scholarly journals Camera-Driven Probabilistic Algorithm for Multi-Elevator Systems

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6161
Author(s):  
Yerzhigit Bapin ◽  
Kanat Alimanov ◽  
Vasilios Zarikas
Keyword(s):  
Image Processing ◽  
Control Algorithm ◽  
State Of The Art ◽  
Control Strategies ◽  
Processing System ◽  
Probabilistic Algorithm ◽  
Image Processing System ◽  
Traffic Conditions ◽  
Average Travel Time ◽  
Novel Method

A fast and reliable vertical transportation system is an important component of modern office buildings. Optimization of elevator control strategies can be easily done using the state-of-the-art artificial intelligence (AI) algorithms. This study presents a novel method for optimal dispatching of conventional passenger elevators using the information obtained by surveillance cameras. It is assumed that a real-time video is processed by an image processing system that determines the number of passengers and items waiting for an elevator car in hallways and riding the lifts. It is supposed that these numbers are also associated with a given uncertainly probability. The efficiency of our novel elevator control algorithm is achieved not only by the probabilistic utilization of the number of people and/or items waiting but also from the demand to exhaustively serve a crowded floor, directing to it as many elevators as there are available and filling them up to the maximum allowed weight. The proposed algorithm takes into account the uncertainty that can take place due to inaccuracy of the image processing system, introducing the concept of effective number of people and items using Bayesian networks. The aim is to reduce the waiting time. According to the simulation results, the implementation of the proposed algorithm resulted in reduction of the passenger journey time. The proposed approach was tested on a 10-storey office building with five elevator cars and traffic size and intensity varying from 10 to 300 and 0.01 to 3, respectively. The results showed that, for the interfloor traffic conditions, the average travel time for scenarios with varying traffic size and intensity improved by 39.94% and 19.53%, respectively.

scholarly journals Distributed Network Image Processing System and Transmission Control Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Pu Zaiyi ◽  
Yang Kun ◽  
Wang Chaobin
Keyword(s):  
Image Processing ◽  
Control Algorithm ◽  
Processing System ◽  
Image Data ◽  
Image Feature ◽  
Distributed Network ◽  
Transmission Control ◽  
Image Processing System ◽  
Original Algorithm ◽  
Reconstruction Performance

With the increasing use of Internet technologies, image data is spreading more and more on the Internet. Whether it is a social network or a search engine, a large amount of image data is generated. By studying the distributed network image processing system and transmission control algorithm, this paper proposes a more accurate gradient calculation method based on the SIFT algorithm. It is concluded that the performance of the proposed algorithm is slightly better than that of the original algorithm, so the system is implemented. On the basis of reducing the performance of the original algorithm, the dimension of the image features is effectively reduced. By comparing the influence of the image retrieval system in the single-machine environment and the distributed environment on the image feature extraction rate, it is proved that the system uses five distributed nodes to construct the image transmission system that achieves the best results in terms of machine cost and system performance. The random Gaussian orthogonal matrix is analyzed with good stability and performance. The OMP algorithm has good convergence and reconstruction performance. The MH-BCS-SPL reconstruction algorithm works best, and the PSNR decreases very smoothly in the process of increasing the packet loss rate from 0.1 to 0.6. The experimental results show that different orthogonal bases behave differently under different images. Overall, the BCS-SPL series algorithm has greatly improved the reconstruction effect compared with the traditional OMP algorithm.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

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