To reach SDSB (Self-Driving Sweeping Bot) in an efficient-sweeping manner, data collection of visual images regarding sweeping target must be conducted prior to analyze the required sweeping objects with other noises. In this work, three categorized target objects including, fallen leaves, speed bumps and manhole cover etc. are involved in training and validation phases. To reach SDSB with real-time object detection, the work further investigated one-stage of Yolo v5 learning approach of four version including, Yolo v5s, Yolo v5m, Yolo v51, and Yolo v5x, wherein Yolo v5s in terms of its benefits on lower frame rate, high-accuracy, and high-speed characteristics for real-time object detection. Furthermore, to detailed analyze the Yolo v5s performance on training and validation set, several indices including, box loss, objectness loss, classification loss, precision, recall and mean average precision (mAP) in terms of epoch number, are also reported in the experiments.
The automation of motorcycle equipment allows users to control equipment more safely and conveniently or use the APP to view information, such as sensors controlling spin pedal switches and mobile phones to view water tank data. This topic allows users to understand the condition of the motorcycle more quickly. This topic uses ARDUINO UNO+ESP8266 as the development platform and is composed of several sensors and a servo motor: it can sense gravity to control the opening and closing of the pedal, detect the function of the water level sensor of the water tank, and check various information at any time through 5G from the APP, To learn more about the health of the car, if there is any unusual information, immediately remind it through the mobile phone, and go to the car dealer for inspection as soon as possible to avoid the situation that the machine cannot be repaired.
Reporting the contents of the prescription is a time-consuming and labor-intensive task for pharmacists, and it is easy to make mistakes in this kind of work that requires repeated confirmation under the time pressure of customers. Because medicines usually have an impact on the human body, if the wrong medicine is accidentally provided to the customer, it may even lead to death. Therefore, in our work, we train a bidirectional LSTM-CRF network combined with an attention mechanism to perform the NER task on prescription notes and extract key prescription declaration information. The semi-automatic labeling module was implemented using a rule-based approach, and a total of 636 different prescription notes were trained and tested, including hospitals, clinics, and medical centers. The identification accuracy rate is more than 90 % in each field of declaration information. Provides a UI interface for subsequent identification to facilitate pharmacists to modify the identification content and declare key information.
Metauniverse is a post real world, a multi-user environment combined with physical reality, and a permanent and lasting digital virtual. It is based on the integration of various technologies that can interact with virtual environments, digital objects and people (such as virtual reality (VR) and augmented reality (AR)). Therefore, the meta universe is a network composed of social, networked and immersive environments, which are connected in a lasting multi-user platform. It realizes seamless user communication with real-time and dynamic interaction of digital artifacts. The educational meta universe also provides different possibilities for teacher training, and provides teachers with a virtual training environment that is not limited by time and space. This paper mainly uses the methods of experimental analysis and questionnaire survey to study the design of distance teacher training system based on virtual reality. It aims to tap the advantages of VR, reduce problems in teacher training, and further realize VR based remote training. The design and implementation of the system become possible. The survey results show that when 1100 people access at the same time, the response time is 3.81 seconds, and the server utilization of the system continues to rise with the increase of the number of concurrent users; Most interviewees expressed satisfaction with the design of the teacher remote training system.
In this article, a robust fuzzy RBF neural network sliding-mode control with actor-critic for a class of robot systems. Trajectory tracking control of robotic systems has favorable performance for tracking control. The fuzzy RBF neural network sliding-mode and actor-critic method is handled to compensate the uncertainty and disturbance of system. The stability analysis is based on for the proposed adaptive and robust control method. The simulation results show the effectiveness under the uncertainties.
