Simplification of water supply networks is an indispensible design step to make the original network easier to be analysed. The impact of networks’ simplification on water hammer phenomenon is investigated. This study uses two loops network with different diameters, thicknesses, and roughness coefficients. The network is fed from a boundary head reservoir and loaded by either distributed or concentrated boundary water demands. According to both hydraulic and hydraulic plus water quality equivalence, three simplification levels are performed. The effect of demands’ concentration on the transient flow is checked. The transient flow is initialized by either concentrated or distributed boundary demands which are suddenly shut-off or released. WHAMO software is used for simulation. All scenarios showed that both hydraulic equivalence and demands’ concentration simplifications increase the transient pressure and flow rate. However, hydraulic plus water quality equivalence simplification produces an adverse effect. Therefore, simplifications of the networks should be done carefully. Also, it was found that pump shut-off gives the same trend of valve shut-off or release.

In this paper, we propose a novel hand written character recognition system using a combination of gradient-based features and run length count (GBF–RLC). The performance of the proposed method has been tested on Malayalam script, a South Indian language. The gradient of image is the intensity at each point, giving the direction of the largest possible increase from light to dark and the rate of change in that direction. RLC is the count of contiguous group of 1’s encountered in a left to right/top to bottom scan of a character image or block of an image. Classification was carried out with a Simplified Quadratic Classifier (SQDF) and Multi Layer Perceptron (MLP). A database containing 19,800 isolated handwritten characters pertaining to 44 classes was used for the study. The feature vector is augmented by including aspect ratio, position of centroid and ratio of pixels on the vertical halves of a character image. The recognition accuracy of 99.78% was achieved with minimum computational and storage requirement.

This invention system involves hardware, firmware and software to develop an intelligent control system of automatic window motor with diverse wireless sensor network (WSN) devices for health and environmental monitoring. The parts of this invention are improved by implementing the WSN mote into environmental sensors that may detect temperature, humility, toxic gas, smog or aerosol, etc. With embedded system design, these sensors are capable of delivering WSN signal packets based on ZigBee protocol that follows the IEEE 802.14.4 standards. The primary hardware of the system is the window motor with circuit design by integrating micro control unit (MCU), radio frequency (RF) and WSN antenna to receive command. The firmware developed under embedded system can bridge hardware and software to control the window at the specified position. At the back end, the control system software can manage diverse sensor data and provide the interface for remote monitoring.

In this paper, the hydrogen and hydrogen-methane mixed plasma have been generated inside a 33 cm diameter quartz bell jar with a low power (9 KW) and lower frequency 915 MHz microwave plasma chemical vapor deposition system. The reactor is being used for growing polycrystalline diamond (PCD) over large area (100 mm). The generated plasma is diagnosed by in situ optical emission spectroscopy method with wave length ranging from 200 to 900 nm. The effects of microwave power, chamber pressure and gas concentration on plasma characteristics have been studied in this work. Within the optical range, Balmer H α , H β , C2swan band and CH lines have been detected at the wavelengths of 655.95, 485.7, 515.82 and 430.17 nm, respectively. It has been observed that for hydrogen plasma, the amount of transition from hydrogen atom inner shell 3 to 2 (H α ) is almost constant with increasing microwave (MW) power (from 2000 to 2800 W) and pressure (from 15 to 30 Torr) initially, after that it increases with further increase of MW power and pressure, whereas, the transition from 4 to 2 (H β ) is slowly increased with increasing MW power and pressure. For hydrogen-methane plasma, intensities of C2 swan band, i.e., the transitions from D3π g to A3π μ energy levels, are also increased with the increasing microwave power and reactor pressure. It has been observed that the radicals present in the plasma are affected by variation of different reactor parameters like pressure, MW power, CH4 concentration, etc.

In structural analysis, especially in indeterminate structures, it becomes essential to know material and geometrical properties of members. The codal provisions recommend elastic properties of concrete and steel and these are fairly accurate enough. The stress–strain curve for concrete cylinder or a cube specimen is plotted. The slope of this curve is modulus of elasticity of plain concrete. Another method of determining modulus of elasticity of concrete is by flexural test of a beam specimen. The modulus of elasticity most commonly used for concrete is secant modulus. The modulus of elasticity of steel is obtained by performing a tension test of steel bar. While performing analysis by any software for high rise building, cross area of plain concrete is taken into consideration whereas effects of reinforcement bars and concrete confined by stirrups are neglected. The aim of study is to determine elastic properties of reinforced cement concrete material. Two important stiffness properties such as AE and EI play important role in analysis of high rise RCC building idealized as plane frame. The experimental programme consists of testing of beams (model size 150 × 150 × 700 mm) with percentage of reinforcement varying from 0.54 to 1.63%. The experimental results are verified by using 3D finite element techniques. This study refers to the effect of variation of percentage of main longitudinal reinforcement and concrete grade. Effect of confinement is not considered and it appears in a separate study.

