Incorporation of fractional-order dynamics into an existing PI/PID DC motor control loop

The problem of changing the dynamics of an existing DC motor control system without the need of making internal changes is considered in the paper. In particular, this paper presents a method for incorporating fractional-order dynamics in an existing DC motor control system with internal PI or PID controller, through the addition of an external controller into the system and by tapping its original input and output signals. Experimental results based on the control of a real test plant from MATLAB/Simulink environment are presented, indicating the validity of the proposed approach.This work was partially supported by the following grants under the Slovak Grant Agency, the Slovak Research and Development Agency: VEGA 1/0552/14, VEGA 1/0729/12, VEGA 1/0497/11, VEGA 1/2578/12, and APVV-0482-11, and the European Union through the European Regional Development Fund, and the Estonian Doctoral School in Information and Communication Technology through the interdisciplinary project FOMCON

Towards a Synesthesia Laboratory: Real-time Localization and Visualization of a Sound Source for Virtual Reality Applications

In this paper, we present our findings related to the problem of localization and visualization of a sound source placed in the same room as the listener. The particular effect that we aim to investigate is called synesthesia—the act of experiencing one sense modality as another, e.g., a person may vividly experience flashes of colors when listening to a series of sounds. Towards that end, we apply a series of recently developed methods for detecting sound source in a three-dimensional space around the listener.We also apply a Kalman filter to smooth out the perceived motion. Further, we transform the audio signal into a series of visual shapes, such that the size of each shape is determined by theloudness of the sound source, and its color is determined by the dominant spectral component of the sound. The developed prototype is verified in real time. The prototype configuration is described and some initial experimental results are reported and discussed. Some ideas for further development are also presented

Towards a Synesthesia Laboratory: Real-time Localization and Visualization of a Sound Source for Virtual Reality Applications

In this paper, we present our findings related to the problem of localization and visualization of a sound source placed in the same room as the listener. The particular effect that we aim to investigate is called synesthesia—the act of experiencing one sense modality as another, e.g., a person may vividly experience flashes of colors when listening to a series of sounds. Towards that end, we apply a series of recently developed methods for detecting sound source in a three-dimensional space around the listener.We also apply a Kalman filter to smooth out the perceived motion. Further, we transform the audio signal into a series of visual shapes, such that the size of each shape is determined by theloudness of the sound source, and its color is determined by the dominant spectral component of the sound. The developed prototype is verified in real time. The prototype configuration is described and some initial experimental results are reported and discussed. Some ideas for further development are also presented

Intelligent Control and Digital Twins for Industry 4.0

One of the prominent features of the Fourth Industrial Revolution—frequently referred to as Industry 4 [...]</i

Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks

In the manuscript, the issue of detecting and segmenting out pavement defects on highway roads is addressed. Specifically, computer vision (CV) methods are developed and applied to the problem based on deep learning of convolutional neural networks (ConvNets). A novel neural network structure is considered, based on a pipeline of three ConvNets and endowed with the capacity for context awareness, which improves grid-based search for defects on orthoframes by considering the surrounding image content&mdash;an approach, which essentially draws inspiration from how humans tend to solve the task of image segmentation. Also, methods for assessing the quality of segmentation are discussed. The contribution also describes the complete procedure of working with pavement defects in an industrial setting, involving the workcycle of defect annotation, ConvNet training and validation. The results of ConvNet evaluation provided in the paper hint at a successful implementation of the proposed technique

Adaptive Control of Nonlinear TRMS Model by Using Gradient Descent Optimizers

International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEYThis study demonstrates an application of direct gradient descent control for adaptively control of a nonlinear stable system models. The approach is based on utilization of gradient descent optimization techniques for the synthesis of control signals to control a specific plant model. In a former work, gradient descent optimizers were designed by considering a first degree instant input-output relation model assumption of the controlled system and this can allow model independent adaptive control of a class of plant models that can approximate to first order stable plant dynamics. The current study is an extension of this scheme for the purpose of nonlinear adaptive control. Here, we consider a higher degree polynomial assumption of instant input-output relations of the controlled system to obtain gradient descent optimizers that can be applied for adaptive control of a class of nonlinear systems. For evaluation of control performance of gradient descent optimizers, it is applied for the control of nonlinear TRMS model and the results are compared with performance of conventional PID control.Inonu Univ, Comp Sci Dept, IEEE Turkey Sect, Anatolian Sc

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the detector. Source data for training the detector come in the form of orthoframes acquired by a mobile mapping system. Compared to our previous work, the orthoframes are generally of better quality, but more importantly, in this work, we introduce a manual preprocessing step: sets of orthoframes are carefully selected for training and manually digitized to ensure adequate performance of the detector. Pretrained convolutional neural networks are then fine-tuned for the problem of pavement distress detection. Corresponding experimental results are provided and analyzed and indicate a successful implementation of the detector

Bridging the Gap in Technology Transfer for Advanced Process Control with Industrial Applications

In the present paper, a software framework comprising the implementation of Model Predictive Control&mdash;a popular industrial control method&mdash;is presented. The framework is versatile and can be run on a variety of target systems including programmable logic controllers and distributed control system implementations. However, the main attractive property of the framework stems from the goal of achieving smooth technology transfer from the academic setting to real industrial applications. Technology transfer is, in general, difficult to achieve, because of the apparent disconnect between academic studies and actual industry. The proposed software framework aims at bridging this gap for model predictive control&mdash;a powerful control technique which can result in substantial performance improvement of industrial control loops, thus adhering to modern trends for reducing energy waste and fulfilling sustainable development goals. In the paper, the proposed solution is motivated and described, and experimental evidence of its successful deployment is provided using a real industrial plant