Explore the vast range of titles available.

Microwave electromagnetic devices have been used for many applications in tropospheric communication, navigation, radar systems, and measurement. The development of the signal preprocessing units including frequency-selective devices (bandpass filters) determines the reliability and usability of such systems. In wireless sensor network nodes, filters with microstrip resonators are widely used to improve the out-of-band suppression and frequency selectivity. Filters based on multimode microstrip resonators have an order that determines their frequency-selective properties, which is a multiple of the number of resonators. That enables us to reduce the size of systems without deteriorating their selective properties. Various microstrip multimode resonator topologies can be used for both filters and microwave sensors, however, the quality criteria for them may differ. The development of every resonator topology is time consuming. We propose a technique for the automatic generation of the resonator topology with required frequency characteristics based on the use of evolutionary algorithms. The topology is encoded into a set of real valued parameters, which are varied to achieve the desired features. The differential evolution algorithm and the genetic algorithm with simulated binary crossover and polynomial mutation are applied to solve the formulated problem using the dynamic penalties method. The experimental results show that our technique enables us to find microstrip resonator topologies with desired amplitude-frequency characteristics automatically, and manufactured devices demonstrate characteristics very close to the results of the algorithm. The proposed algorithmic approach may be used for automatically exploring the new perspective topologies of resonators used in microwave filters, radar antennas or sensors, in accordance with the defined criteria and constraints.

Hybrid high-voltage direct current (HVDC) transmission has the characteristic of long transmission distance, complex corridor environment, and rapid fault evolution of direct current (DC) lines. As high fault current can easily cause irreversible damage to power devices, rapid and reliable line protection and isolation are necessary to improve the security and reliability of hybrid HVDC transmission system. To address such requirement, this paper proposes a single-ended protection method based on transient voltage frequency band characteristics. First, the frequency characteristics of the smoothing reactor, DC filter, and DC line are analyzed, and the characteristic frequency band is defined. A fault criterion is then constructed based on the voltage characteristic frequency band energy, and faulty pole selection is performed according to the fault voltage characteristic frequency band energy ratio. The proposed protection method is verified by simulation, and the results show that it can rapidly and reliably identify internal and external faults, accurately select faulty poles without data communication synchronization, and has good fault-resistance and anti-interference performance.

Background Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. New method In this study, we provide a classification approach for SZ patients based on a spatial–temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions. Results We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands. Comparison with existing methods Other methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal. Conclusion We provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.

Harvesting sugar and fodder beet tops is a complex technological process that requires the use of special harvesting machines. Trailed harvesters of different rows, which together with aggregate tractors form symmetric or asymmetric machine-tractor units, the movement of which in the horizontal plane is not always stable, are widely used. The purpose of this study is to determine the parameters of stable plane-parallel motion of asymmetric harvester machine-tractor unit based on numerical computer simulation of the obtained analytical dependencies. According to the results of the analytical study, the values of the amplitude and phase-frequency characteristics of the turning angle tractor’s oscillations were obtained. They reflect the reproduction by the angle rotation fluctuations of the haulm harvester machine in the horizontal plane. Calculations have shown that reducing the value of the input resistance coefficient of pneumatic tires of the driving wheels of the aggregating tractor increases its sensitivity to the action of disturbing influences. The greater the sensitivity, the closer the wheels of the power tool are to the attachment point of the trailed haulm harvester. In qualitative terms, increasing the speed of the machine-tractor unit from 1.5 to 2.5 m∙s−1 leads to an undesirable increase in the amplitude-frequency response and desired increase in the phase-frequency response when reproducing its external disturbing effects in the form of oscillations of the angle of rotation of the harvester.

One of the main problems in oil fields is the deposition of scale inside oil pipelines, which causes problems such as the reduction of the internal diameter of oil pipes, the need for more energy to transport oil products, and the waste of energy. For this purpose, the use of an accurate and reliable system for determining the amount of scale inside the pipes has always been one of the needs of the oil industry. In this research, a non-invasive, accurate, and reliable system is presented, which works based on the attenuation of gamma rays. A dual-energy gamma source (241Am and 133Ba radioisotopes), a sodium iodide detector, and a steel pipe are used in the structure of the detection system. The configuration of the detection structure is such that the dual-energy source and the detector are directly opposite each other and on both sides of the steel pipe. In the steel pipe, a stratified flow regime consisting of gas, water, and oil in different volume percentages was simulated using Monte Carlo N Particle (MCNP) code. Seven scale thicknesses between 0 and 3 cm were simulated inside the tube. After the end of the simulation process, the received signals were labeled and transferred to the frequency domain usage of fast Fourier transform (FFT). Frequency domain signals were processed, and four frequency characteristics were extracted from them. The multilayer perceptron (MLP) neural network was used to obtain the relationship between the extracted frequency characteristics and the scale thickness. Frequency characteristics were defined as inputs and scale thickness in cm as the output of the neural network. The prediction of scale thickness with an RMSE of 0.13 and the use of only one detector in the structure of the detection system are among the advantages of this research.

In the field of microgrids (MGs), steady-state power imbalances and frequency/voltage fluctuations in the transient state have been gaining prominence owing to the advancing distributed energy resources (DERs) connected to MGs via grid-connected inverters. Because a stable, safe power supply and demand must be maintained, accurate analyses of power system dynamics are crucial. However, the natural frequency components present in the dynamics make analyses complex. The nonlinearity and confidentiality of grid-connected inverters also hinder controllability. The MG considered in this study consisted of a synchronous generator (the main power source) and multiple grid-connected inverters with storage batteries and virtual synchronous generator (VSG) control. Although smart inverter controls such as VSG contribute to system stabilization, they induce system nonlinearity. Therefore, Koopman mode decomposition (KMD) was utilized in this study for consideration as a future method of data-driven analysis of the measured frequencies and voltages, and a frequency response analysis of the power system dynamics was performed. The Koopman operator is a linear operator on an infinite dimensional space, whereas the original dynamics is a nonlinear map on a finite state space. In other words, the proposed method can precisely analyze all the dynamics of the power system, which involve the complex nonlinearities caused by VSGs.

