Computational Collective Intelligence

Steelmaking is one of the most energy-intensive industries, so improving control efficiency helps to reduce the energy used to produce a tonne of steel. Mutual influences between the phases of an electric arc furnace in available electrode movement control systems cause unproductive electrode movements as a reaction to the redistribution of currents among the phases of a three-phase power supply system due to changes in arc length in one of the phases. The nonlinearity of the characteristics of an electric arc furnace significantly complicates the ability to provide autonomous electrode movement control. The approach proposed in this paper, based on the formation of a matrix of mutual influences with variable coefficients, significantly improves the per-phase autonomy of the electrode movement control system. Nonlinear dependences of the mutual influence coefficients as a function of the current increment in the phase in which the disturbance occurred are obtained. Thus, it is possible to practically eliminate unproductive electrode movements in existing control systems by avoiding the traditional use of a dead zone, which reduces the control quality in the zone of small disturbances. The complex of experiments performed using the mathematical model demonstrate that the mutual influence improves the dynamic properties of the electrode movement system in certain operating modes.

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A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Lin

Dai

et al. 2020

Sensors

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One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.

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Computational Collective Intelligence

Cited by 5 publications

References 9 publications

Spam Detection Over Call Transcript Using Deep Learning

Spam Detection Over Call Transcript Using Deep Learning

Increasing the Level of Autonomy of Control of the Electric Arc Furnace by Weakening Interphase Interactions

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

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