![]() ![]() To do a comparative study in the accuracy of prediction of the characteristics of the deposited droplet, triangular, Gaussian and bell-shaped membership functions have been considered after a careful study. In the proposed adaptive neuro-fuzzy predictive system, hybridization and backpropagation were used to predict the values. In order to stabilize this fluctuating jet frequency, a second-order non-linear regression equation has been implemented. ![]() Since it is difficult to maintain constant operating conditions, the jet frequency and droplet diameter fluctuate. ![]() This paper focuses on a fuzzy logic-based neural networking system, also known as ANFIS which has been used to predict and model the output parameter, i.e., droplet frequency for three controllable processes like nozzle-substrate gap, ink flow rate, and applied voltage. This method helps to achieve higher resolution and control which is way better than that of traditional processes. Additionally, a user study reveals that the questions from our systems are more natural (4.02 on average on a scale of 1 to 5) as compared to a state-of-the-art (3.08 on average).Įlectrohydrodynamic inkjet printing-based micro-additive manufacturing technology is a nano-manufacturing process using fluid jet printing, influenced by an electric field through nano-scale nozzles. Our system can identify the stalemate and resolve them with appropriate dialogue exchange with 82% accuracy. We evaluate our system based on a data set of initial instruction and situational scene pairs. To realize the possible stalemate, we utilize the dense captions of the observed scene and the given instruction jointly to compute the robot’s next action. Through dialogue, it either finds a cue to move forward in the original plan, an acceptable alternative to the original plan, or affirmation to abort the task altogether. In this article, we present a system called Talk-to-Resolve (TTR) that enables a robot to initiate a coherent dialogue exchange with the instructor by observing the scene visually to resolve the impasse. However, while executing the task, the robot may face unforeseeable circumstances due to the variations in the observed scene and therefore requires further user intervention. If a robot accepts task instruction in natural language, first, it has to understand the user’s intention by decoding the instruction. The utility of collocating robots largely depends on the easy and intuitive interaction mechanism with the human. However, the video stream processing speed is able to be reduced at 15 fps and latency less than 415 ms when four users appear concurrently. The proposed robot can move on uneven surfaces with a speed at 0.21 m/s and an accuracy over 90%. The processing speed at 14 fps of video stream in real-time. The robot controller is embedded into hardware of 128 graphics processing unit cores and 4 ARM Cortex-A9 cores in order to execute convolutional neural network (NCNN) algorithms for elderly recognition and body tracking. In addition, the video streaming algorithm with the pipeline mechanism is integrated on robot controllers so that the owner interacts with the elderly through the internet. The design allows the robot to follow the elderly and accompany them in real-time. The proposed robot is based on humanoid structure and AI-embedded-GPU controller. In this paper, a design of mobile servant robot with integrated tracking algorithm in order to assist the elderly by companionship is proposed not only to help families take care of their elderly at home but also reduce the pressure on health-care providers. The advent of elderly care robots will reduce that pressure. Span lang="EN-US">Recently, elderly population increasing worldwide has put higher pressure on health-care providers and their families. ![]()
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