Estimating Natural Frequencies of Cartesian 3D Printer Based on Kinematic Scheme

Nowadays, 3D printers based on Cartesian kinematics are becoming extremely popular due to their reliability and inexpensiveness. In the early stages of the 3D printer design, once it is chosen to use the Cartesian kinematics, it is always necessary to select relative positions of axes and linear drives (prismatic joints), which would be optimal for the particular specification. Within the class of Cartesian mechanics, many designs are possible. Using the Euler–Lagrange formalism, this paper introduces a method for estimating the natural frequencies of Cartesian 3D printers based on the kinematic scheme. Comparison with the finite element method and experimental validation of the proposed method are given. The method can help to develop preliminary designs of Cartesian 3D printers and is especially useful for emerging 3D-printing technologies

Image preprocessing for artistic robotic painting

Artistic robotic painting implies creating a picture on canvas according to a brushstroke map preliminarily computed from a source image. To make the painting look closer to the human artwork, the source image should be preprocessed to render the effects usually created by artists. In this paper, we consider three preprocessing effects: aerial perspective, gamut compression and brushstroke coherence. We propose an algorithm for aerial perspective amplification based on principles of light scattering using a depth map, an algorithm for gamut compression using nonlinear hue transformation and an algorithm for image gradient filtering for obtaining a well-coherent brushstroke map with a reduced number of brushstrokes, required for practical robotic painting. The described algorithms allow interactive image correction and make the final rendering look closer to a manually painted artwork. To illustrate our proposals, we render several test images on a computer and paint a monochromatic image on canvas with a painting robot

Improving accuracy and generalization performance of small-size recurrent neural networks applied to short-term load forecasting

The load forecasting of a coal mining enterprise is a complicated problem due to the irregular technological process of mining. It is necessary to apply models that can distinguish both cyclic components and complex rules in the energy consumption data that reflect the highly volatile technological process. For such tasks, Artificial Neural Networks demonstrate advanced performance. In recent years, the effectiveness of Artificial Neural Networks has been significantly improved thanks to new state-of-the-art architectures, training methods and approaches to reduce overfitting. In this paper, the Recurrent Neural Network architecture with a small-size model was applied to the short-term load forecasting of a coal mining enterprise. A single recurrent model was developed and trained for the entire four-year operational period of the enterprise, with significant changes in the energy consumption pattern during the period. This task was challenging since it required high-level generalization performance from the model. It was shown that the accuracy and generalization properties of small-size recurrent models can be significantly improved by the proper selection of the hyper-parameters and training method. The effectiveness of the proposed approach was validated using a real-case dataset. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

The impact of data filtration on the accuracy of multiple time-domain forecasting for photovoltaic power plants generation

The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Industry experience of developing day-ahead photovoltaic plant forecasting system based on machine learning

This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Разработка и исследование демодуляторов сигналов c псевдослучайной перестройкой рабочей частоты

Demodulation task is encountered in many practical applications including digital signal processing and digital communications. Demodulation is connected with the communication system performance. Demodulation depends on a number of factors including signal-to-noise ratio (SNR) in the received message. In practice, it is necessary to minimize the number of errors for the given SNR and therefore new demodulation techniques are constantly developed with increased interference immunity. Demodulators aimed at for frequency-hopping spread spectrum signals have to meet special requirements since the message length can reach several ms and the number of messages can exceed several dozens.Frequency-hopping spread spectrum is a technique of information transmission via radio channel and it is distinguished by variable carrier frequency that can change many times. The carrier frequency changes according to a pseudo random number sequence, which is available to both a sender and a recipient. This technique improves interference immunity of a communication channel.Frequency-hopping spread spectrum is used in civil and special applications. This signal is stable to jamming (until the third side finds out the number sequence), which makes it possible to use it for special purposes (however, the signal still needs additional encryption).Demodulation includes signal detection, synchronization, message type determination (modulation speed and modulation type), decoding, determination of autostarting and autostop combinations (for message identification), composition of the received message. The paper considers the tasks beginning with message type determination.Message type determination can be carried out several ways: using the cross-correlation function, spectral analysis, etc. Since the structure of a synchrosequence is known, it is possible to obtain more precise results using the crosscorrelation function. Several synchrosequences are formed for each message and then we compute their cross-correlation with the received message. The analysis includes the comparison all the results of cross-correlation function computa-tion and finally we make a decision regarding the message.Determination of autostarting and autostop combinations is performed by comparing autostarting and autostop combinations from the database. Each autostarting combination determines the receiver operation mode (single-channel or frequency-hopping spread spectrum). Determination of combinations is performed during signal demodulation.Reception of a frequency-hopping spread spectrum signal is performed according to the frequency plan. According to this plan, the carrier frequency changes in fixed time points. After receiving the autostarting combination of frequencyhopping spread spectrum, a reception mode for frequency-hopping spread spectrum signal is switched on. After receiving the autostop combination this mode is terminated. The output of a demodulator is the message itself, modulation type, and carrier frequency.The outcome of demodulator performance can be represented with a table. The first column of this table contains the carrier frequency, the second column contains frequency deviation, the third column - modulation type, the fourth one - message speed, the fifth one and further - the message itself.In the paper, we provide new demodulation techniques of frequency-modulated messages for the given SNR. The developed techniques are based on spectral analysis and correlation analysis. We determine the computational complexity of the developed demodulation techniques. The total error is computed for each SNR and the selected demodulation technique using the developed MATLAB/Simulink model for a communication channel. Finally, we conclude about the best demodulation technique for the selected message type for the given SNR. Представлены разработка и исследования различных методов демодуляции частотно-манипулированных сообщений в режиме псевдослучайной перестройки рабочей частоты при заданном диапазоне изменения отношения "сигнал/шум" (ОСШ). Разработанные методы основаны на использовании спек­ трального и корреляционного анализа. На основе разработанной в MATLAB/Simulink модели канала связи вычислена ошибка исследуемых методов демодуляции при различных значениях ОСШ. В результате исследования определен наилучший метод демодуляции при заданном ОСШ.

