99 research outputs found
CONCEPT AND CONTEXT OF NEW EMPLOYMENT OPPORTUNITIES FOR WOMEN IN AGRICULTURE
No full textGlobalization, liberalization, technological development, infrastructure development and WTO etc. are ‘contexts’, which have the potential to positively influence the quantity and quality of women’s participation in agriculture in India’s North Eastern Region. Women in agriculture in this part of the country, are going to get more and more employments both in the value chain and in newer ‘concepts’ (work avenues) being created. However, in practice, many of these current and would be ‘contexts’ and ‘concepts’ are not and would not be unmixed blessings. In the short-run, some of the changes may bring in miseries to the women folks. However, in the long run, women in this region are going to gain the lion’s share of these ensuing opportunities, because they are ideologically more empowered vis-a vis women in rest of India. Having said this, the fact is also that, this ideological empowerment has not greatly been translated into economic and political empowerment in terms of access to land, assets, credit, information, knowledge etc. Hence, the need of the hour is to promote more such empowerment in the institutional levels. This will brighten their access to new work avenues in agriculture in the near future.Agriculture; employment; women; globalization; liberalization; North East India;empowerment; gender
State estimation and non-linear model based control of a continuous stirred tank reactor using unscented Kalman filter
No full textNovel Approaches for Offline Data-Driven Evolutionary Multiobjective Optimization
No full textMost multiobjective evolutionary algorithms (MOEAs) assume that analytical functions or simulation models are available while solving a multiobjective optimization problem (MOP). However, in some cases we must start with data and build approximation models known as surrogates that are later used to solve the MOP by an MOEA. These types of problems are called data-driven MOPs. This
thesis is devoted to solving so-called offline data-driven MOPs that are particularly challenging as no new data is available during the optimization process.
The author first presents approaches to utilize the uncertainty in the prediction of Kriging or Gaussian process (GP) surrogates as additional objectives. However, these approaches increase the complexity of the MOP being solved. Hence, the author proposes probabilistic selection approaches that can be embedded in a decomposition-based MOEA without further analytical derivations.
These approaches utilize Monte Carlo sampling and kernel density estimation to calculate the probability of selection criterion of the MOEA and later select individuals based on them. Next, the author proposes an interactive optimization framework that utilizes decision maker’s preferences for uncertainties in addition to preferences for objective values. The framework was further extended to use probabilistic selection approaches for a decomposition-based MOEA and a custom reference vector adaptation technique to consider uncertainty in the solutions during the adaptation process.
Building GPs with all the provided data becomes computationally expensive when the size of the data is large. Hence, the author finally proposes treed GP surrogates for multiobjective optimization (TGP-MO). They can be built with a relatively low computational cost and have a good accuracy exclusively in the regions around the optimal solutions. This thesis provides multiple novel approaches
and detailed experimental studies for solving offline data-driven MOPs with decision support that will enhance real-world problem-solving capabilities.
Keywords: metamodelling, surrogates, Pareto optimality, Kriging, Gaussian processes, evolutionary algorithm, decision making, uncertainty, interactive methods, preference informationunknown accessibilityei tietoa saavutettavuudest
A brief description of the writings, author and period of composition etc. in the family memoir book/ পারিবারিক স্মৃতিলিপি পুস্তকে লেখা, লেখক ও রচনাকাল প্রভৃতির সংক্ষিপ্ত বিবরণ
No full textThe Proverbial Professor Pasupati Sasmal once researched the family memoirs of Jorasanko Thakurbari. Mainly following his research, a brief list of various writings including serial number, title of part of the first line, author, period of composition, page of manuscript etc. is presented here. This is the first part of the given description. The second part of it is as follows: After writing No.105, the list and description (in the second part) of all the remaining writings which are not given serial numbers are also given. Interested Rabindra-inquisitors can know almost all the information at a glance from this list
A nonlinear model-based control scheme for benchmark industrial processes
No full textOver the last two decades, model-based control schemes have been widely used for designing industrial control systems especially in process control industries. In this paper, a nonlinear model-based control scheme has been proposed for benchmark industrial processes. The scheme also uses adaptive control strategy for better tuning the controller gains. The proposed framework is applied to control the liquid level of a spherical tank, which is a benchmark nonlinear industrial process. Reference tracking and disturbance rejection performance have been tested via simulation results and the performance of the proposed scheme has been compared with the classical adaptive PI controller. Simulation results shows that the proposed scheme ensures closed-loop stability and maintains satisfactory performance in presence of parameter variation.</p
Parameter estimation and its application in tuning PI control scheme
No full textIn this paper, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) based robust adaptive PI Control Scheme have been proposed and implemented on the simulated model of the non-linear benchmark processes. The servo and regulatory performance using proposed tuning methodologies were found satisfactory. The performances of the proposed control schemes have been compared with conventional adaptive PI (CA-PI) control scheme. From the extensive simulation studies, it was found that the proposed schemes implemented on non-linear processes are having better performance over CA-PI control scheme. It was also found that proposed control schemes are able to eliminate measurement noise and having good robustness features.</p
Model-based adaptive control scheme for benchmark pH-neutralisation process
No full textControl of pH has always been a critical task because of the highly complex nonlinear characteristic of the titration curve. This paper is concerned about the design of an adaptive nonlinear model-based tracking control scheme and its application in a benchmark pH-neutralisation process. The scheme exploits a deterministic process model to generate the model state which reflects the actual process dynamics, and utilises three different adaptive tuning methods to update the controller gain on real-time basis. Simulation results show that the proposed scheme ensures closed-loop stability and facilitates satisfactory set-point tracking (both time-varying and constant) performance despite parameter variation, exogenous disturbance and measurement noise. </p
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