Face Recognition using Probabilistic Neural Networks

In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results

Face Recognition using Probabilistic Neural Networks

n this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.

Reinforcement Learning Approaches to Power System Scheduling

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.

Marketing orientation of small Firms- a study with reference to Trivandrum district

The small business has attracted very little attention of the historians in the ancient times, or public mind inspite of the fact that its impact on the various civilisations has been phenominal. Even in recent times economists considered the small firms as inappropriate, obselate and anacronistic as it cannot assimilate the full potential of technological change in the production system. But today everybody agrees that the small business has a definite role in shaping the human destiny and enhancing the quality of life in any society. In a developing country like India small firms are necessary to generate employment for millions, high standared of personal choice to consumers, provide competition and act as a check to monopoly power; further the small firms provide an important source of innovation and in turn it paves the way for entrepreneur development in the society. In many countries the small enterprises played a significant role in the growth and development of their economic system. Italy and Japan are quoted as classic examples . In India, too, with the abundance of labour and scarce capital resources small firms have been promoted and protected by the government. But one must say that the small firm owners/managers in India have been shy in developing a market orientation in themeselves. Due to this many firms failed and closed. The alarming rate of sickness among the small firms in India may be attributed to the lack of market driven/customer orientation approach among the owner/managers of small business. So the study on the market oreintation of the small firms has never been in the mind of marketing experts and academicians. Thus, an attempt is made to enquire into them systematically and scientifically. For the study, Trivandrum district in Kerala has been selected. The data for the study has been collected by the help of a schedule which has been prepared after consulting the relevant literature and after consultation with experts in the field, academicians and practising managers.

Behavior of geopolymer concrete exposed to elevated temperatures

The research in the area of geopolymer is gaining momentum during the past 20 years. Studies confirm that geopolymer concrete has good compressive strength, tensile strength, flexural strength, modulus of elasticity and durability. These properties are comparable with OPC concrete.There are many occasions where concrete is exposed to elevated temperatures like fire exposure from thermal processor, exposure from furnaces, nuclear exposure, etc.. In such cases, understanding of the behaviour of concrete and structural members exposed to elevated temperatures is vital. Even though many research reports are available about the behaviour of OPC concrete at elevated temperatures, there is limited information available about the behaviour of geopolymer concrete after exposure to elevated temperatures. A preliminary study was carried out for the selection of a mix proportion. The important variable considered in the present study include alkali/fly ash ratio, percentage of total aggregate content, fine aggregate to total aggregate ratio, molarity of sodium hydroxide, sodium silicate to sodium hydroxide ratio, curing temperature and curing period. Influence of different variables on engineering properties of geopolymer concrete was investigated. The study on interface shear strength of reinforced and unreinforced geopolymer concrete as well as OPC concrete was also carried out. Engineering properties of fly ash based geopolymer concrete after exposure to elevated temperatures (ambient to 800 °C) were studied and the corresponding results were compared with those of conventional concrete. Scanning Electron Microscope analysis, Fourier Transform Infrared analysis, X-ray powder Diffractometer analysis and Thermogravimetric analysis of geopolymer mortar or paste at ambient temperature and after exposure to elevated temperature were also carried out in the present research work. Experimental study was conducted on geopolymer concrete beams after exposure to elevated temperatures (ambient to 800 °C). Load deflection characteristics, ductility and moment-curvature behaviour of the geopolymer concrete beams after exposure to elevated temperatures were investigated. Based on the present study, major conclusions derived could be summarized as follows. There is a definite proportion for various ingredients to achieve maximum strength properties. Geopolymer concrete with total aggregate content of 70% by volume, ratio of fine aggregate to total aggregate of 0.35, NaOH molarity 10, Na2SiO3/NaOH ratio of 2.5 and alkali to fly ash ratio of 0.55 gave maximum compressive strength in the present study. An early strength development in geopolymer concrete could be achieved by the proper selection of curing temperature and the period of curing. With 24 hours of curing at 100 °C, 96.4% of the 28th day cube compressive strength could be achieved in 7 days in the present study. The interface shear strength of geopolymer concrete is lower to that of OPC concrete. Compared to OPC concrete, a reduction in the interface shear strength by 33% and 29% was observed for unreinforced and reinforced geopolymer specimens respectively. The interface shear strength of geopolymer concrete is lower than ordinary Portland cement concrete. The interface shear strength of geopolymer concrete can be approximately estimated as 50% of the value obtained based on the available equations for the calculation of interface shear strength of ordinary portland cement concrete (method used in Mattock and ACI). Fly ash based geopolymer concrete undergoes a high rate of strength loss (compressive strength, tensile strength and modulus of elasticity) during its early heating period (up to 200 °C) compared to OPC concrete. At a temperature exposure beyond 600 °C, the unreacted crystalline materials in geopolymer concrete get transformed into amorphous state and undergo polymerization. As a result, there is no further strength loss (compressive strength, tensile strength and modulus of elasticity) in geopolymer concrete, whereas, OPC concrete continues to lose its strength properties at a faster rate beyond a temperature exposure of 600 °C. At present no equation is available to predict the strength properties of geopolymer concrete after exposure to elevated temperatures. Based on the study carried out, new equations have been proposed to predict the residual strengths (cube compressive strength, split tensile strength and modulus of elasticity) of geopolymer concrete after exposure to elevated temperatures (upto 800 °C). These equations could be used for material modelling until better refined equations are available. Compared to OPC concrete, geopolymer concrete shows better resistance against surface cracking when exposed to elevated temperatures. In the present study, while OPC concrete started developing cracks at 400 °C, geopolymer concrete did not show any visible cracks up to 600 °C and developed only minor cracks at an exposure temperatureof 800 °C. Geopolymer concrete beams develop crack at an early load stages if they are exposed to elevated temperatures. Even though the material strength of the geopolymer concrete does not decrease beyond 600 °C, the flexural strength of corresponding beam reduces rapidly after 600 °C temperature exposure, primarily due to the rapid loss of the strength of steel. With increase in temperature, the curvature at yield point of geopolymer concrete beam increases and thereby the ductility reduces. In the present study, compared to the ductility at ambient temperature, the ductility of geopolymer concrete beams reduces by 63.8% at 800 °C temperature exposure. Appropriate equations have been proposed to predict the service load crack width of geopolymer concrete beam exposed to elevated temperatures. These equations could be used to limit the service load on geopolymer concrete beams exposed to elevated temperatures (up to 800 °C) for a predefined crack width (between 0.1mm and 0.3 mm) or vice versa. The moment-curvature relationship of geopolymer concrete beams at ambient temperature is similar to that of RCC beams and this could be predicted using strain compatibility approach Once exposed to an elevated temperature, the strain compatibility approach underestimates the curvature of geopolymer concrete beams between the first cracking and yielding point.