scholarly journals Combining Cloud Computing and Artificial Intelligence Scene Recognition in Real-time Environment Image Planning Walkable Area

2020 ◽  
Vol 5 (1) ◽  
pp. 10-17
Author(s):  
Jia-Shing Sheu ◽  
Chen-Yin Han
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Object Detection ◽  
Real Time ◽  
Scene Recognition ◽  
Obstacle Detection ◽  
Cleaning Robot ◽  
Artificial Intelligence Technology ◽  
Time Utilization ◽  
Environmental Image

This study developed scene recognition and cloud computing technology for real-time environmental image-based regional planning using artificial intelligence. TensorFlow object detection functions were used for artificial intelligence technology. First, an image from the environment is transmitted to a cloud server for cloud computing, and all objects in the image are marked using a bounding box method. Obstacle detection is performed using object detection, and the associated technique algorithm is used to mark walkable areas and relative coordinates. The results of this study provide a machine vision application combined with cloud computing and artificial intelligence scene recognition that can be used to complete walking space activities planned by a cleaning robot or unmanned vehicle through real-time utilization of images from the environment.

scholarly journals An Edge Computing-Enabled Train Obstacle Detection Method Based on YOLOv3

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Song Li ◽  
Hongli Zhao ◽  
Jinmin Ma
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Detection Method ◽  
Obstacle Detection ◽  
Edge Computing ◽  
Traffic Load ◽  
Learning Tasks ◽  
Onboard Equipment

Rail transit is developing towards intelligence which takes lots of computation resource to perform deep learning tasks. Among these tasks, object detection is the most widely used, like track obstacle detection, catenary wear, and defect detection and looseness detection of train wheel bolts. But the limited computation capability of the train onboard equipment prevents running deep and complex detection networks. The limited computation capability of the train onboard equipment prevents conducting complex deep learning tasks. Cloud computing is widely utilized to make up for the insufficient onboard computation capability. However, the traditional cloud computing architecture will bring in uncertain heavy traffic load and cause high transmission delay, which makes it fail to complete real-time computing intensive tasks. As an extension of cloud computing, edge computing (EC) can reduce the pressure of cloud nodes by offloading workloads to edge nodes. In this paper, we propose an edge computing-based method. The onboard equipment on a fast-moving train is responsible for acquiring real-time images and completing a small part of the inference task. Edge computing is used to help execute the object detection algorithm on the trackside and carry most of the computing power. YOLOv3 is selected as the object detection model, since it can balance between the real-time and accurate performance on object detection compared with two-stage models. To save onboard equipment computation resources and realize the edge-train cooperative interface, we propose a model segmentation method based on the existing YOLOv3 model. We implement the cooperative inference scheme in real experiments and find that the proposed EC-based object detection method can accomplish real-time object detection tasks with little onboard computation resources.

Artificial intelligence model for real-time monitoring of ideological and political teaching system

2020 ◽  
pp. 1-10
Author(s):  
Ying Luo
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Problem Behaviors ◽  
Control Experiment ◽  
Real Time Monitoring ◽  
Model Function ◽  
Monitoring Equipment ◽  
Information Monitoring ◽  
Function Modules ◽  
Artificial Intelligence Technology

In ideological and political teaching, students have more serious problem behaviors in the classroom, including distracted, dazed, inattentive, and sleeping. In order to improve the efficiency of ideological and political teaching, based on artificial intelligence technology, this paper constructs a real-time monitoring system for ideological and political classrooms based on artificial intelligence algorithms, and builds model function modules according to the actual needs of ideological and political teaching monitoring. Moreover, this study makes reasonable calculations on the information monitoring and information transmission parts and installs a different number of monitoring equipment in different fixed locations according to the needs of signal monitoring. In addition, this paper designs a control experiment to study the system performance and verify the parameters from multiple aspects. The research results show that the system model constructed in this paper is stable in ideological and political teaching and has certain effects.

