bluuebunny/crossref_metadata_2025_split huggingface dataset:
Possibility and application of all-weather lidar-radio sensing complexes
Authors: Boreysho A S , Ilyin M Y , Kim A A , Konyaev M A , Shchukin G G , Zhukov V Y 
Published: June 2020

Abstract
The article discusses the main problems of the development and applying of all-weather meteorological complexes of remote optical and radar atmospheric sensing. Discussed different areas of application depending on type of airports. The concept of a new mobile three-band true all-weather meteorological single platform complex for operational measurements in airports is proposed. Shown the results of multi-bands systems measurements in PPI mode and vertical profiler performed under various conditions.
Ultrashort pulse decomposition in reflection symmetric meander lines of two cascaded half-turns
Authors: Chernikova E B , Belousov A O , Gazizov T R 
Published: June 2020

Abstract
The paper presents the analysis of the time response of reflection symmetric meander lines of two cascaded half-turns to the excitation of an ultrashort pulse (USP). The analysis is based on simulating 3 circuit diagrams of half-turns connections for a length of 1 m. The simulations revealed the possibility of additional pulses in the time response to appear at the output. It is shown that the delay values of the additional pulses are equal to the arithmetic mean values of the two mode delays in different versions. The results of the work are useful for further study of additional pulses in the time response and to establish the nature of their occurrence.
Antenna for radar “Radio Dozor-M”
Authors: Satarov R N , Tseplyaev I S , Shipilov S E , Buyanov Y 
Published: June 2020

Abstract
The article presents a new antenna for “Radio Dozor-M” device, which uses for a searching people behind the obstacles. The antenna is maded with a printing on a foil PCB, mounted on a copper base. The antenna is part of an array with serial perquisition and it’s a combination of electrical and magnetic emitters. The calculated characteristics of the simulated antenna are presented. Also presents the results of the oscillogram obtained by probing a person behind the wall.
Experimental modeling of radio information systems for solving the problem of failures forecasting
Authors: Perlov A , Zyuzin A , Kokuev A , Kochkarov A , L’vov K , Tymoshenko A 
Published: June 2020

Abstract
We report a simulation model of technical condition data of radio information systems at solving the problem of failures forecasting. Time cost analysis on sampling for high-accuracy forecasting of radio information systems failures showed that the performance of samples accumulation by testing the equipment at the stands does not allows us to create radio information systems with automated operation management system on time. The existing level of product unification allows the use of both data from previous generation stations and current data from of the built-in control of new generation radar stations, due to their correlation connections. These features create the conditions for operative formation of a large amount of data on the technical states of the stations due to the development of a simulation data model of the built-in control. A feature of such a model concludes in its versatility, i.e. the formation of the data stream on both binary sensors (operational or not) and sensors of physical parameters. An analysis of the temperature recorded during the development process of the components was carried out to determine the minimum sample size required. The temperature prediction model described above was trained on samples of various volumes for determination of the influence of the training sample volume on forecast accuracy. The developed methodological apparatus for formation of data from the station’s built-in control provides a solution of the problem of high-precision failure prediction.
Combined treatment of energy-saturated materials in external fields
Authors: Kuzmina L , Gazenaur E , Krasheninin V , Mixel O , Galtseva O , Gazenaur N 
Published: June 2020

Abstract
The paper analyses the effect lead and iron ions have on slow and explosive decomposition in crystals of silver azide initiated by a contact electric filed, local physical impact and UV-radiation. The reaction of decomposition registered immediately in the process of impact was examined according to gas release. This method provides accuracy of 10−12 mole. A stop-watch was used to determine time-to-explosion. The outcomes of experiments show that introduction of lead and iron ions into crystals of silver azide deteriorates strength of samples under local physical impact; furthermore, gas release caused by this type of impact stimulates fracture of samples. Experimental data indicate that crystals of silver azide with introduced lead and iron tend to decompose more intensively in a contact electric field. As for UV-radiation, it also has a significant decomposing effect on samples with introduced impurity substances, however, no explosion is registered, i.e. a maximal amount of gas is emitted at a certain period of time; afterwards gas release becomes less intensive and stops in approximately 20 minutes after the treatment starts. Any external impact generates misbalanced charge carriers (holes). Holes are localized on cation vacancies associated with generation of reactive centres. The number of reactive centres grows due to the increased concentration of impurity defects and might become critical. Data of experiments suggest that crystals of silver azide with introduced impurities are less stable and chemically inactive in external fields.
Electrons accelerator for research Cherenkov radiation in different specimens
Authors: Tarasenko V F , Alekseev S B , Kh Baksht E , Burachenko A G , Lomaev M I 
Published: June 2020

