scholarly journals Research and Application of Artificial Intelligence in the Field of Vision System and Network

2019 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Vision System ◽  
Field Of Vision

scholarly journals Application of Artificial Intelligence in Food Industry—a Guideline

2021 ◽  
Author(s):  
Nidhi Rajesh Mavani ◽  
Jarinah Mohd Ali ◽  
Suhaili Othman ◽  
M. A. Hussain ◽  
Haslaniza Hashim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Food Industry ◽  
Near Infrared ◽  
Vision System ◽  
Electronic Tongue ◽  
World Population ◽  
Computer Vision System ◽  
The Past

AbstractArtificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

The Research and Application of Artificial Intelligence in the Field of Intelligent Robot

2014 ◽  
Vol 898 ◽  
pp. 763-766
Author(s):  
Zhi Hao Li
Keyword(s):  
Artificial Intelligence ◽  
Environmental Changes ◽  
Vision System ◽  
Mineral Exploration ◽  
Intelligent Robot ◽  
Application Range ◽  
Intelligent Robots ◽  
External Data ◽  
Wide Range ◽  
Artificial Intelligence Application

The research and application of artificial intelligence has a very wide range in intelligent robot field. Intelligent robot can not only make use of artificial intelligence gain access to external data, information, (such as stereo vision system, face recognition and tracking, etc.), and then deal with it so as to exactly describe external environment, and complete a task independently, owing the ability of learning knowledge, but also have self-many kinds of artificial intelligence like judgment and decision making, processing capacity and so on. It can make corresponding decision according to environmental changes. Its application range is expanding. In deep sea exploration, star exploration, mineral exploration, heavy pollution, domestic service, entertainment clubs, health care and so on, the figure of intelligent robots artificial intelligence application can all be seen.

scholarly journals Propuesta de servicio de supervisión remoto en cultivos del sector vinícola

2020 ◽  
pp. 5-12
Author(s):  
Ricardo JARA-RUIZ ◽  
Ignacio Alejandro MONTES-GARCÍA ◽  
Néstor David FELICIANO-VELÁZQUEZ ◽  
Marcos Emmanuel QUEZADA-MUÑOZ
Keyword(s):  
Artificial Intelligence ◽  
Communication Network ◽  
Information And Communication Technologies ◽  
National Economy ◽  
Mobile Application ◽  
Vision System ◽  
Communication Technologies ◽  
Information And Communication ◽  
Wine Sector ◽  
Technological Impact

This research paper presents and structures a proposal for agro-industrial application using Unmanned Air Vehicles (UANs) or Drones in the wine sector, which proposes the use of information and communication technologies (ICTs) in synergy with advances in artificial intelligence. For development, the design and structuring of a remote service is considered that allows the user or owner to generate a request for wine monitoring through a communication network through a mobile application considering the use of a drone equipped with a vision system that allows to identify patterns and consequently detect characteristics in specific that have a high potential to pose an obvious risk; this with an approach that provides the user or producer with the information necessary for making effective decisions in the process of assisting the problems. All with the aim of developing tools that contribute and work for new contributions that generate a technological impact in the context of the sector considered important in the national economy.

scholarly journals Role of Artificial Intelligence in Cyber Security

2019 ◽  
Vol 9 (1) ◽  
pp. 4628-4630
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Vision System ◽  
Human Life ◽  
News Item ◽  
Security Measures ◽  
Daily News ◽  
Cyber Crimes ◽  
Financial Transactions

Artificial Intelligence (AI) is a buzz word in the cyber world. It is still a developing science in multiple facets according to the challenges thrown by 21st century. Use of AI has become inseparable from human life. In this day and age one cannot imagine a world without AI as it has much significant impact on human life. The main objective of AI is to develop the technology based activities which represents the human knowledge in order to solve problems. Simply AI is study of how an individual think, work, learn and decide in any scenario of life, whether it may be related to problem solving or learning new things or thinking rationally or to arrive at a solution etc. AI is in every area of human life, naming a few it is into gaming, language processing, speech recognition, expert system, vision system, hand writing recognition, intelligence robots, financial transactions and what not, every activity of human life has become a subset of AI. In spite of numerous uses, AI can also used for destroying the human life, that is the reason human inference is required to monitor the AI activities. Cyber crimes has become quite common and become a daily news item. It is not just a problem faced in one country, it is across the world. Without strong security measures, AI is meaningless as it can be easily accessible by others. It has become a big threat for governments, banks, multinational companies through online attacks by hackers. Lot of individual and organizational data is exploited by hackers and it becomes a big threat to the cyber world. In this connection research in the area of AI and cyber security has gained more importance in the recent times and it is ever lasting also as it is a dynamic and sensitive issue linked to human life.

Prediction of pork loin quality using online computer vision system and artificial intelligence model

Meat Science ◽  
2018 ◽  
Vol 140 ◽  
pp. 72-77 ◽  
Author(s):  
Xin Sun ◽  
Jennifer Young ◽  
Jeng-Hung Liu ◽  
David Newman
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Pork Loin

scholarly journals Use of artificial intelligence-based computer vision system to practice social distancing in hospitals to prevent transmission of COVID-19

2020 ◽  
Vol 45 (3) ◽  
pp. 379 ◽  
Author(s):  
Vathsala Patil ◽  
BM Zeeshan Hameed ◽  
DasharathrajK Shetty ◽  
Nithesh Naik ◽  
Nikhil Nagaraj ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Social Distancing

scholarly journals PSXI-2 A computer vision system for feed bunk management in beef cattle feedlot

