| URI: | http://dyuthi.cusat.ac.in/purl/5223 |
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| Dyuthi T-2258.pdf | (2.864Mb) |
| Abstract: | Multivariate lifetime data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated lifetime when an individual is followed for the occurrence of two or more types of events, or when distinct individuals have dependent event times. In most studies there are covariates such as treatments, group indicators, individual characteristics, or environmental conditions, whose relationship to lifetime is of interest. This leads to a consideration of regression models.The well known Cox proportional hazards model and its variations, using the marginal hazard functions employed for the analysis of multivariate survival data in literature are not sufficient to explain the complete dependence structure of pair of lifetimes on the covariate vector. Motivated by this, in Chapter 2, we introduced a bivariate proportional hazards model using vector hazard function of Johnson and Kotz (1975), in which the covariates under study have different effect on two components of the vector hazard function. The proposed model is useful in real life situations to study the dependence structure of pair of lifetimes on the covariate vector . The well known partial likelihood approach is used for the estimation of parameter vectors. We then introduced a bivariate proportional hazards model for gap times of recurrent events in Chapter 3. The model incorporates both marginal and joint dependence of the distribution of gap times on the covariate vector . In many fields of application, mean residual life function is considered superior concept than the hazard function. Motivated by this, in Chapter 4, we considered a new semi-parametric model, bivariate proportional mean residual life time model, to assess the relationship between mean residual life and covariates for gap time of recurrent events. The counting process approach is used for the inference procedures of the gap time of recurrent events. In many survival studies, the distribution of lifetime may depend on the distribution of censoring time. In Chapter 5, we introduced a proportional hazards model for duration times and developed inference procedures under dependent (informative) censoring. In Chapter 6, we introduced a bivariate proportional hazards model for competing risks data under right censoring. The asymptotic properties of the estimators of the parameters of different models developed in previous chapters, were studied. The proposed models were applied to various real life situations. |
| Description: | Department of Statistics, Cochin University of Science and Technology |
| URI: | http://dyuthi.cusat.ac.in/purl/2708 |
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| Dyuthi-T0758.pdf | (5.959Mb) |
| Abstract: | Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously. |
| Description: | TENCON 2008-2008 IEEE Region 10 Conference |
| URI: | http://dyuthi.cusat.ac.in/purl/4480 |
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| A Reinforcement ... ng Transmission Losses.pdf | (265.1Kb) |
| Abstract: | This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then two variants of RL algorithms are presented. A third algorithm which takes into consideration the transmission losses is also explained. Efficiency and flexibility of the proposed algorithms are demonstrated through different representative systems: a three generator system with given generation cost table, IEEE 30 bus system with quadratic cost functions, 10 generator system having piecewise quadratic cost functions and a 20 generator system considering transmission losses. A comparison of the computation times of different algorithms is also carried out. |
| Description: | Electrical Power and Energy Systems 33 (2011) 836–845 |
| URI: | http://dyuthi.cusat.ac.in/purl/4484 |
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| Reinforcement L ... nomic Dispatch problem.pdf | (311.8Kb) |
| Abstract: | One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved. |
| Description: | School of Engineering, Cochin University of Science and Technology |
| URI: | http://dyuthi.cusat.ac.in/purl/2817 |
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| Dyuthi-T0837.pdf | (6.227Mb) |
| Abstract: | This paper presents a Reinforcement Learning (RL) approach to economic dispatch (ED) using Radial Basis Function neural network. We formulate the ED as an N stage decision making problem. We propose a novel architecture to store Qvalues and present a learning algorithm to learn the weights of the neural network. Even though many stochastic search techniques like simulated annealing, genetic algorithm and evolutionary programming have been applied to ED, they require searching for the optimal solution for each load demand. Also they find limitation in handling stochastic cost functions. In our approach once we learn the Q-values, we can find the dispatch for any load demand. We have recently proposed a RL approach to ED. In that approach, we could find only the optimum dispatch for a set of specified discrete values of power demand. The performance of the proposed algorithm is validated by taking IEEE 6 bus system, considering transmission losses |
