Institution
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.
Topics: Sliding mode control, Control theory, Feature extraction, Cloud computing, Wireless sensor network
Papers published on a yearly basis
Papers
More filters
••
TL;DR: In this paper, the authors exploited the solid supported micellar bilayer (called admicelle) to extract Cu(II) ion from aqueous medium via adsorption, or more appropriately called adsolubilization.
Abstract: Micelles have unique solubilizing properties. Here has been exploited the solid supported micellar bilayer (called admicelle) to extract Cu(II) ion from aqueous medium via adsorption, or more appropriately called adsolubilization. The adsorbent used in the present study is alumina modified with anionic surfactant. The material has been designated as surfactant-modified alumina (SMA). Batch adsorption results show efficient Cu(II) extraction (94.45%) by SMA at equilibrium conditions. The batch adsorption of Cu(II) obeyed Freundlich isotherm and pseudo second order kinetic model. Thermodynamic parameters (ΔG, ΔH, and ΔS) indicate that the adsorption process is spontaneous, endothermic, and favorable. Desorption of copper from Cu(II)-adsorbed SMA is possible by 0.2 M Na2-EDTA. The applicability of SMA for the removal of Cu(II) from Cu(II)-spiked real wastewater has been examined. The results show that SMA is capable of treating wastewater containing Cu(II) having an initial concentration of 20 mg L−...
7 citations
••
25 Apr 2019TL;DR: This survey paper is the overview of the work done in this area till date and also discusses what can be done to improve and extend the work.
Abstract: Natural language processing (NLP) is typically used for analyzing a large set of data. It has traditional applicability like sentiment analysis, spam mail detection, summarizing a large text. However, word problem solving is challenging if it is to be done with NLP. There are some approaches which have been proposed. Some could solve basic arithmetic problems like addition/subtraction. Knowledge representation is the main task to be done by NLP. Each kind of problem can be solved by a specific approach. This survey paper is the overview of the work done in this area till date and also discuss what can be done to improve and extend the work.
7 citations
••
01 Aug 2018TL;DR: The comparative performance analysis with CNFET shows that there is improvement of 264 percent in gain while maintaining the complete stable performance in both the case and results show the tremendous increase in unity gain bandwidth while significantly saving in power.
Abstract: This paper presents performance analysis for the two-stage CMOS operational transconductance amplifier using conventional CMOS technology and emerging carbon nanotube technology. Both the theoretical calculations and computer aided simulation analysis have been given in detail. Designs have been carried out on backend tool of Mentor graphics using PTM 32nm CMOS process. Schematic simulations have been carried out using ‘Pyxis Schematic’ and simulations have been done using simulator ‘ELDO’, version 11.2. For CNFET implementation 32nm CNFET model from Stanford University is used and simulations are carried out using Hspice. The a. c. analysis demonstrates that gain of the CMOS amplifier is very low i.e. 15.6 dB, Phase Margin is 80.2°, & Unity Gain Bandwidth is 7 MHz. The output swings up to 1.15V and the op-amp dissipates power of $336.5\mu \text{W}$ under supply voltage of 1.2V. The comparative performance analysis with CNFET shows that there is improvement of 264 percent in gain while maintaining the complete stable performance in both the case. Further results show the tremendous increase in unity gain bandwidth while significantly saving in power.
7 citations
••
TL;DR: The effect of nonlinear behavior of spring stiffness measured experimentally with the linear assumption of stiffness using validated SIMULINK model is emphasis.
7 citations
••
01 Jan 2015TL;DR: A proposed framework is build with four main challenges such as future assumptions, data stream summarization, change in data stream trend clusters, and learner adaptivity and model, and an adaptive classify algorithm for the predictive ability evaluation on the test set.
Abstract: Recent developments in electricity market deregulation, the prices are not fixed. In such application, class labels are not available directly and potentially valuable information is lost. A learning model of electricity demand and prices needs to be adaptive for dynamic changes in massive data streams. This paper presents adaptive building of learning model for electricity demand supply and prices by detecting and adapting changes in trends and values. A proposed framework is build with four main challenges such as future assumptions, data stream summarization, change in data stream trend clusters, and learner adaptivity and model. A proposed online algorithm for not only considering data values by avoiding trends of the streams. A correlation-based similarity method is used to produce concept clusters to handle unlabeled data and trend analysis, change detection type in terms of variation between past concept clusters and current ones, and predict future assumptions. An adaptive classify algorithm for the predictive ability evaluation on the test set. Results of experiments using electricity data confirm applicability of methodology with more than 80–85 % unlabeled data.
7 citations
Authors
Showing all 4264 results
Name | H-index | Papers | Citations |
---|---|---|---|
Devavrat Shah | 66 | 374 | 18772 |
Kenji Higashi | 57 | 510 | 14336 |
Bijnan Bandyopadhyay | 38 | 360 | 5611 |
Kalpana Joshi | 27 | 100 | 2452 |
Nikhil Naik | 25 | 55 | 3562 |
J.K. Chakravartty | 23 | 153 | 1711 |
M. D. Uplane | 21 | 75 | 1567 |
Shrivijay B. Phadke | 21 | 68 | 1989 |
Kiyohito Okamura | 21 | 89 | 1157 |
Sudeep D. Thepade | 21 | 241 | 2173 |
Rajendra Kumar Goyal | 20 | 71 | 1236 |
Avinash M. Dongare | 20 | 83 | 1149 |
Parikshit N. Mahalle | 17 | 118 | 1534 |
Parag Kulkarni | 17 | 116 | 1633 |
Elumalai Natarajan | 17 | 56 | 1470 |