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 389-390
Author(s):  
João R R Dorea ◽  
Sek Cheong
Keyword(s):  
Artificial Intelligence ◽  
Beef Cattle ◽  
Real Time ◽  
Vision System ◽  
Management Practice ◽  
Computing System ◽  
Intelligence System ◽  
Cloud Computing System ◽  
Model Training ◽  
Cattle Feedlot

Abstract Feed bunk scoring is a common management practice in feedlots. Usually, the bunk score is assigned visually by a trained person. However, the subjectivity of bunk scoring and inconsistency across bunk readers can result in excessive variation on feed delivery. Such variation can result on feed waste, sub-optimal animal performance, and increased incidence of metabolic disorders. The objective of this study was to develop an artificial intelligence system to perform bunk management in real-time. RGB-cameras were installed above the feed bunk in a commercial feedlot, and a total of 4,280 images were acquired, together with visual bunk scores with four categories: empty (no feed remaining), low (scattered feed remaining), medium (30–50% of feed remaining), and full (> 50% of feed remaining). Cattle behavior at the feed bunk was also classified into four classes: empty (no cattle at the feed bunk); low (< 30% bunk occupied); medium (30–70% feed bunk occupied); full (above 70% feed bunk occupied). The labeled images were then used for model training and a new set of 105 images were used for validation. A deep neural network (DNN) called ResNet was implemented to generate the predictions using a transfer learning with weights from the ImageNet dataset. A cloud computing system was developed to acquire, process and store images every 15 minutes, and implement real-time predictions of bunk score and cattle behavior. Prediction accuracies across bank score categories were: 81.8% (empty), 82.4% (low), 88.8% (medium), and 90% (full). For cattle behavior, accuracies were: 83.7% (empty), 66.6% (low), 71.4% (medium), and 86.6% (full). Combining feed bunk score and cattle behavior can provide an important decision-making tool to improve nutritional management in beef cattle feedlot. The use of artificial intelligence can allow the development of fully automated real-time systems to enhance livestock operations.

scholarly journals Learning to See the Hidden Part of the Vehicle in the Autopilot Scene

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 331 ◽  
Author(s):  
Yifeng Xu ◽  
Huigang Wang ◽  
Xing Liu ◽  
Henry He ◽  
Qingyue Gu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Object Detection ◽  
Vision System ◽  
Image Inpainting ◽  
Semantic Segmentation ◽  
Fatal Crash ◽  
White Side ◽  
Original Size ◽  
High Level

Recent advances in deep learning have shown exciting promise in low-level artificial intelligence tasks such as image classification, speech recognition, object detection, and semantic segmentation, etc. Artificial intelligence has made an important contribution to autopilot, which is a complex high-level intelligence task. However, the real autopilot scene is quite complicated. The first accident of autopilot occurred in 2016. It resulted in a fatal crash where the white side of a vehicle appeared similar to a brightly lit sky. The root of the problem is that the autopilot vision system cannot identify the part of a vehicle when the part is similar to the background. A method called DIDA was first proposed based on the deep learning network to see the hidden part. DIDA cascades the following steps: object detection, scaling, image inpainting assuming a hidden part beside the car, object re-detection from inpainted image, zooming back to the original size, and setting an alarm region by comparing two detected regions. DIDA was tested in a similar scene and achieved exciting results. This method solves the aforementioned problem only by using optical signals. Additionally, the vehicle dataset captured in Xi’an, China can be used in subsequent research.

Applying Optoelectronic Devices Fusion in Machine Vision

2020 ◽  
pp. 184-213
Author(s):  
Wendy Flores-Fuentes ◽  
Moises Rivas-Lopez ◽  
Daniel Hernandez-Balbuena ◽  
Oleg Sergiyenko ◽  
Julio C. Rodríguez-Quiñonez ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Vision ◽  
Robot Navigation ◽  
Vision System ◽  
Optoelectronic Devices ◽  
Structural Monitoring ◽  
Machine Vision System ◽  
Wide Range ◽  
Spatial Coordinates ◽  
Electronic Hardware

Machine vision is supported and enhanced by optoelectronic devices, the output from a machine vision system is information about the content of the optoelectronic signal, it is the process whereby a machine, usually a digital computer and/or electronic hardware automatically processes an optoelectronic signal and reports what it means. Machine vision methods to provide spatial coordinates measurement has developed in a wide range of technologies for multiples fields of applications such as robot navigation, medical scanning, and structural monitoring. Each technology with specified properties that could be categorized as advantage and disadvantage according its utility to the application purpose. This chapter presents the application of optoelectronic devices fusion as the base for those systems with non-lineal behavior supported by artificial intelligence techniques, which require the use of information from various sensors for pattern recognition to produce an enhanced output.

Diagnostics of Surface Errors by Embedded Vision System and its Classification by Machine Learning Algorithms

2015 ◽  
Vol 669 ◽  
pp. 459-466 ◽  
Author(s):  
Kamil Židek ◽  
Alexander Hošovský ◽  
Ján Dubják
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Error Detection ◽  
Nearest Neighbor ◽  
Vision System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Vision Systems ◽  
Embedded Vision

The Article deals with usability and advantages of embedded vision systems for surface error detection and usability of advanced algorithms, technics and methods from machine learning and artificial intelligence for error classification in machine vision systems. We provide experiments with following classification algorithms: Support Vector Machines (SVM), Random Threes, Gradient Boosted Threes, K-Nearest Neighbor and Normal Bayes Classifier. Next comparison experiment was conducted with multilayer perceptron (MLP), because currently it is very popular for classification in the field of artificial intelligence. These classification approaches are compared by precision, reliability, speed of teaching and algorithm implementation difficulty.

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