Abstract
Cherenkov radiation is widely used to register charged particles with high energy. This paper describes the design and parameters of an electrons accelerator, as well as the results of studies of the spectra and amplitude-time characteristics of sapphire and quartz KU1 radiation under the excitation of electrons with an energy of up to 350 keV. It was shown that in the UV and visible spectral regions a wide band is recorded, the intensity of which increases with decreasing wavelength. It was established that the duration and shape of the radiation pulse of this band coincides with the pulse of the electron beam. All this allows us to attribute the registered emission to the Cherenkov radiation.
Digital 3d x-ray microtomographic scanners for electronic equipment testing
Authors: Klestov S A , Syryamkin V I , Suntsov S B 
Published: June 2020

Abstract
The article studies the operating procedures of an X-ray microtomographic scanner and the module of reconstruction and analysis 3D-image of a test sample in particular. An algorithm for 3D-image reconstruction based on image shadow projections and mathematical methods of the processing are described. Chapter 1 describes the basic principles of X-ray tomography and general procedures of the device developed. Chapters 2 and 3 are devoted to the problem of resources saving by the system during the X-ray tomography procedure, which is achieved by preprocessing of the initial shadow projections. Preprocessing includes background noise removing from the images, which reduces the amount of shadow projections in general and increases the efficiency of the group shadow projections compression. In conclusion, the main applications of X-ray tomography are presented.
Development of microstrip ferrite decoupling devices for mm-wave range microwave equipment
Authors: Semenov A S , Nalogin A G , Alekseev A A 
Published: June 2020

Abstract
This article considers structural and technological features, which developers encounter during the creation of microstrip ferrite devices for mm-range microwave equipment. The development results of a 8-mm range microstrip ferrite circulator and isolator are presented in the article.
Do recent NDVI trends demonstrate boreal forest decline in Alaska?
Authors: Fiore N M , Goulden M L , Czimczik C I , Pedron S A , Tayo M A 
Published: June 2020

Abstract
Remote sensing analyses of boreal forest regions have found widespread decreasing or increasing trends in normalized difference vegetation index (NDVI). Initially, these trends were attributed to climate change induced shifts in primary productivity. It is emerging, however, that fire disturbance and subsequent succession also strongly impact the optical properties of boreal forests. Here we use NDVI time series data from Landsat (1999–2018) paired with surveys of 102 forest stands with known recent fire history to investigate the relationship between NDVI and forest structure during succession. We found that NDVI varies systematically with stand age as a result of successional changes in forest structure and composition and that the proportion of deciduous (broad-leaved) trees in the upper canopy is a better predictor of NDVI than leaf area index. Recent fire disturbance led to strong NDVI decreases and early post-fire recovery of herbaceous and deciduous vegetation to strong NDVI increases. The mid-succession transition from deciduous to evergreen (needle-leaved) stands led to weak NDVI decreases, while mid-to-late succession thinning of evergreen canopies led to weak NDVI increases. Thus, both increasing and decreasing NDVI stands occur naturally across the landscape, and do not necessarily reflect a large-scale shift in boreal forest productivity.
GIS Partial Discharge Patterns Recognition with Spherical Convolutional Neural Network
Authors: Yang Wei , Zhang Guobao , Zhu Taiyun , Cai Mengyi , Zhao Hengyang , Yan Jing , Wang Yanxin 
Published: June 2020

Abstract
The ubiquitous construction of the power Internet of Things provides a new idea for the real-time and accurate diagnosis of GIS partial discharge online monitoring fault diagnosis. However, the traditional partial discharge fault diagnosis method is difficult to solve the problem that the fault information of different online monitoring systems is different from the reference axis. In order to solve the problem that the fault information is difficult to identify in rotation and transformation, and improve the accuracy of fault diagnosis, this paper proposes a spherical convolutional neural network based on complex data sources. First, the PRPS picture transmitted to the ubiquitous power Internet of Things terminal is selected as the fault feature information. Secondly, a generalized Fourier algorithm (GFT) algorithm is used to construct a spherical convolution structure for PD pattern recognition. The algorithm can perform automatic feature extraction. Thirdly, the spherical convolutional neural network-based PD recognition method is applied to processing of the complex data sources with 84.88% average accuracy rate. It shows that the PRPS 3D map is one of effective way to avoid the complexity of artificial feature extraction for spherical CNN and in the meantime, it can also improve the accuracy of fault diagnosis.
Analysis of The Influence of Flux Detector on the Subsynchronous Oscillation Characteristics of DFIG
Authors: Jin Haiqiang , Wen Jun , Wang Tongtong , Zhang Hanhua 
Published: June 2020

