Inner Spirit of Sree Narayana Guru’s Poetry

Sree Narayana Guru’s (1855-1928) position among the renaissance leaders of Modern Kerala is well established. The visions of the Guru, who guided the people trapped in slavery and ignorance to compassion and liberation, were in a sense the corner stones of humanity. At the same time Sree Narayana Guru was a spiritual leader, social reformer, philosopher and poet. Guru’s active work was form the revolution at Aruvippuram in 1888, which dedicated the temple to the untouchables. As a result of the spiritual and worldly activities of Guru, Kerala has become a model state in India. It is no exaggeration to say that Guru’s action plan for transformation in the fields of customs, belief, thought, education, culture, employment and industry have made Kerala a modern society. Guru’s influence on the renaissance of Malayalam Literature as well as on the Kerala renaissance is acknowledged. But the contributions of Guru, as a poet has not been adequately evaluated. here is an explanation of the magnetic force of Guru’s poem, that unleashed the socio-political structure and world consciousness.

Ensemble Kalman Filter for non-conservative moving mesh solvers with a joint physics and mesh location update

<p>Numerical solvers using adaptive meshes can focus computational power on important regions of a model domain capturing important or unresolved physics. The adaptation can be informed by the model state, external information, or made to depend on the model physics. <br> In this latter case, one can think of the mesh configuration  as part of the model state. If observational data is to be assimilated into the model, the question of updating the mesh configuration with the physical values arises. Adaptive meshes present significant challenges when using popular ensemble Data Assimilation (DA) methods. We develop a novel strategy for ensemble-based DA for which the adaptive mesh is updated along with the physical values. This involves including the node locations as a part of the model state itself allowing them to be updated automatically at the analysis step. This poses a number of challenges which we resolve to produce an effective approach that promises to apply with some generality. We evaluate our strategy with two testbed models in 1-d comparing to a strategy that we previously developed that does not update the mesh configuration. We find updating the mesh improves the fidelity and convergence of the filter. An extensive analysis on the performance of our scheme beyond just the RMSE error is also presented.</p>

2D sequential data assimilation in Elmer/Ice with Stokes

<p>Providing suitable initial states is a long-standing problem in numerical modelling of glaciers and ice sheets. Models often require lengthy relaxation periods to dissipate incompatibilities between input datasets gathered over different timeframes, which may lead to the modelled initial state diverging significantly from the real state of the glacier, with consequent effects on the accuracy of the simulation. Sequential data assimilation offers one possibility for resolving this issue: by running the model over a period for which various observational datasets are available and loading observations into the model at the time they were gathered, the model state can be brought into good agreement with the real glacier state at the end of the observational window. This assimilated model state can then be used to initialise prognostic runs without introducing model artefacts or a distorted picture of the actual glacier.</p><p>In this study, we present a framework for conducting sequential data assimilation in a 2D, flowline setting of the open-source, finite-element glacier flow model, Elmer/Ice, and solving the Stokes equations rather than using the shallow shelf approximation. Assimilation is undertaken using the open-source PDAF library developed at the Alfred Wegener Institute. We demonstrate that the set-up allows us to accurately retrieve the bed of a synthetic glacier and present our progress in extending it to a full 3D simulation.</p>

The Newton’s Polynomial Based - Automatic Model Generation (AMG) for Sensor Calibration to Improve the Performance of the Low-Cost Ultrasonic Range Finder (HC-SR04)

The ultrasonic range finder sensors is a general-purpose sensor to measure the distance contactless. This sensor categorized as low-cost sensor that widely used in various application. This sensor has a significant deviation that lead to significant error in the measurement result. The error that produced by this sensor tends to increase proportionally to the measured distance. The implementation of the particular algorithm is required to reduce the error value. The model-based calibration is a solution to increase the accuracy. The model-based solutions are no longer feasible if the states of the model have changed. The longer of the usage of the sensor lead to sensor fatigue. Sensor fatigue is one of the causes of model state changes. As long as the drift still within the tolerance limit, the performance of the sensor still can be restored by using calibration method. The model-based calibration calibrates the sensor by using the model. The update of the model must be made whenever the changing of the model state occurred. Since the manual model making process is not an easy task, time and cost required, then the Newton polynomial-based AMG (Automatic Model Generation) have been implemented to this research. The AMG algorithm generates the new sensor model automatically based on the most updated states. This automatic model generation is implemented in the calibration process of the ultrasonic sensor. The implementation of polynomial-based AMG algorithm for sensor calibration have been succeeded to improve the accuracy of the calibrated sensor by 96.4% and reduce the MSE level from 25.6 to 0.914.