This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propagation neural network (BPNN). This analysis is carried out following a series of experiments employing high speed steel (HSS) drills for drilling on mild steel workpieces, under different sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as the average flank wear in each case. The results show that the PSO trained ANN not only gives better prediction results and reduced computational times compared to the BPNN, it is also a more robust model, being free of getting trapped in local optimum solutions unlike the latter. Besides, it offers the advantages of a straight-forward logic, simple realization and underlying intelligence.

A numerical study is carried out using finite element method, to examine the effects of square and rectangular cutout on the buckling behavior of a sixteen ply quasi-isotropic graphite/epoxy symmetrically laminated rectangular composite plate [0°/+45°/-45°/90°]2s, subjected to various linearly varying in-plane compressive loads. Further, this paper addresses the effects of size of square/rectangular cutout, orientation of square/rectangular cutout, plate aspect ratio(a/b), plate length/thickness ratio(a/t), boundary conditions on the buckling bahaviour of symmetrically laminated rectangular composite plates subjected to various linearly varying in-plane compressive loading. It is observed that the various linearly varying in-plane loads and boundary conditions have a substantial influence on buckling strength of rectangular composite plate with square/rectangular cutout.

In this paper, a novel occlusion invariant face recognition algorithm based on Mean based weight matrix (MBWM) technique is proposed. The proposed algorithm is composed of two phases—the occlusion detection phase and the MBWM based face recognition phase. A feature based approach is used to effectively detect partial occlusions for a given input face image. The input face image is first divided into a finite number of disjointed local patches, and features are extracted for each patch, and the occlusion present is detected. Features obtained from the corresponding occlusion-free patches of training images are used for face image recognition. The SVM classifier is used for occlusion detection for each patch. In the recognition phase, the MBWM bases of occlusion-free image patches are used for face recognition. Euclidean nearest neighbour rule is applied for the matching. GTAV face database that includes many occluded face images by sunglasses and hand are used for the experiment. The experimental results demonstrate that the proposed local patch-based occlusion detection technique works well and the MBWM based method shows superior performance to other conventional approaches.

Crystallization is extensively used in different industrial applications, including the production of a wide range of materials such as fertilizers, detergents, food and pharmaceutical products, as well as in the mineral processing industries and treatment of waste effluents. In spite of the wide-spread use of crystallization, a clear understanding of the thermodynamic, kinetic and hydrodynamic aspects of the design methodologies are not yet well established. More often than not crystallization is still considered an art especially in fine-chemicals, pharmaceuticals and life-sciences sector. It is essential to understand and relate key thermodynamic, kinetic and hydrodynamic aspects to crystallizer performance, not just in terms of yield but also in terms of product quality (characterized by particle size distribution, morphology, polymorphism and the amount of strain as well as the uptake of solvent or impurities in the crystal lattice). This paper attempts to do that by critically reviewing published experimental and modelling studies on establishing and enhancing state-of-the-art thermodynamic, kinetic and hydrodynamic aspects of crystallization. Efforts are made to discuss and raise points for emerging modelling tools needed for a flexible design and operation of crystallizers and crystallization processes that are needed to meet the ever increasing demand on precise product specifications. Focus is on bringing out the trends which can be used as perspectives for future studies in this field.

Economic load dispatch is one of the vital purposes in electrical power system operation, management and planning. Economic dispatch problem is one of the most important problems in electric power system operation. In large scale system, the problem is more complex and difficult to find out optimal solution because it is nonlinear function and it contains number of local optimal. Combined economic emission dispatch (CEED) problem is to schedule the committed generating units outputs to meet the required load demand at minimum operating cost with minimum emission simultaneously. The main aim of economic load dispatch is to reduce the total production cost of the generating system and at the same time the necessary equality and inequality constraints should also be fulfilled. This leads to the development of CEED techniques. There are various techniques proposed by several researchers to solve CEED problem based on optimization techniques. But still some problems such as slower convergence and higher computational complexity exist in using the optimization techniques such as GA for solving CEED problem. This paper proposes an efficient and reliable technique for combined fuel cost economic optimization and emission dispatch using the Modified Ant Colony Optimization algorithm (MACO) to produce better optimal solution. The simulation results reveal the significant performance of the proposed MACO approach.