Oleg Sergiyenko

Bio: Oleg Sergiyenko is an academic researcher from Autonomous University of Baja California. The author has contributed to research in topics: Machine vision & Laser scanning. The author has an hindex of 22, co-authored 154 publications receiving 1409 citations. Previous affiliations of Oleg Sergiyenko include Technical University of Madrid & University of Pardubice.

Mobile robot vision system using continuous laser scanning for industrial application

Abstract: The purpose of this paper is the presentation and research of a novel robot vision system, which uses laser dynamic triangulation, to determine three-dimensional (3D) coordinates of an observed object. The previously used physical operation principle of discontinuous scanning method is substituted by continuous method. Thereby applications become possible that were previously limited by this discretization.,The previously used prototype No. 2, which uses stepping motors to realize a discontinuous laser scan, was substituted by the new developed prototype No. 3, which contains servomotors, to achieve a continuous laser scan. The new prototype possesses only half the width and turns out to be significantly smaller and therefore lighter than the old one. Furthermore, no transmissions are used, which reduce the systematic error of laser positioning and increase the system reliability.,By using a continuous laser scan method instead of discontinuous laser scan method, dead zones in the laser scanner field can be eliminated. Thereby, also by changing the physical operation principle, the implementation of applications is allowed, which previously was limited by the fixed step size or by the object distance under observation. By using servomotors instead of stepping motors, also a significant reduced positioning time can be accomplished maintaining the relative positioning error less than 1 per cent.,The originality is based on the substitution of the physical operation principle of discontinuous by continuous laser scan. The previously used stepping motors discretized the laser scanner field and thereby produced dead zones, where 3D coordinates cannot be detected. These stepping motors were substituted by servomotors to revoke these disadvantages and provide a continuous laser scan, where dead zones in the field of view get eliminated and the step response of the laser scanner accelerated.

Improve a 3D distance measurement accuracy in stereo vision systems using optimization methods’ approach

Abstract: This paper presents a 3D distance measurement accuracy improvement for stereo vision systems using optimization methods A Stereo Vision system is developed and tested to identify common uncertainty sources. As the optimization methods are used to train a neural network, the resulting equation can be implemented in real time stereo vision systems. Computational experiments and a comparative analysis are conducted to identify a training function with a minimal error performance for such method. The offered method provides a general purpose modelling technique, attending diverse problems that affect stereo vision systems. Finally, the proposed method is applied in the developed stereo vision system and a statistical analysis is performed to validate the obtained improvements.

Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications

Abstract: For agricultural applications, regularized smart-farming solutions are being considered, including the use of unmanned aerial vehicles (UAVs). The UAVs combine information and communication technologies, robots, artificial intelligence, big data, and the Internet of Things. The agricultural UAVs are highly capable, and their use has expanded across all areas of agriculture, including pesticide and fertilizer spraying, seed sowing, and growth assessment and mapping. Accordingly, the market for agricultural UAVs is expected to continue growing with the related technologies. In this study, we consider the latest trends and applications of leading technologies related to agricultural UAVs, control technologies, equipment, and development. We discuss the use of UAVs in real agricultural environments. Furthermore, the future development of the agricultural UAVs and their challenges are presented.

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning.

Abstract: Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.