scholarly journals Efficient Multi-Object Detection and Smart Navigation Using Artificial Intelligence for Visually Impaired People

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 941
Author(s):  
Rakesh Chandra Joshi ◽  
Saumya Yadav ◽  
Malay Kishore Dutta ◽  
Carlos M. Travieso-Gonzalez
Keyword(s):  
Artificial Intelligence ◽  
Object Detection ◽  
Real Time ◽  
Visually Impaired ◽  
Auditory Information ◽  
Visually Impaired People ◽  
Video Frames ◽  
Average Accuracy ◽  
Impaired People ◽  
Impaired Person

Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person. Training images are augmented and manually annotated to bring more robustness to the trained model. In addition to computer vision-based techniques for object recognition, a distance-measuring sensor is integrated to make the device more comprehensive by recognizing obstacles while navigating from one place to another. The auditory information that is conveyed to the user after scene segmentation and obstacle identification is optimized to obtain more information in less time for faster processing of video frames. The average accuracy of this proposed method is 95.19% and 99.69% for object detection and recognition, respectively. The time complexity is low, allowing a user to perceive the surrounding scene in real time.

Application of artificial intelligence in university sports risk recognition and identification

2020 ◽  
pp. 1-12
Author(s):  
Ju-An Wang ◽  
Shen Liu ◽  
Xiping Zhang
Keyword(s):  
Artificial Intelligence ◽  
Risk Factors ◽  
Real Time ◽  
Risk Mitigation ◽  
Time Data ◽  
Teaching Preparation ◽  
Data Store ◽  
Teacher Factors ◽  
Risk Sources ◽  
Artificial Intelligence Technology

This article is based on artificial intelligence technology to recognize and identify risks in college sport. The application of motion recognition technology first need to collect the source data, store the collected data in the server database, collect the learner’s real-time data and return it to the database to achieve the purpose of real-time monitoring. It is found that in the identification of risk sources of sports courses, there are a total of 4 first-level risk factors, namely teacher factors, student factors, environmental factors, and school management factors, and a total of 15 second-level risk factors, which are teaching preparation, teaching process, and teaching effect. When the frequency of teaching risks is low, the consequence loss is small. When the frequency of teaching risks is low, the consequences are very serious. Risk mitigation is the main measure to reduce the occurrence of teaching risks and reduce the consequences of losses.

Artificial intelligence based on effectiveness of inverted classroom teaching of college sports

2020 ◽  
pp. 1-11
Author(s):  
Jianqin Cheng ◽  
Xiaomeng Wang
Keyword(s):  
Artificial Intelligence ◽  
Physical Education ◽  
Real Time ◽  
Visual Analysis ◽  
Analytical Framework ◽  
Colleges And Universities ◽  
Classroom Teaching ◽  
Teaching Behaviors ◽  
Inverted Classroom ◽  
Artificial Intelligence Technology

This study takes the effectiveness analysis of inverted classroom teaching in colleges and universities as a breakthrough point, and combines artificial intelligence technology with the analysis method of inverted classroom teaching in colleges and universities to enrich the existing methods for analyzing, the behavior of inverted classroom teaching in colleges and universities to realize the effectiveness of inverted classroom teaching in colleges and universities analysis. This research first constructs an analytical framework for the teaching behaviors of college physical education inverted classrooms based on artificial intelligence technology, which consists of observation dimension and the evaluation dimension. In order to further test the scientifically and operability of the analytical framework, taking emotion recognition as an example, practical operations are combined with specific examples to obtain visual analysis results. This study expands the dimension and depth of analysis of the behavior of inverted sport in classroom teaching in sport inversion colleges and universities, and has obvious advantages in saving manpower and real-time visual display. Through the analysis of the effectiveness of physical education inverted classroom teaching in sports inversion colleges and universities through artificial intelligence technology, the use of technology to participate in the analysis of physical education inverted classroom teaching behaviors in sports inverted colleges and universities, shorten the evaluation time, expand the evaluation dimension, improve the evaluation efficiency, achieve real-time feedback, real-time attention to classroom effects. Effectively regulating the inverted classroom teaching behavior of college physical education can promote the cultivation of teachers’ professional abilities, scientifically and accurately improve and correct teaching problems, and improve the quality of education and teaching. Eventually, students will achieve comprehensive self-evaluation of students, and promote personalized and standardized growth of students.