| Description: | Fifteenth National Power Systems Conference (NPSC), IIT Bombay, December 2008 |
| URI: | http://dyuthi.cusat.ac.in/purl/4490 |
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| A Reinforcement ... using Neural Networks.pdf | (165.9Kb) |
| Abstract: | Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems |
| Description: | International J. of Recent Trends in Engineering and Technology, Vol. 3, No. 3, May 2010 |
| URI: | http://dyuthi.cusat.ac.in/purl/4489 |
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| Reinforcement L ... through pursuit method.pdf | (568.2Kb) |
| Abstract: | Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems |
| Description: | Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT'09. International Conference on |
| URI: | http://dyuthi.cusat.ac.in/purl/4494 |
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| Reinforcement L ... through pursuit method.pdf | (300.8Kb) |
| Abstract: | Microcosm studies have been carried out to find out the relative survival of Escherichia coli and Salmonella typhimurium in a tropical estuary. Survival has been assessed in relation to the important self-purifying parameters such as biotic factors contained in the estuarine water, toxicity due to the dissolved organic and antibiotic substances in the water and the sunlight. The results revealed that sunlight is the most important inactivating factor on the survival of E. coli and S. typhimurium in the estuarine water. While the biological factors contained in the estuarine water such as protozoans and bacteriophages also exerted considerable inactivation of these organisms, the composition of the water with all its dissolved organic and inorganic substances was not damaging to the test organisms. Results also indicated better survival capacity of E. coli cells under all test conditions when compared to S. typhimurium |
| Description: | Water Research |
| URI: | http://dyuthi.cusat.ac.in/purl/3961 |
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| Relative surviv ... in a tropical estuary.pdf | (221.5Kb) |
| URI: | http://dyuthi.cusat.ac.in/purl/5350 |
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| Dyuthi T-2407.pdf | (6.653Mb) |
| Abstract: | Reliability analysis is a well established branch of statistics that deals with the statistical study of different aspects of lifetimes of a system of components. As we pointed out earlier that major part of the theory and applications in connection with reliability analysis were discussed based on the measures in terms of distribution function. In the beginning chapters of the thesis, we have described some attractive features of quantile functions and the relevance of its use in reliability analysis. Motivated by the works of Parzen (1979), Freimer et al. (1988) and Gilchrist (2000), who indicated the scope of quantile functions in reliability analysis and as a follow up of the systematic study in this connection by Nair and Sankaran (2009), in the present work we tried to extend their ideas to develop necessary theoretical framework for lifetime data analysis. In Chapter 1, we have given the relevance and scope of the study and a brief outline of the work we have carried out. Chapter 2 of this thesis is devoted to the presentation of various concepts and their brief reviews, which were useful for the discussions in the subsequent chapters .In the introduction of Chapter 4, we have pointed out the role of ageing concepts in reliability analysis and in identifying life distributions .In Chapter 6, we have studied the first two L-moments of residual life and their relevance in various applications of reliability analysis. We have shown that the first L-moment of residual function is equivalent to the vitality function, which have been widely discussed in the literature .In Chapter 7, we have defined percentile residual life in reversed time (RPRL) and derived its relationship with reversed hazard rate (RHR). We have discussed the characterization problem of RPRL and demonstrated with an example that the RPRL for given does not determine the distribution uniquely |
| Description: | Department of Statistics, Cochin University of Science and Technology |
| URI: | http://dyuthi.cusat.ac.in/purl/3157 |
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| Dyuthi-T1131.pdf | (3.496Mb) |
