scholarly journals Design and implementation path of intelligent transportation information system based on artificial intelligence technology

2020 ◽  
Vol 2020 (13) ◽  
pp. 482-485
Author(s):  
Wen-hui Xia ◽  
Dan Zhou ◽  
Qian-yin Xia ◽  
Lan-rui Zhang
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Intelligent Transportation ◽  
Design And Implementation ◽  
Artificial Intelligence Technology

scholarly journals Research on Campus Education Information System Based on Internet of Things and Artificial Intelligence Decision

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hetiao Hong
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Original System ◽  
Functional Testing ◽  
Excellent Performance ◽  
Teachers And Students ◽  
Interface Function ◽  
Artificial Intelligence Technology ◽  
Education Platform ◽  
Better Than

Because of the different reasons between regions, the distribution of educational resources is also different, the development of each school is unbalanced, and the degree of campus education informationization is different. The complex functional structure not only does not facilitate teachers and students but also leads to many problems: the prevention and prevention of campus life safety. It is difficult to keep and use multiple cards owned by one person. Software and education platform cannot be seamlessly connected, and there are various barriers between data and data and people and data. The lack of learning materials leads to the inequality of information. There are no good feedback and solution between teachers and students. It is difficult to manage accurately with a large number of people. This study will be based on the Internet and artificial intelligence technology, to explore how to study a large (or super large), concise and efficient, and excellent performance of campus education information system; this system can meet the teachers and students no matter what year, month, and day of a large number of visits. For some problems in the process of building the system, actively optimize and refine them. After functional testing and analysis of the system, the experimental results show that the interface function of the new system is stable, the usability test is better than the feedback experience of the original system, the response time is reduced by 21.6% on average, and the overall power consumption of the system is reduced by about 1.43% on average.

scholarly journals Risk Management in the Construction of Intelligent Transportation Platform Based on Artificial Intelligence

CONVERTER ◽  
2021 ◽  
pp. 543-549
Author(s):  
Hongxi Di
Keyword(s):  
Artificial Intelligence ◽  
Risk Management ◽  
Traffic Management ◽  
Motor Vehicle ◽  
Transmission Rate ◽  
Intelligent Transportation ◽  
Existing Problems ◽  
Core Technology ◽  
Artificial Intelligence Technology ◽  
Response Measures

The smoothness of a city's traffic is one of the signs that measure the development of a city. With the advent of the era of artificial intelligence and big data, the previously bloated and blocked motor vehicle transportation system is increasingly unable to adapt to this fast-paced society. The use of artificial intelligence technology to build a brand-new intelligent transportation platform is imminent, and reasonable planning of the risks in the construction of the transportation platform can effectively increase the transmission rate and reduce the frequency of accidents. The purpose of this paper is to study the risk management in the construction of intelligent transportation platform based on artificial intelligence. This article first summarizes the basic theory of artificial intelligence, and then extends the core technology of artificial intelligence. And combined with the current situation of my country's contemporary intelligent transportation, analysis of the existing problems and shortcomings, on this basis, combined with artificial intelligence technology to research and analyze the risk management in the construction of intelligent transportation platform. This research systematically expounds the risk construction principles, model construction and risk response measures of the intelligent transportation platform. This paper uses field surveys, interviews and other research methods to research and investigate traffic risk management in a certain place. The experimental research shows that risk management in the construction of intelligent transportation platforms based on artificial intelligence has higher feasibility than traditional traffic management.

scholarly journals Container Multimodal Cooperative Transportation Management Information System Based on Artificial Intelligence Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ling Wang ◽  
Shuai Fu
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Management Information System ◽  
Management Information ◽  
Efficient Operation ◽  
Multimodal Transportation ◽  
Multimodal Transport ◽  
Artificial Intelligence Technology ◽  
Application Fields ◽  
Transport Management

Artificial intelligence is a branch of computer science, which includes natural language, intelligent processing, and professional methods. Since the birth of artificial intelligence, the technology and application fields have continued to grow, and the application fields have also continued to expand. This article aims to study the application of artificial intelligence technology in the management information system of container multimodal transportation and to provide convenient and efficient operation methods for container multimodal transportation. This paper proposes the C-means clustering method. Through the research and development of the terminal management system, it has achieved great success in automation, intelligent planning, and integrated management. At the same time, the EDI system is adopted, which mainly uses the combination of GPS and GIS information platform Internet network technology. Therefore, when evaluating the operation of the multimodal transport virtual container under the control of coproduction, the DEA method is used to operate the multimodal virtual container. The situation is analyzed and evaluated, and the multimodal transport virtual container is determined through investment. The experimental results of this article show that the artificial intelligence system achieves the most efficient multimodal transport management with the most efficient system model, combined with the leading container multimodal transport virtual enterprise, to provide the best way of the management process for the development of the multimodal transport management information system. The intact rate of container cargo during transportation is as high as 99.7%.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

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