Wearable sensor devices with minimal discomfort to the wearer have been widely developed to realize continuous measurements of vital signs (body temperature, blood pressure, respiration rate, and pulse wave) in many applications across various fields, such as healthcare and sports. Among them, microelectromechanical systems (MEMS)-based differential pressure sensors have garnered attention as a tool for measuring pulse waves with weak skin tightening. Using a MEMS-based piezoresistive cantilever with an air chamber as the pressure change sensor enables highly sensitive pulse-wave measurements to be achieved. Furthermore, the initial static pressure when attaching the sensor to the skin is physically excluded because of air leakage around the cantilever, which serves as a high-pass filter. However, if the frequency characteristics of this mechanical high-pass filter are not appropriately designed, then the essential information of the pulse-wave measurement may not be reflected. In this study, the frequency characteristics of a sensor structure is derived theoretically based on the air leakage rate and chamber size. Subsequently, a pulse wave sensor with a MEMS piezoresistive cantilever element, two air chambers, and a skin-contacted membrane is designed and fabricated. The developed sensor is 30 mm in diameter and 8 mm in thickness and realizes high-pass filter characteristics of 0.7 Hz. Finally, pulse wave measurement at the neck of a participant is demonstrated using the developed sensor. It is confirmed that the measured pulse wave contains signals in the designed frequency band.

When harvesting cereals and fodder grasses, a two-phase method is often used. This process is carried out using trailed and suspended collecting units. The former are asymmetrical and often pose problems regarding the stability of their movement in the horizontal plane. In practice, suspended harvesting units with a front-mounted header are becoming more and more widely used. The disadvantage of their use is that the soil is exposed after passing through the space between the swaths of the mown crop. This is followed by an intense loss of moisture. In order to eliminate this shortcoming, a collecting unit was proposed, consisting of a tractor with a front attachment and a disc harrow mounted at the rear. An appropriate mathematical model was developed to justify the scheme and parameters of such a unit. In this case, this model is used to assess the controllability of the movement of the dynamic system under the influence of control action in the form of the angular rotation of the tractor’s steered wheels. As a result of mathematical modelling, it was found that satisfactory controllability of the movement of the harvesting units can be ensured by acting on the tractor’s driven wheels with a frequency of 0–1 s−1 and a working speed of close to 3 m·s−1. In this case, it is desirable to set the deflection resistance coefficient of the rear tyres of the tractor (and therefore, the air pressure in them) to a smaller value, and that of the front tyres to a larger value. This helps both to improve the movement controllability of the harvesting unit and to reduce its energy consumption by an average of 6.75%. The emissivity of selected harmful chemicals and particulates emitted by the harvesting unit, depending on the fuel burned, was also examined. The way in which the use of the harvesting unit affects the reduction of emissions of harmful compounds into the atmosphere was also revealed.

The two-phase method is quite widespread when harvesting grain crops and forage grasses. For its implementation, farmers prefer harvesting units with a front-mounted header. The main disadvantage of such units is that after their passage in the space between the swaths of mown crops, open soil falls under the direct influence of sunlight. Subsequently, this leads to an intensive loss of its moisture. A harvesting unit has been designed to eliminate this shortcoming, consisting of a tractor with a front harvester and a soil-cultivating machine (disc harrow) mounted at the rear. The latter chops stubble and soil in the space between the swaths of the mowed crop, contributing to soil moisture conservation. In the diagram plan, the tillage machine can have a swivel or fixed conjunction with the tractor in a horizontal plane. A mathematical model of the harvesting unit plane-parallel movement in the horizontal plane has been designed to choose these conjunctions better. In the study, it is used to analyze the corresponding amplitude (AFC) and phase (PFC) frequency characteristics of a dynamic system when it is working out a control action in the form of the sighting point transverse shift. As a mathematical modeling result, it was found that the fixed conjunction of a tillage machine with a tractor is preferable. In this case, the natural AFC practically approaches the perfect one for servo dynamical systems. The correlation degree between the tractor‘s steered wheels‘ rotation and heading angles increases by almost 37%. As a result, this leads to a significant decrease in this parameter variance fluctuations, which contributes to an increase in the harvesting unit controllability movement as a whole.

The modern world industry involves the use of innovative approaches and optimisations of the existing agricultural management methods, which contribute to the implementation of the sustainable development of related industries and economies of different countries. The use of mobile agricultural units with extended functional properties can have a steady demand in the agricultural machinery market and contribute to the practical implementation of the philosophy of the “European Green Deal”. The research results show that when assembling a unit for mowing agricultural crops with simultaneous grinding and placing their stubble in the soil, preference should be given to a self-propelled machine with rear swivel wheels. When using a wheeled tractor, it must have a reversible control post and a reversible transmission. A mathematical model of the collecting unit was developed, which allows for obtaining the corresponding amplitude and phase frequency characteristics and, with their help, the stability of the horizontal movement was evaluated. According to the results of field studies, the dispersion of the angle of directional oscillation of the tractor with front-steered wheels was 4.48 grad2. For the tractor with rear-steered wheels, the value of this statistical parameter was 2.90 grad2, which, according to the F-test at the level of statistical significance of 0.05, is naturally lower.