Spatial Anisotropies and Temporal Fluctuations in Extracellular Matrix Network Texture during Early Embryogenesis

Early stages of vertebrate embryogenesis are characterized by a remarkable series of shape changes. The resulting morphological complexity is driven by molecular, cellular, and tissue-scale biophysical alterations. Operating at the cellular level, extracellular matrix (ECM) networks facilitate cell motility. At the tissue level, ECM networks provide material properties required to accommodate the large-scale deformations and forces that shape amniote embryos. In other words, the primordial biomaterial from which reptilian, avian, and mammalian embryos are molded is a dynamic composite comprised of cells and ECM. Despite its central importance during early morphogenesis we know little about the intrinsic micrometer-scale surface properties of primordial ECM networks. Here we computed, using avian embryos, five textural properties of fluorescently tagged ECM networks — (a) inertia, (b) correlation, (c) uniformity, (d) homogeneity, and (e) entropy. We analyzed fibronectin and fibrillin-2 as examples of fibrous ECM constituents. Our quantitative data demonstrated differences in the surface texture between the fibronectin and fibrillin-2 network in Day 1 (gastrulating) embryos, with the fibronectin network being relatively coarse compared to the fibrillin-2 network. Stage-specific regional anisotropy in fibronectin texture was also discovered. Relatively smooth fibronectin texture was exhibited in medial regions adjoining the primitive streak (PS) compared with the fibronectin network investing the lateral plate mesoderm (LPM), at embryonic stage 5. However, the texture differences had changed by embryonic stage 6, with the LPM fibronectin network exhibiting a relatively smooth texture compared with the medial PS-oriented network. Our data identify, and partially characterize, stage-specific regional anisotropy of fibronectin texture within tissues of a warm-blooded embryo. The data suggest that changes in ECM textural properties reflect orderly time-dependent rearrangements of a primordial biomaterial. We conclude that the ECM microenvironment changes markedly in time and space during the most important period of amniote morphogenesis—as determined by fluctuating textural properties

A Robot for Artistic Painting in Authentic Colors

Artistic robotic painting automates the process of creating an artwork. This complex and challenging task includes several aspects: creating algorithms for rendering brushstrokes, reproducing the exact shape of a brushstroke, and developing the principles of mixing paints. This work contributes to the previously unsolved problem of accurately reproducing colors of brushstrokes by means of artistic paints. The main contributions of this paper include: the development of a novel 4-component data-driven mathematical model for artistic paint mixing; the design and implementation of a novel robot capable of accurately dosing and mixing acrylic paints thanks to the improved syringe pumps and the innovative paint mixer; the development of a novel pneumatic system for paint release with a build-in clogging detection mechanism. The capabilities of the designed robotic system are demonstrated by painting four artworks: replicas of Claude Monet’s and Arkady Rylov’s landscapes, a synthetic image generated using the StyleGAN2 neural network trained on Vincent van Gogh’s artistic heritage, and a synthetic image generated using the Midjourney neural network. The obtained results can be useful in various applications of computer creativity, as well as in artistic image replication and restoration, and also in colored 3D printing

A two-parameter modified logistic map and its application to random bit generation

This work proposes a modified logistic map based on the system previously proposed by Han in 2019. The constructed map exhibits interesting chaos related phenomena like antimonotonicity, crisis, and coexisting attractors. In addition, the Lyapunov exponent of the map can achieve higher values, so the behavior of the proposed map is overall more complex compared to the original. The map is then successfully applied to the problem of random bit generation using techniques like the comparison between maps, XOR, and bit reversal. The proposed algorithm passes all the NIST tests, shows good correlation characteristics, and has a high key space. © 2020 by the authors