Articles written in Sadhana
Volume 42 Issue 9 September 2017 pp 1617-1628
Bearing-only passive target tracking is a well-known underwater defence issue dealt in the recent past with the conventional nonlinear estimators like extended Kalman filter (EKF) and unscented Kalman filter (UKF). It is being treated now-a-days with the derivatives of EKF, UKF and a highly sophisticated particle filter(PF). In this paper, two novel methods based on the Estimate Merge Technique are proposed. The Estimate Merge Technique involves a process of getting a final estimate by the fusion of a posteriori estimates given by different nonlinear estimates, which are in turn driven by the towed array bearing-only measurements. The fusion of the estimates is done with the weighted least squares estimator (WLSE). The two novel methods, one named as Pre-Merge UKF and the other Post-Merge UKF, differ in the way the feedback to the individual UKFsis applied. These novel methods have an advantage of less root mean square estimation error in position and velocity compared with the EKF and UKF and at the same time require much lesser number of computations than that of the PF, showing that these filters can serve as an optimal estimator. A testimony of the aforementioned advantages of the proposed novel methods is shown by carrying out Monte Carlo simulation in MATLAB R2009a for a typical war time scenario
Volume 43 Issue 5 May 2018 Article ID 0066
The present research work involves the implementation of Modified Chaotic Invasive Weed Optimization (MCIWO) algorithm for optimizing the gains of torque based proportional integral and derivative (PID) controller used to control the motors of the biped robot while walking on flat surface. While designing thecontroller, the dynamics of the biped robot has been derived using the well-known Lagrange-Euler (L-E) formulation. Subsequently, manual tuning procedure is employed to find the ranges of the gains of PID controller used in the developed algorithm. Once it is optimized, the effectiveness of the proposed algorithm is thencompared with the Differential Evolution (DE) algorithm, in terms of variation of error, torque required, zero moment point (ZMP) and dynamic balance margin (DBM) of the biped robot. It has been observed that the MCIWO algorithm tuned PID controller is found to perform better than DE tuned controller. Further, theoptimal gait obtained through the developed algorithm is validated by executing it on the real robot. It has been observed that the robot has successfully negotiated the flat terrain with the gaits obtained by the optimal PIDcontroller.
Volume 44 Issue 2 February 2019 Article ID 0040
This paper reports a new approach for clustering melodies in audio music collections of both western as well as Indian background and its application to genre classification. A simple yet effective new classification technique called mean centred clustering (MCC) is discussed. The proposed technique maximizesthe distance between different clusters and reduces the spread of data in individual clusters. The use of MCC as a preprocessing technique for conventional classifiers like artificial neural network (ANN) and support vector machine (SVM) is also demonstrated. It is observed that the MCC-based classifier outperforms the classifiers based on conventional techniques such as Principal Component Analysis (PCA) and discrete cosine transform (DCT). Extensive simulation results obtained on different data sets of western genre (ISMIR) and classicalIndian ragas are used to validate the efficiency of proposed MCC-based clustering algorithm and ANN/SVM classifiers based on MCC. As an additional endeavour, the performance of MCC on preprocessed data from PCA and DCT is studied. Based on simulation results, it is concluded that the application of MCC on DCT coefficients resulted in the highest overall classification success rate over different architectures of the classifiers.
Volume 45 All articles Published: 27 June 2020 Article ID 0167
The present work is aimed to explore the microstructural and mechanical characteristics of coal-fly ash reinforced iron metal-matrix composites (IMMCs), synthesized through powder metallurgy technique. Coalfly ash wt%, compacting load and sintering temperature were considered as the input variables, whereas sintered density and microhardness of the composites were taken as the output responses. Flowability and compressibility of the starting materials were demonstrated using Hausner ratio and Carr’s index. Decorous morphological,crystallographic and elemental characteristics of the starting materials and IMMCs were deliberated using Scanning electron microscopy, X-ray diffraction and Energy-dispersiveX-ray spectroscopy investigations respectively. A significant improvement in the microhardness of IMMCs by 50% and drop in density by 35% were found at 15 wt% as compared to 0 wt% reinforcement. The substantial increase in the microhardness eventually resulted in an increase in their specific microhardness by a factor of two. Significant improvements inthe microhardness of IMMCs at 15 wt % of reinforcement, compacted at 10 ton and sintered at 1150°C were found to be prompted by the strengthening mechanisms like load transfer, Hall–Petch effect and Taylor strengthening. The analytically calculated microhardness in the light of strengthening mechanisms was found smaller than the corresponding experimental values as a function of wt % of reinforcement. Further, statistical analysis of the obtained results was carried out using response surface methodology