| Abstract: | Software systems are progressively being deployed in many facets of human life. The implication of the failure of such systems, has an assorted impact on its customers. The fundamental aspect that supports a software system, is focus on quality. Reliability describes the ability of the system to function under specified environment for a specified period of time and is used to objectively measure the quality. Evaluation of reliability of a computing system involves computation of hardware and software reliability. Most of the earlier works were given focus on software reliability with no consideration for hardware parts or vice versa. However, a complete estimation of reliability of a computing system requires these two elements to be considered together, and thus demands a combined approach. The present work focuses on this and presents a model for evaluating the reliability of a computing system. The method involves identifying the failure data for hardware components, software components and building a model based on it, to predict the reliability. To develop such a model, focus is given to the systems based on Open Source Software, since there is an increasing trend towards its use and only a few studies were reported on the modeling and measurement of the reliability of such products. The present work includes a thorough study on the role of Free and Open Source Software, evaluation of reliability growth models, and is trying to present an integrated model for the prediction of reliability of a computational system. The developed model has been compared with existing models and its usefulness of is being discussed. |
| URI: | http://dyuthi.cusat.ac.in/purl/4965 |
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| Dyuthi-T2041.pdf | (3.721Mb) |
| URI: | http://dyuthi.cusat.ac.in/purl/5509 |
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| Dyuthi T-2551.pdf | (44.51Mb) |
| Abstract: | This work projects photoluminescence (PL) as an alternative technique to estimate the order of resistivity of zinc oxide (ZnO) thin films. ZnO thin films, deposited using chemical spray pyrolysis (CSP) by varying the deposition parameters like solvent, spray rate, pH of precursor, and so forth, have been used for this study. Variation in the deposition conditions has tremendous impact on the luminescence properties as well as resistivity. Two emissions could be recorded for all samples—the near band edge emission (NBE) at 380 nm and the deep level emission (DLE) at ∼500 nm which are competing in nature. It is observed that the ratio of intensities of DLE to NBE (𝐼DLE/𝐼NBE) can be reduced by controlling oxygen incorporation in the sample. 𝐼-𝑉 measurements indicate that restricting oxygen incorporation reduces resistivity considerably. Variation of 𝐼DLE/𝐼NBE and resistivity for samples prepared under different deposition conditions is similar in nature. 𝐼DLE/𝐼NBE was always less than resistivity by an order for all samples.Thus from PL measurements alone, the order of resistivity of the samples can be estimated. |
| Description: | International Journal of Photoenergy Volume 2013, Article ID 105796, 9 pages |
| URI: | http://dyuthi.cusat.ac.in/purl/4723 |
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| Reliable and Da ... luminescence Technique.pdf | (1.904Mb) |
| Abstract: | In this thesis, a variety of available satellite data products have been made use of to bring out a synergistic analysis on the upwelling phenomenon in SEAS. Basic concepts of remote sensing, upwelling and linked oceanography topics have been dealt in this work .Auxiliary data products utilized in this study are described in chapter 2. The climatological monthly variability of the upwelling signatures are detailed under chapter 3. Chapter 4 presents the forcing factors that trigger the upwelling process in SEAS. Chapter 5 describes the oceanic response to the forcing factors with respect to the SST cooling and CHLA blooms. Chapter 6 presents the heat budget of the region and the variability of heat budget terms with respect to upwelling. Chapter 7 describes the inter-annual variability of upwelling intensity in SEAS and the influence of climatic events on upwelling. |
| URI: | http://dyuthi.cusat.ac.in/purl/2951 |
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| Dyuthi-T0942.pdf | (18.44Mb) |
| Abstract: | Salicylaldehyde Schiff base of amino-methylated polystyrene has been developed as a novel reagent for the removal of Fe(III) from solutions. The selectivity of the metal ion uptake over a wide range of different concentrations of metal ion, effect of pH, ligand concentration and the influence of other foreign ions were studied. A very good selectivity was achieved for the removal of the ion. It was found that 0.01 g of the ligand was sufficient to achieve about 96% removal of the metal ion in terms of concentration (ppm) from a 30 ppm solution in acidic pH. |
| URI: | http://dyuthi.cusat.ac.in/purl/511 |
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| env.che.lett.published.pdf | (159.9Kb) |
| URI: | http://dyuthi.cusat.ac.in/purl/5533 |
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| Dyuthi T-2576.pdf | (10.53Mb) |
| URI: | http://dyuthi.cusat.ac.in/purl/5276 |
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| Dyuthi T-2312.pdf | (8.522Mb) |
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