Hate Speech is a widespread problem that degrades a person or people based on their race, religion, gender or disability. This research work proposes a tool to raise awareness on the persistent hate speech in social media platforms. The primary aim of this research work is to highlight the content that promotes violence or hatred against individuals or groups based on religion, gender or ethnicity. Logistic regression is a technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems. Using this algorithm, the model trains itself from the dataset and identifies and displays the sentiment of the tweets. Also, to get the real-time analysis on tweets, Twitter API and libraries such as Tweepy and Textblob are used. The proposed model has the ability to detect the appropriate sentiment with 83.98 percent accuracy. The tool is made free and available for demo use to thepublic
Model predictive control (MPC) approaches are widely used in robotics, because they guarantee feasibility and allow the computation of updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of the parameters of the cost function in order to obtain good performance. For instance, when a legged robot has to react to disturbances from the environment (e.g., to recover after a push) or track a specific goal with statically unstable gaits, the effectiveness of the algorithm can degrade. In this work, we propose a novel optimization-based reference generator which exploits a linear inverted pendulum (LIP) model to compute reference trajectories for the center of mass while taking into account the possible underactuation of a gait (e.g., in a trot). The obtained trajectories are used as references for the cost function of the nonlinear MPC presented in our previous work. We also present a formulation that ensures guarantees on the response time to reach a goal without the need to tune the weights of the cost terms. In addition, footholds are corrected by using the optimized reference to drive the robot toward the goal. We demonstrate the effectiveness of our approach both in simulations and experiments in different scenarios with the Aliengo robot.
The purpose of this report is to identify /observe and determine the pattern of velocity profile and pressure distribution by using CFD simulation program after the 3D design and modeling of the pump is made using Vista CPD. We have also created a Solid model using Fusion 360 to get a clear idea of Centrifugal pump design. Basically, this report revolves around the idea of investigating the effect and distribution of velocity profile and pressure within a pump having the following specification, Head = 20 m, Flow rate = 100 m3 /hr, and RPM = 2000. 3D Navier–Stokes equations were solved using ANSYS CFX. The standard k −εturbulence model was chosen for the turbulence model. From the design point of view, we have studied the effects of different parameters like rotational speed, volume flow rate etc on the impeller and volute. From the simulation results it was observed that the pressure increases gradually from impeller inlet to outlet. The static pressure on the pressure side is evidently larger than that on the suction side at the same impeller radius. In addition to this, it was observed that the velocity increases from the impeller inlet until it enters the volute casing. It then drops to a minimum value at the outlet region.
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