A STUDY ON HIGH PERFORMANCE CONCRETE WITH SUPERFINE BRUCITE

Balakrishnan.P, J.Jegan
Department of Structural Engineering Regional Centre of Anna University Madurai, India
Department of Civil Engineering University College of Engineering Ramanathapuram, India

     This paper presents results of an experimental study on high performance concrete with brucite as a mineral additive. In this work the cement is replaced by brucite powder with 5%, 10%, 15%, 20% and 25% of its weight in M60 grade concrete. All the proportions of concrete are prepared and the optimum replacement of brucite is identified by various tests results with a comparison of conventional concrete. The fresh and hardened concrete properties are evaluated and the micro structural behavior of concrete is analyzed by scanning electron microscope. It is found that the incorporation of brucite powder up to 15% can enhance the performance of concrete. The content of hydrate product of high performance concrete also increased with the increase of brucite.

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CHANGE DETECTION IN SAR IMAGES BASED ON IMAGE FUSION AND SPATIAL FUZZY CLUSTERING METHOD

J.Marimuthu
Assistant Professor Head - Department of CSE, Sri Vidya College of Engineering and Technology, Virudhunagar, Tamilnadu, India.

     Change detection approach for synthetic aperture radar (SAR) images is based on an image fusion and a spatial fuzzy clustering algorithm. The image fusion technique generates a difference image by using complementary information from a mean-ratio image and a log-ratio image. Wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and high-frequency band, respectively to restrain the background information and enhance the information of changed regions in the fused difference image. A fuzzy local-information C-means clustering algorithm proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and reduces the effect of speckle noise. In rationing, changes are obtained by applying pixel-by-pixel ratio operator to the considered couple of temporal images. In the case of SAR images, the ratio operator is typically used instead of subtraction operator. The result will be proven that rationing generates better difference image for change detection using spatial fuzzy clustering approach and efficiency of this algorithm will be exhibited by sensitivity and correlation evaluation.

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ANALYSIS OF SPACE VECTOR MODULATION (SVM) CONCEPT FOR THREE PHASES TWO-LEVEL UNIDIRECTIONAL PWM RECTIFIER

J.MOHAN
1Assistant Professor, Department of EEE, Ranganathan Engineering College, Coimbatore, India.

     Space vector modulation applied to three phase Two-level unidirectional PWM rectifier will be discussed in this proposed work. A methodology for the use of this modulation is proposed and applied in different group of rectifier. For each group of rectifier the converter switching techniques are analyzed to determine switch control signals for space vector modulation. One switching sequence is proposed for all rectifiers in order to minimize the number of switch commutations and reduce the switching losses thereby improving performance of the converter. Switching sequences are determined and performed by a simple space vector pulse width modulation. Two level converter topology is introduced in order to maintain constant output voltage and reduce the number of sensors and controllers. To evaluate the proposed modulation techniques, simulations results have been presented for low and medium power applications.

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EVALUATION AND OPTIMIZATION OF MACHINING PARAMETERS OF 80%TN-14%NI-6%CU COMPOSITE

S.Marichamy ¹, Dr.M.Saravanan ^2
1 Assistant Professors in Mechanical Engineering, Vickram college of Engineering, Sivagangai, India.
² Principal, SS College of Engineering,Palani-India

     The experimental investigation of Material removal rate (BRR), and Tool wear rate (TWR) during machining of 80%Sn-14%Ni-6%Cu using EDM was studied in this paper. The input parameters include current (I), Pulse ON time (Ton), Voltage, Flushing pressure were used for experimental work. A well-designed experimental scheme was used to reduce the total number of experiments. The results of analysis of variance (ANOVA) indicate that the proposed mathematical model can be adequately describe the performance within the limit of factors being studied. The optimal set of process parameters has also been predicted to maximize the MRR and minimize the TWR.To optimize the machining parameters of EDM on 80%Sn-14%Ni-6%Cu by using the Response Surface Methodology(RSM), Box-behnken Design(BBD) and ANOVA methods. It can be observed that Current has the largest effect on the material removal rate.The Pulse ON has the smallest effect on the MRR.

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REMOTE SENSING IMAGE CLASSIFICATION USING FUZZY RULE CLASSIFIER

¹M.Saranya, ²S. Kalaiselvi, ³Dr.K.G. Srinivasagan
¹ PG-Scholar, Department of CSE-PG, National Engineering College Kovilpatti,Tamilnadu,India.
² Assistant Professor, Department of CSE-PG National Engineering College Kovilpatti,Tamilnadu,India.
³ Professor and Head , Dept of CSE-PG, National Engineering college,Kovilpatti,Tamilnadu,India.

     Land cover of the earth’s land surface has been changing since time immemorial and is likely to continue to change in the future. These changes are occurring at a range of spatial scales from local to global and at temporal frequencies of days to millennia. Both natural and anthropogenic forces are responsible for the change. Natural forces such as continental drift, glaciations, flooding, tsunamis and anthropogenic forces such as conversion of forest to agriculture, urban sprawl, and forest plantations have changed the dynamics of land-use/land-cover types throughout the world. The main intention of the classification is to group the pixels in the image into form the several land cover classes, or “themes”. And then the categorized data is used to produce the thematic map of land cover classes. Image classification is one of the most important parts in image processing. Image segmentation is the process of dividing the images into regions with similar attributes. The proposed research consists of three phases. First phase, the image segmentation is done by Self Organizing Map (SOM). Second phase, feature extraction is done by Gray Level Coocurrence Matrix methods. Finally the classification is done by fuzzy rule based classification. The final output is land use and land cover map, with labels of corresponding classes.

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