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.
This study establishes an intelligent medical system for diagnosis and treatment of Chinese medicine. Temperature and heart rate were measured using a variety of Internet of things technologies, including the WeMos D1 Mini with ESP8266 integrated and thermistors, and then the resistance was learned to relate to temperature via a neural network. In addition, the AD8232 biomedical sensor and the WeMos D1 Mini were used for ECG measurements and heart rate conversion. A self-written mobile application designed with multiple units to collect data from a variety of patients. The tongue diagnosis unit can recognize the characteristics of tongue substance and coating. The pulse examination unit adopts the five layer pulse method of Yaowangmai pulse. The inquiry unit adopts the syndrome differentiation method of Dr. Shaogong Shen. These IoT data, information if the patient's tongue diagnosis, five-layer pulse examination data and various inquiry data can all be read and transmitted to the cloud. The intelligent medical system of Chinese medicine in this study can also make preliminary treatment recommendations based on a variety of patient data and provide corresponding treatment drugs
Intermediately withdrawing of insurance will damage the credit condition even the economic condition of the applicant. For an insurance company, predicting the credit behavior of each applicant is an important step in the risk control of the company, so as to avoid encountering the worst situation of the applicant's intermediate withdrawal. This study attempts to use BP network for building a model to predict the policyholder behavior. We also integrate the results into the policyholder behavior prediction system, and facilitate the internal use of insurance company employees. After all, we hope this can help users make decisions with lower risk.
This research introduces the research on children's intelligent design from the perspective of Metaverse, including the concept of Metaverse, the research status of Metaverse brand at home and abroad, This paper introduces the research on the problems to be solved, the technical route, the scheme design as well as feasibility analysis. It mainly studies the sustainability and brand marketing model of smart kidswear combined with Metaverse sales, so as to provide assistance for the development of smart kidswear happiness design under the condition of Metaverse.
With the increasing popularity of artificial intelligence, the technologies used by robots are changing rapidly. We propose a robotic system that can handle Mandarin-related problems, effectively dealing with Mandarin phonetic transcription, multiple-option reading comprehension, text error correction, similarity judgment, and keyword search. The experimental results show that using the national language assessment of grades 1–6 in primary schools, the highest score on the national language assessment is 90, and the lowest is 63, which is consistent with the actual score-provided by Shekou National Primary School for Mandarin assessment. Furthermore, more than half of the average scores in each subject are higher than the low standard (average of the bottom 25%) and even higher than the high standard (average of the top 25%). Through this system, the machine can reach the national language level of primary school students.
For Bluetooth Low Energy application, difficult setting and complicated management of timing parameters in multi-connection between master and slave nodes is obvious. For this situation, a method based on time slice was proposed for updating slot parameters to guarantee the reliability of multiple connections. It determines the value of multi-slots period and other timing parameters depending on the quantitative relationship between the minimum(maximum) connection interval of every connection and multi-slot period. In this paper, a reliable writing and reading method for multi-connection between master and slaves was designed and implemented in TI CC2640R2F platform, on which we made some performance measurements of above updating method. The experimental results show that the proposed method for updating slot parameters based on the writing and reading procedure behaves well in multiple connections for Bluetooth Low Energy.
The XXIV Olympic Winter Games in Beijing has created hot discussion in new media and social media. The Olympic Games is an important opportunity to build up the image of the country and achieve external and internal publicity. Through the analysis of the new media's public opinion big data, we can see hot spots and public emotions and the characteristics of audiences, which is in line with the mirror metaphor theory of communication and reflects the reality of social phenomena through new media.
The fire department's disaster relief center receives an average of hundreds of thousands of ambulance calls every year, among which the ambulance center dispatches and guides relevant ambulances and ambulance personnel to the scene through a wireless notification system. Ambulance personnel must continue to guide and communicate through radio during the rescue process in the fire scene. In an emergency, the clarity of radio listening information is the first requirement to strengthen the judgment of the ambulance personnel and strive to save lives in a short time. Usually the sampling rate of the general radio is 8KHz, and the disaster relief site is easy to receive a lot of noise, so it is very important to improve the sampling frequency of radio communication and noise cancellation. By integrating two deep learning models Unet+AFiLM [1] and I-DTLN into audio super-resolution and noise cancellation system, which can effectively remove noise in radio communication and increase the audio sampling rate to enhance the effect of speech recognition.
This paper analyzes the employees' job satisfaction in Shanghai Disney from four factors of work environment, work group, employee compensation and company culture. According to the result, the performance of employees' job satisfaction from high to low is company culture, work environment, work group and employee compensation in order. The positive factors of job satisfaction include great company culture, immersive work environment, Disney-themed landscaping, training and disability hiring program. However, employees show lower job satisfaction with the factors of irregular work scheduling, pay, colleague cooperation, rotating shifts, and career path. Despite of great culture, lack of belonging is an unfavorable factor for job satisfaction. Different ways to improve employees' job satisfaction are proposed in this paper, including facilitating communication and team cooperation, raising hourly worker pay and offering a career development program.
