Artificial Intelligence and Sensor Technology in the Automotive Industry: An Overview

2021 ◽  
pp. 145-164
Author(s):  
S. Meenakshi Ammal ◽  
M. Kathiresh ◽  
R. Neelaveni
Keyword(s):  
Artificial Intelligence ◽  
Automotive Industry ◽  
Sensor Technology

INTERCONNECTION BETWEEN GLOBAL SUPPLY CHAINS AND INDUSTRY

2021 ◽  
pp. 6-11
Author(s):  
Alexander N. Bryntsev ◽  
◽  
M.A. Bykova ◽  
Keyword(s):  
Artificial Intelligence ◽  
Supply Chains ◽  
Automotive Industry ◽  
Industrial Production ◽  
High Tech ◽  
Global Supply Chains ◽  
Global Competition ◽  
Macroeconomic Analysis ◽  
The Relationship

In the article, the authors consider the issues of the relationship between global supply chains and industrial production of semiconductors in modern conditions. Particular attention is paid to the applied value of the application of artificial intelligence technologies in industry in the light of the growth of global competition. Their specific features, strengths and weaknesses are shown. A brief macroeconomic analysis of the development of markets for robotics, the automotive industry, high-tech products, as well as modern regulations on the eve of a new technological order is given.

scholarly journals Intelligent Application of Artificial Intelligence Internet of Things Technology in the Economic and Legal Fields

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Tingting Tan
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Criminal History ◽  
Fund Raising ◽  
Economic Crime ◽  
Internet Technology ◽  
Sensor Technology ◽  
Crime Intelligence ◽  
Internet Of Things Technology ◽  
Economic Crimes

In today’s globalized situation, people on the one hand enjoy the great convenience brought by the Internet and artificial intelligence Internet of Things (IoT) technology, and, on the other hand, they are also inevitably subject to a series of harms brought by network technology. Internet economic crime is a new type of crime based on Internet technology. Criminals use Internet technology to conduct illegal visits and Trojan horse program attacks, steal user information, and defraud victims of money. This has resulted in the people’s personal and property safety and social harmony and stability. Strictly cracking down on cyber economic crimes in accordance with the law is of great significance to safeguarding the interests of the people and maintaining social stability. However, as the methods and forms of cyber economic crimes emerge endlessly, it is very important to collect intelligence information on such crimes. This paper proposes using the sensor technology, embedded system technology, radio frequency automatic identification technology, and cloud computing technology in artificial intelligence Internet of Things technology to design and build a data-mining-based network economic crime intelligent information aggregation collection system to realize network economic crime intelligence of aggregation and analyze and help combat cyber economic crimes. This article takes cyber economic crime cases in various cities in our province as an example, selects 9 cyber economic criminals’ intelligence information as sample data, and tests and applies the designed cyber economic crime intelligence information system. The final results show that the numbers of cyber economic crime cases in four cities A, B, C, and D in four provinces are roughly the same, but city A has the largest number; the minimum confidence of the 9 criminals is above 0.60, indicating that the economic crimes of cyber economic criminals are related to their academic background and family status and criminal history are related to a certain extent; illegal fund-raising fraud and online credit card fraud account for the largest proportion of the four cities and are currently the main forms of online economic crime.

scholarly journals Micro-work, artificial intelligence and the automotive industry

2019 ◽  
Vol 46 (3) ◽  
pp. 333-345 ◽  
Author(s):  
Paola Tubaro ◽  
Antonio A. Casilli
Keyword(s):  
Artificial Intelligence ◽  
Automotive Industry

scholarly journals Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning

Fermentation ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. 104
Author(s):  
Claudia Gonzalez Viejo ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Electronic Nose ◽  
Near Infrared ◽  
Raw Materials ◽  
Low Cost ◽  
Consumer Preference ◽  
Sensor Technology ◽  
Type Model ◽  
Data Prediction

Beer quality is a difficult concept to describe and assess by physicochemical and sensory analysis due to the complexity of beer appreciation and acceptability by consumers, which can be dynamic and related to changes in climate affecting raw materials, consumer preference, and rising quality requirements. Artificial intelligence (AI) may offer unique capabilities based on the integration of sensor technology, robotics, and data analysis using machine learning (ML) to identify specific quality traits and process modifications to produce quality beers. This research presented the integration and implementation of AI technology based on low-cost sensor networks in the form of an electronic nose (e-nose), robotics, and ML. Results of ML showed high accuracy (97%) in the identification of fermentation type (Model 1) based on e-nose data; prediction of consumer acceptability from near-infrared (Model 2; R = 0.90) and e-nose data (Model 3; R = 0.95), and physicochemical and colorimetry of beers from e-nose data. The use of the RoboBEER coupled with the e-nose and AI could be used by brewers to assess the fermentation process, quality of beers, detection of faults, traceability, and authentication purposes in an affordable, user-friendly, and accurate manner.