Stage performance layout design based on artificial intelligence technology

2021 ◽  
pp. 1-7
Author(s):  
Gelang Li
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Directed Graph ◽  
Design Method ◽  
Layout Design ◽  
Motion Path ◽  
Filtering Algorithm ◽  
Artificial Intelligence Technology ◽  
Stage Performance ◽  
Target Path

The main purpose of stage performance arrangement is to improve the audience’s satisfaction with the stage performance. For this purpose, the stage performance arrangement design method based on artificial intelligence technology was explored. In the process of stage performance action retrieval, the offline link uses the method of frame distance definition based on quaternion in artificial intelligence technology to construct the structure of motion database digraph; the real-time link determines the corresponding vertex and edge in the digraph. According to the vertices and edges in the directed graph, the performer’s trajectory was determined, and the multi-resolution filtering algorithm in artificial intelligence technology was used to extract the motion path in the performer’s trajectory. The target path of stage performance arrangement was obtained by mapping the performer’s motion path through zoom processing. Based on the goal path, by arranging the control position of the performer’s root joint and correcting the direction of the performer, the movement track of the performer in the stage performance process was reconstructed, and the stage performance layout design was realized by combining with the position arrangement of the stage performance motion segment. The experimental results show that the method can accurately plan the performers’ path and improve the user’s satisfaction with the stage performance.

College English cross-cultural teaching based on cloud computing MOOC platform and artificial intelligence

2020 ◽  
pp. 1-11
Author(s):  
Xie Huiying ◽  
Mai Qiang
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Online Teaching ◽  
Model Performance ◽  
Cross Cultural ◽  
College English ◽  
Experimental Teaching ◽  
Cultural Teaching ◽  
Artificial Intelligence Technology ◽  
Improved Model

College English cross-cultural teaching has changed from offline to online teaching. Under the impetus of MOOC teaching mode, college English cross-cultural teaching online teaching has exposed problems such as insufficient intelligence and poor online teaching effects. In order to improve the efficiency of college English cross-cultural teaching, based on cloud computing technology and artificial intelligence technology, this article improves and analyzes traditional MOOC, improves traditional algorithms according to the actual needs of MOOC teaching, and proposes a new improved model. Moreover, this article sets up functional modules through requirements analysis. In addition, according to actual teaching, this paper designs control experiments to verify and analyze the model performance from experimental teaching and the effect of attracting students’ online learning. The research results show that the model proposed in this paper has good performance and can effectively improve the efficiency of English cross-cultural teaching.

scholarly journals Research on Integrated Development Technology of Artificial Intelligence, Big Data and Cloud Computing

2021 ◽  
pp. 349-356
Author(s):  
Yu Qing
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Big Data ◽  
Development Strategy ◽  
Reference Value ◽  
The United States ◽  
Two Dimensions ◽  
Integrated Development ◽  
Development Technology ◽  
Artificial Intelligence Technology

Big data is profoundly changing our society and our way of production, life and thinking. At the same time, the development of big data continues to promote the innovation and breakthrough of artificial intelligence. Artificial intelligence is the focus of current research. All countries also raise artificial intelligence to the national strategic level and seize the commanding height of artificial intelligence. This paper analyzes the strategic characteristics of the development of artificial intelligence in the United States, Britain and Japan from the two dimensions of technology deployment and system guarantee. This paper studies the artificial intelligence technology based on big data and the development strategy of artificial intelligence, so as to provide a strategic idea for the development of artificial intelligence in China. The idea has a certain reference value for the research on the integrated development technology of artificial intelligence, big data and cloud computing.

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