Based on the unmanned autonomous sanitation robot “Sweeper” introduced by Shanghai Sanda University, the target detection technology is studied by adopting the idea of blob analysis to improve the target detection ability and ground cleaning function of “Sweeper” with more than 300 images of campus pedestrians. After HSV thresholding binarization, image morphology processing and image contour detection fitting, erosion operation with expansion operation and different rectangular structured element adjustment, the noise reduction of the surrounding environment and the extraction of campus pedestrian contours were achieved and campus pedestrian image detection is obtained. The test shows that the system can achieve 100% success rate for single pedestrian detection in any environment, and 65%-75% for multiple pedestrian images, and the detection success rate of stable natural environment will be better than that of complex natural environment.
By analyzing the requirements for environmental monitoring and intelligence of pigpen, this paper adopts Arduino development board, wireless communication module and Blinker Internet of Things platform, combined with a variety of sensors, to build a miniature model and develop a intelligent breeding management system integrating environmental monitoring and remote control of animal feeding. Pig breeding management system can realize food and water allowance monitoring, air quality monitoring, personnel identity detection, infrared intrusion prevention and other environmental perception. The system can realize abnormal alarm, lighting control, automatic cooling treatment, automatic disinfection and other remote control. The simulation model system has the characteristics of low cost, convenient installation and good man-machine interface, and has certain practical value.
Customer satisfaction is the key of a successful modern business. So customer satisfaction prediction becomes an important mission in Business Intelligence. Based on various studies on customer satisfaction research on different types of businesses, We reviewed machine learning methods used in these papers. The results show that customer satisfaction prediction can be analysed by different typical algorithms of machine learning. Due to the difference between data formats (such as review data or survey data), different models should be recommended respectively for different application fields.
In recent years, the research of autonomous robotic arms has received high attention from academia and industry, so this thesis aims to develop a visual detection system for robotic arms to grasp and place objects. Object detection technology The purpose of which is to locate objects of interest (target) in a given image, providing object location bounding boxes and categories. ROS is a set of open-source software libraries designed to simplify the creation of complex and robust on various robotic platforms. the task of the robot behavior. The proposed scheme uses a camera with depth information and combines target localization and edge detection algorithms to accurately measure the relative distance between the object and the robot arm. The process includes deep neural network construction and use, robotic arm positioning, object bounding box position information, and robotic arm movement control. Considering the visual scale, the relative distance between the target and the robotic arm is calculated so that the robotic arm can grasp the target and place the object in a specific position. This paper introduces how to combine the deep learning object detection model and ROS moveit! to complete the object identification and grasp and place the robotic arm.
Several deep learning-based object detection techniques in medical imaging have been proposed. Chest X-rays are widely used for detecting thorax diseases due to the convenience and low radiation dose compared to Computed Tomography (CT). However, the research on rib fracture detection in chest X-rays is still inadequate. Most of the research primarily focused on frontal CXR and some on lateral CXR. No study of rib fracture detection on oblique view CXR has been previously proposed. Due to the overlapping characteristic of human ribs, the oblique view can help radiologists to recognize the fractured ribs that are blocked in the frontal view. In this paper, we employed a YOLOv5 model along with the techniques of data augmentation and image enhancement for rib fracture detection. We trained and evaluated on E-DA dataset, a private dataset collected from E-DA Cancer Hospital containing frontal and oblique chest X-rays. The developed model can detect fractured ribs in both projection views of CXR.
The cloud computing provides the more elastic organization of computing resources, and more efficiency of computing works. However, the private cloud is still necessary to various companies and institutions because of the security requirements. Therefore, these companies and institutions must manage and maintain their private clouds by themselves. The low-cost and effective private cloud maintenance alternative is an important issue to the private cloud. This paper proposes an agentless maintenance system based on the open-souce tool, Ansible. The proposed approach integrate the Ansible with the Web platform. It is unnecessary to install any software on the client machines. The maintenance system can remotely control the client machine to complete asset management, performance monitoring, Risk reporting, and so on. The proposed approach can effectively reduce the maintenance cost for the private cloud.
Smart tourism has been on the agenda since 2012 under the framework of the smart city development plan in China. After that, the Culture Administration and the Tourism Administration was emerged and become a new Ministry of Culture and Tourism. Tourism has then entrusted a mission of education and culture exchange. This paper did a literature review on the development of Chinese smart culture and tourism, pointed out the major problems of the history, and offered suggestions for future development.