Developing a sensor-based learning concentration detection system

2014 ◽  
Vol 31 (2) ◽  
pp. 216-230 ◽  
Author(s):  
Yen-Ning Su ◽  
Chia-Cheng Hsu ◽  
Hsin-Chin Chen ◽  
Kuo-Kuang Huang ◽  
Yueh-Min Huang
Keyword(s):  
Artificial Intelligence ◽  
Learning Environment ◽  
Detection Method ◽  
Detection System ◽  
Random Search ◽  
Sensor Technology ◽  
Content Type ◽  
Abc Algorithm ◽  
Sensing Technology ◽  
Fitness Value

Purpose – This study aims to use sensing technology to observe the learning status of learners in a teaching and learning environment. In a general instruction environment, teachers often encounter some teaching problems. These are frequently related to the fact that the teacher cannot clearly know the learning status of students, such as their degree of learning concentration and capacity to absorb knowledge. In order to deal with this situation, this study uses a learning concentration detection system (LCDS), combining sensor technology and an artificial intelligence method, to better understand the learning concentration of students in a learning environment. Design/methodology/approach – The proposed system uses sensing technology to collect information about the learning behavior of the students, analyzes their concentration levels, and applies an artificial intelligence method to combine this information for use by the teacher. This system includes a pressure detection sensor and facial detection sensor to detect facial expressions, eye activities and body movements. The system utilizes an artificial bee colony (ABC) algorithm to optimize the system performance to help teachers immediately understand the degree of concentration and learning status of their students. Based on this, instructors can give appropriate guidance to several unfocused students at the same time. Findings – The fitness value and computation time were used to evaluate the LCDS. Comparing the results of the proposed ABC algorithm with those from the random search method, the algorithm was found to obtain better solutions. The experimental results demonstrate that the ABC algorithm can quickly obtain near optimal solutions within a reasonable time. Originality/value – A learning concentration detection method of integrating context-aware technologies and an ABC algorithm is presented in this paper. Using this learning concentration detection method, teachers can keep abreast of their students' learning status in a teaching environment and thus provide more appropriate instruction.

scholarly journals Ethical Review in the Age of Artificial Intelligence

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
John Heo
Keyword(s):  
Artificial Intelligence ◽  
Automotive Industry ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Ethical Review ◽  
Benefit Analysis ◽  
The Public ◽  
Intelligent Machines ◽  
Domain Specific ◽  
The Subject

Artificial intelligence (AI), particularly machine learning, has made significant strides in the past decade. Due to the widely applicable nature of this technology, the emergence of increasingly intelligent machines is poised to transform today’s society. Recently, the rate of AI development has aroused significant concerns due to the lack of guiding policy and regulation. Thus, it is integral for the public to recognize the technology and make informed choices regarding the future of AI. This paper serves to acquaint the layperson and other stakeholders involved in AI development with the current progress of AI and the ethical concerns that must be addressed before significant advancements. The subject of discussion is narrowed down to three fields of AI’s most prominent use: (1) the internet; (2) the automotive industry; and (3) the healthcare industry. For each sector, the foundation of the domain-specific AI technique is introduced, the benefits and ethical ramifications are discussed, and a final cost-benefit analysis is provided.

scholarly journals Development of a system for analyzing and unloading road traffic using artificial intelligence

2021 ◽  
Vol 2094 (3) ◽  
pp. 032036
Author(s):  
L M Akhmetov ◽  
D I Bikov ◽  
M R Khamidullin ◽  
G A Gareeva ◽  
G K Gabdullina
Keyword(s):  
Artificial Intelligence ◽  
Growth Rate ◽  
Automotive Industry ◽  
Road Traffic ◽  
Traffic Light ◽  
Traffic Lights ◽  
Arduino Uno ◽  
Smart Traffic

Abstract Every year, the growth rate of cars in Russia will continue to grow, which makes it extremely difficult to organize road traffic. To solve this problem, innovations in the automotive industry, such as smart traffic lights, are needed to more effectively regulate traffic on public roads. Therefore, an urgent issue is the development of a system for analyzing and unloading road traffic to improve the situation at intersections and subsequent automation through the introduction of artificial intelligence. The Arduino Uno was chosen as the layout for the organization of a smart traffic light.

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