Design and Implementation of a Video/Voice Process System for Recognizing Vehicle Parts Based on Artificial Intelligence

Abstract

With the recent development of artificial intelligence along with information and communications infrastructure, a new paradigm of online services is being developed. Whereas in the past a service system could only exchange information of the service provider at the request of the user, information can now be provided by automatically analyzing a particular need, even without a direct user request. This also holds for online platforms of used-vehicle sales. In the past, consumers needed to inconveniently determine and classify the quality of information through static data provided by service and information providers. As a result, this service field has been harmful to consumers owing to such problems as false sales, fraud, and exaggerated advertising. Despite significant efforts of platform providers, there are limited human resources for censoring the vast amounts of data uploaded by sellers. Therefore, in this study, an algorithm called YOLOv3+MSSIM Type 2 for automatically censoring the data of used-vehicle sales on an online platform was developed. To this end, an artificial intelligence system that can automatically analyze an object in a vehicle video uploaded by a seller, and an artificial intelligence system that can filter the vehicle-specific terms and profanity from the seller’s video presentation, were also developed. As a result of evaluating the developed system, the average execution speed of the proposed YOLOv3+MSSIM Type 2 algorithm was 78.6 ms faster than that of the pure YOLOv3 algorithm, and the average frame rate per second was improved by 40.22 fps. In addition, the average GPU utilization rate was improved by 23.05%, proving the efficiency.

[Retracted] Mobile Communication Network Optimization System Based on Artificial Intelligence

“…The process of minimizing E can be regarded as an optimization problem. The steepest descent method is adopted to adjust the weight matrix, namely, as shown in formulas (4) and (5).…”

“…These types of software all focus on completing certain functions, but lack the thinking mode of "why do you do this?" Due to the "mobile" characteristics of the mobile communication network, the structure and operation process of the whole network are very complex, and problems arising in the maintenance process also involve many aspects, requiring a large number of personnel with specialized knowledge to carry out maintenance and optimization [4,5].…”

Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory

“…Recent developments in computing hardware (e.g., graphics processing units (GPUs) and tensor processing units (TPUs) [ 1 ]) have enabled large scale parallel processing, resulting in a substantial reduction in the inference/training time for deep learning on PC/server platforms. As hardware performance improvements have made neural network models deeper and wider, the deep learning model has outperformed humans in various fields such as computer vision, natural language processing, and audio classification [ 2 , 3 , 4 , 5 , 6 ]. Many recent studies have used the superior performance of deep learning algorithms, which normally run on PC/server platforms, for their deployment in mobile devices [ 7 , 8 , 9 , 10 , 11 ].…”