Reliable N sleep shuffled phase damping design for ground bouncing noise mitigation

Sivasankari, B.; Ahilan, A.; Jothin, R.; Malar, A. Jasmine Gnana

doi:10.1016/j.microrel.2018.07.078

Cited by 13 publications

(4 citation statements)

References 4 publications

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“…The RL's education to fully imitate robot dynamics, the agent proposed in this work will require a simulation that can replicate robot motions in real-time, replicate depth cameras to provide depth images for training, support a wide range of obstacles, and be a complete physics simulator [28,29]. The gazebo is the free software simulation that best meets the parameters for training the navigation algorithm [20][21][22][23][24][25][26][27][28][29][30][31]. It offers physical and sensor simulations that are quicker than genuine.…”

Section: Experimental Setup Results and Discussionmentioning

confidence: 99%

Artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction

Sivaranjani¹,

Vinod²

2023

Intelligent Automation &Amp; Soft Computing

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Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment. Reinforcement Learning methods enable a state action function in mobile robots suited to their environment. During trial-and-error interaction with its surroundings, it helps a robot to find an ideal behavior on its own. The Deep Q Network (DQN) algorithm is used in TurtleBot 3 (TB3) to achieve the goal by successfully avoiding the obstacles. But it requires a large number of training iterations. This research mainly focuses on a mobility robot's best path prediction utilizing DQN and the Artificial Potential Field (APF) algorithms. First, a TB3 Waffle Pi DQN is built and trained to reach the goal. Then the APF shortest path algorithm is incorporated into the DQN algorithm. The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System (ROS). The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algorithm. The performance improvement rate of the proposed DQN + APF in comparison with DQN in terms of the number of successful targets is attained by 88%. The performance of the proposed DQN + APF in comparison with DQN in terms of average time is achieved by 0.331 s. The performance of the proposed DQN + APF in comparison with DQN average rewards in which the positive goal is attained by 85% and the negative goal is attained by −90%.

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Section: Experimental Setup Results and Discussionmentioning

confidence: 99%

Artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction

Sivaranjani¹,

Vinod²

2023

Intelligent Automation &Amp; Soft Computing

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“…e power supply circuit, three-phase diode rectifier, speed controller, IGBT-based VSI inverter, feedback optimization circuit, and optimization circuit are all shown in the block diagram in Figure 1. e fundamental concept of a threephase induction motor is to transform three-phase electrical energy into mechanical energy [16,17]. e magnetic field produced by the stator is caused by the alternating current power provided to it.…”

Section: Fodpso-flc-based Focmentioning

confidence: 99%

Hybrid Optimization Approach for Energy Control in Electric Vehicle Controller for Regulation of Three-Phase Induction Motors

Mehbodniya

Kumar

Xie

et al. 2022

Mathematical Problems in Engineering

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Three-phase induction motors are becoming increasingly popular for electric cars and industrial uses because of their improved efficiency and simplicity of production, among other things. Many enterprises and industries use induction motors in several rotating applications. However, it is a difficult talent to master when it comes to controlling the speed of an induction motor for various purposes. This study examines the performance of a three-phase induction motor using approaches such as field-oriented control and direct torque control. This work utilized the fractional order Darwinian particle swarm optimization (FODPSO) method in fuzzy methodology to optimize a motor’s performance. Field-oriented control (FOC) and Direct torque control (DTC) methods are regulated by FODPSO, which is compared to standard FOC and DTC methods. MATLAB-Simulink was used to compare the outcomes of each system’s simulation model to determine which one performed the best. The support vector machine-direct torque control (SVM-DTC) technology is famous for its rapid dynamic response and decreased torque ripples. Using torque and settling time and rising time reduction, the suggested technique is proved to be superior to the present way.

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“…In our work, we proposed two 16-bit GDI Energy and Area Efficient High-Speed Error-Tolerant Adders (GDI-EAHSETA) as described in Figure 7 based on our 1-bit adder EAFA Design 1 and EAFA Design 2. In the proposed 16-bit adder, we modified the accurate 8-bit adder, which uses a combination of 4-bit CSLA and 4-bit RCA [ 29 – 32 ] for the upper MSB 8-bit segment. Lower 8-bit LSB segment uses our proposed EAFA designs.…”

Section: Proposed Gdi-based Addersmentioning

confidence: 99%

Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model

Nagarajan

Muthaiah

Teekaraman

et al. 2022

Computational Intelligence and Neuroscience

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Approximate computing is an upsurging technique to accelerate the process through less computational effort while keeping admissible accuracy of error-tolerant applications such as multimedia and deep learning. Inheritance properties of the deep learning process aid the designer to abridge the circuitry and also to increase the computation speed at the cost of the accuracy of results. High computational complexity and low-power requirement of portable devices in the dark silicon era sought suitable alternate for Complementary Metal Oxide Semiconductor (CMOS) technology. Gate Diffusion Input (GDI) logic is one of the prompting alternatives to CMOS logic to reduce transistors and low-power design. In this work, a novel energy and area efficient 1-bit GDI-based full swing Energy and Area efficient Full Adder (EAFA) with minimum error distance is proposed. The proposed architecture was constructed to mitigate the cascaded effect problem in GDI-based circuits. It is proved by extending the proposed 1-bit GDI-based adder for different 16-bit Energy and Area Efficient High-Speed Error-Tolerant Adders (EAHSETA) segmented as accurate and inaccurate adder circuits. The proposed adder’s design metrics in terms of delay, area, and power dissipation are verified through simulation using the Cadence tool. The proposed logic is deployed to accelerate the convolution process in the Low-Weight Digit Detector neural network for real-time handwritten digit classification application as a case study in the Intel Cyclone IV Field Programmable Gate Array (FPGA). The results confirm that our proposed EAHSETA occupies fewer logic elements and improves operation speed with the speed-up factor of 1.29 than other similar techniques while producing 95% of classification accuracy.

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Reliable N sleep shuffled phase damping design for ground bouncing noise mitigation

Cited by 13 publications

References 4 publications

Artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction

Artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction

Hybrid Optimization Approach for Energy Control in Electric Vehicle Controller for Regulation of Three-Phase Induction Motors

Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model

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