract: This paper presents the novel concept of Support Vector Regression method for predicting the temperatures of rectangular heat sources with dummy components cooled by forced convection in a horizontal channel. The substrate board considered for study is FR4 equipped with silicon heat sources resembles IC chips on printed circuit board. Six heat sources with three dummy components of four different sizes are considered for study. Three dimensional steady state numerical simulations have been performed using finite element method based commercially available software COMSOL Multiphysics 5.4. Laminar non-isothermal fluid flow with conjugate heat transfer module is used to study heat transfer in solids and fluid region. Air cooling of heat sources with dummy components is carried out to study heat transfer characteristics. Flowing air velocity of 2.5 m/s with uniform heat flux value of 5000 W/m2 is considered for study. Support Vector Regression programming is performed using Python programming language to predict temperatures of heat sources to explore optimal configuration. Random hundred different configurations were studied out of which 80% were used in training and 20% used for validation. Results show that the simulated and predicted temperature values are in agreements of ⩽ 10 %.

Pattern Based Network Security Using Semi-Supervised Learning

Abstract: Network security is becoming increasingly important in today’s internet-worked systems. With the development of internet, its use on public networks, the number and the severity of security threats has increased significantly. Intrusion Detection System can provide a layer of security to these systems. Intrusion Detection can be defined as "the act of detecting actions that attempt to compromise the confidentiality, integrity or availability of a resource”. More specifically, the goal of intrusion detection system is to identify entities who attempt to subvert in-place security controls. At present, two fundamental problems, quantity and quality of the outputs i.e. false alarms or alerts of IDS, have not been solved well. The pattern of attack changes frequently. Thus IDS should upgrade accordingly. The changes in patterns are mainly the manifestations of attack. Pattern based IDS provides very low false alarms as compare to heuristic/anomaly based IDS. In real world it is very difficult to have large labeled data for training. Supervised approach can't be used in this case. So in this work we propose a semi-supervised approach for pattern based IDS. Our approach uses supervised algorithm as a black box and then filters the unlabelled data with predicted label for training the system. The experimentation is performed on KDD CUP99 dataset and NSL KDD data which is revised KDD CUP 99 data.

Recycling of Waste Poly(Ethylene Terephthalate) with Naphthalene and Neutral Water

Abstract: The waste poly(ethylene terephthalate) (PET) powder dissolution/reprecipitation was carried out in a batch operation at atmospheric pressure at various temperatures ranging from 180–220°C at temperature intervals of 10°C. Particle sizes of the waste PET ranged from 50–512.5 µm and operation time, which ranged from 30–90 min, were optimized. Dissolution/reprecipitation of the waste PET was carried out in naphthalene (solvent) and neutral water (nonsolvent), respectively. Dissolution/reprecipitation of the waste PET was increased with operation time and temperature. Dissolution/reprecipitation of PET was decreased with increase in the particle size of the waste PET. The waste PET particle size and agitator speed required for complete recycling of the waste PET were also optimized. Analyses of the waste PET and the recycled PET collected after the reprecipitation process was undertaken by determination of various physical properties. The operation applied at lesser time and with cheaper solvent/nons...

Human face detection and recognition in videos

Abstract: Advancement in computer technology has made possible to evoke new video processing applications in field of biometric face detection and recognition. Applications includes are face detection and recognition integrated to surveillance systems, gesture analysis etc. The first step in practical face analysis systems is real-time detection of face in sequential frames containing face and complex objects in background. In this paper a system is proposed for human face detection and recognition in videos. Efforts are made to minimize processing time for detection and recognition processes. To reduce human intervention and increase overall system efficiency the system is segregated into three stages-motion detection, face detection and recognition. Motion detection reduces the search area and processing complexity of systems.