Research on Data Acquisition Algorithms Based on Image Processing and Artificial Intelligence

Hu, Shuyu

doi:10.1142/s0218001420540166

Cited by 15 publications

(8 citation statements)

References 26 publications

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“…Taking traffic security data as an example, after years of informatization construction, public security, and government departments have collected massive amounts of traffic security-related data resources, the scale and complexity of which have exceeded the management and processing capabilities of traditional databases and software technologies. Since the Internet of Things technology has been promoted, big data is everywhere in daily life, and the scope of application continues to expand [ 9 , 10 ]. With the emergence of the scale of big data, it is urgently required to classify and process big data.…”

Section: New Development Methods Of Artificial Intelligence Algorithm...mentioning

confidence: 99%

New Progress in Artificial Intelligence Algorithm Research Based on Big Data Processing of IOT Systems on Intelligent Production Lines

Cao

et al. 2022

Computational Intelligence and Neuroscience

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Intelligent production line is the abbreviation of intelligent production line. Intelligent production line refers to a form of production organization that uses intelligent manufacturing technology to realize the production process of products. The actual manufacturing process includes different levels and links, and each step cooperates to create a high-efficiency production line. The intelligent production line includes 3 levels covering automation equipment, digital workshops, and intelligent factories and runs through 6 major links of intelligent manufacturing (intelligent management, intelligent monitoring, intelligent processing, intelligent assembly, intelligent inspection, and intelligent logistics). The emergence of the Internet of Things system has changed the way of information dissemination. The system combines radio frequency automatic identification and global positioning system technologies to achieve functions such as information exchange and processing, enabling information processing to be intelligent and improving resource utilization. Big data processing includes multiple data processing procedures, but data quality is the most important link in the entire process, and each data processing link will have an impact on the quality of big data. The big data processing process mainly includes data collection, data preprocessing, data storage, data processing and analysis, data display, data visualization, data application, and other links. This article aims to study the new progress of artificial intelligence algorithms for big data processing of IOT systems on intelligent production lines. It is hoped that through the development of intelligent production lines and big data processing technologies, ways to optimize artificial intelligence algorithms can be found. This study proposes a metadata replication method based on a separate replication strategy, which separates the replication process of the data operation log, each is independent, and shortens the data replication time. Combining the existing intelligent production line network platform in the laboratory and carrying out the research of the intelligent production line network state prediction system based on the neural network to design a network prediction system can prejudge the operation status of the intelligent production line network. The experimental results in this article show that when the Namenode mode is used to read data and when the number of clients reaches 8, the data processing basically remains unchanged. When the NCluster system reads data and when the number of clients is 6, the data is processed 1256. When the number of clients is 20, the data is processed 2100, the NCluster system will remain stable when the number of clients reaches 12, and compared with the Namenode system, it has obvious advantages.

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Section: New Development Methods Of Artificial Intelligence Algorithm...mentioning

confidence: 99%

New Progress in Artificial Intelligence Algorithm Research Based on Big Data Processing of IOT Systems on Intelligent Production Lines

Cao

et al. 2022

Computational Intelligence and Neuroscience

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“…Assume a simple supply chain [11], namely a manufacturer and a retailer. Manufacturers only sell products to retailers in this supply chain, assuming that the demand of the end customer is the F t variable.…”

Section: Modeling and Derivation Of The Bullwhip Effectmentioning

confidence: 99%

“…In formula (11), L k represents the lead time for the kth level member in the supply chain to place an order with the k + 1th level member and has the following formula:…”

Section: Information Sharing Value Analysismentioning

confidence: 99%

Legal Protection of Artificial Intelligence Data and Algorithms from the Perspective of Internet of Things Resource Sharing

Wang

2021

Wireless Communications and Mobile Computing

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There are few laws and regulations related to privacy protection in the existing artificial intelligence data sharing environment, lack of practical operability, and low feasibility. The weakening of administrative management and industry self-discipline also reflects my country’s current weak protection of big data privacy. In order to solve the problem of sharing artificial intelligence data and algorithms, it becomes very important to study the legal protection of artificial intelligence data and algorithms from the perspective of Internet of Things resource sharing. This article is aimed at studying the use of laws to protect artificial intelligence data and algorithms. Aiming at reducing the bullwhip effect, a most effective bullwhip effect model derivation algorithm is proposed. This method can not only share customer demand information with members at all levels in the supply chain but also achieve information sharing among members at all levels. Calculate the proportion of the overall time of the program through multiple statistical data ( m = 30 , k = 12 ; and m = 60 , k = 15 ), and extract two special values representing the overall situation ( m = 30 , k = 12 ; m = 60 , k = 15 ). Most of the time consumption of this program is concentrated in the secret distribution stage, accounting for about 80% on average.

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“…Since then, after several years of research by a large number of researchers, the structure of artificial neural networks has been developed from several layers, dozens of layers in the early years to hundreds of layers. Models such as A1exNet [8], VGG [9], ResNet [10], DenseNet [11], and DPN (digital packet network) [12] are all classical network models appearing in this development process. In addition to these classical structural improvements, there are also the proposed Dropout and Dropconnect [11] regularization methods and the proposed trainable activation function structure maxout [12].…”

Section: Related Workmentioning

confidence: 99%

Visual Information Computing and Processing Model Based on Artificial Neural Network

Wang

Liu

2022

Computational Intelligence and Neuroscience

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This paper analyzes the parallel and serial information processing structure of visual system and proposes a visual information processing model with three layers: visual receptor layer, visual information conduction and relay layer, and information processing layer of visual information computing and processing area. Based on the analysis, abstraction, and simplification of the biological prototype of each layer in the visual system, a framework model of an artificial neural system corresponding to the visual system is proposed. An artificial neural network model is proposed to simulate the mechanism of visual attention. A network model is formed by introducing the saliency mask map as additional information on the benchmark network, and the selective enhancement operation is performed on the extracted features in different regions according to the mask map. The experimental results show that the visual computing processing network model can effectively improve the classification performance of the network when the appropriate saliency mask is used. The visual information computing and processing model network can work effectively for different data sets and different structures of the benchmark network, which is a universal network model. The complexity of visual information computing and processing model network is very small, and the improvement of network performance is not at the cost of increasing model complexity, but in the way of improving network efficiency. The performance of artificial neural network visual information computation and processing model is directly related to the performance of saliency map used as mask map.

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Research on Data Acquisition Algorithms Based on Image Processing and Artificial Intelligence

Cited by 15 publications

References 26 publications

New Progress in Artificial Intelligence Algorithm Research Based on Big Data Processing of IOT Systems on Intelligent Production Lines

New Progress in Artificial Intelligence Algorithm Research Based on Big Data Processing of IOT Systems on Intelligent Production Lines

Legal Protection of Artificial Intelligence Data and Algorithms from the Perspective of Internet of Things Resource Sharing

Visual Information Computing and Processing Model Based on Artificial Neural Network

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