Abstract
There is a risk of subsynchronous oscillation after grid connection of doubly fed wind farm. The accurate detection of stator flux amplitude and phase will determine the accuracy of stator flux vector oriented control method, and then affect the subsynchronous oscillation characteristics of grid connected doubly fed wind turbine. Therefore, in PSCAD/EMTDC, this paper establishes the model of grid connected doubly fed wind turbine with u-i flux detector and u-ω flux detector respectively, and studies the influence of different flux detector on the subsynchronous oscillation. Simulation results show that different flux detector will participate in the subsynchronous oscillation to different extent.
Coordinated generation and transmission expansion planning approach considering probabilistic available transfer capability
Authors: Zhou Ping , Kang Peng , Chen Ruiguo , Tian Hao 
Published: June 2020

Abstract
The available transmission capacity of the network has a great impact on the safety and reliability of the whole system. After the reform of electricity market, the grid is required to have the highest available transmission capacity (ATC) based on the existing load due to the uncertain factors. Therefore, it is necessary to consider the growing demand for available transmission capacity from the grid planning stage. Meanwhile, considering the uncertain factors have a great influence on the available transmission capacity (ATC), this paper first models the three uncertain factors and introduces the solving method of probabilistic available transfer capability, then proposes a grid expansion planning model considering the probabilistic available transmission capacity under the influence of uncertainties, which is applied to the six-node test system. Finally, the genetic algorithm is used to solve the final planning scheme. Compared with other planning schemes, this method can consider the comprehensive investment cost and the probability available transmission capacity. The case study shows that the method is effective and can achieve good results.
Coordinated generation and transmission expansion planning approach based on chance-constraints using genetic algorithm
Authors: Zhou Ping , Kang Peng , Zhu Yuan , Chen Ruiguo 
Published: June 2020

Abstract
The grid planning is a crucial and basic work in the power system, but there are various uncertain factors in the power system, which cause dramatic influences to the size of the grid power investment, as well as the power system security and stability for next few years. Traditional planning methods are gradually hard to adapt the grid planning under situations especially with large amounts of uncertainties. To reduce the impact of uncertain factors of the power system, it is necessary to consider increasingly significant uncertainties in system planning. However, if all extreme scenarios all taken into account, generally, the optimization results will be really conservative. Therefore, to prevent a relatively conservative optimization scheme, this paper proposes a grid-based expansion planning model based on chance constraints which take fully considerations of system uncertain factors using genetic algorithm. Compared with other planning schemes, this method comprehensively considers the investment cost and the level of load loss. Finally, a test system with six nodes is used to verify the effectiveness of the proposed method. The economics of the optimization scheme using the proposed approaches are observed using the test system.
Double-layer Electricity Market Trading Strategy for Microgrids Based on Multi-agent System
Authors: Du Le , Zhu Xiaofei 
Published: June 2020

Abstract
Aiming at the power generation and consumption of microgrid, this paper proposes a double-layer electricity market trading model for microgrids(MGs) based on multi-agent system(MAS). In the upper level, MG conducts power transactions with other MGs and grid companies in the electricity market. In the lower layer, the various components within the MG coordinate and cooperate to obtain the maximum profit. In order to the uncertainty problem in power forecasting, a rolling optimization model based on chance constrained programming is established by combining interruptible load and demand response in the real-time market. And the consequence of the case analyses by MATLAB simulation proves the rationality and validity of the model for optimal dispatch of virtual power plants given by this paper.
Research on Optimizing the Recovery Strategy of a Distribution Network Communication System Under Extreme Disasters
Authors: Lin Husheng , Zhou Xia , Lu Jianqiang 
Published: June 2020

Abstract
Extreme disasters, such as typhoons, cause great damage to distribution network communication systems, affecting the perception of disaster situations and the repair speed of the distribution networks. To realize the rapid recovery of a distribution network, an optimal recovery strategy of the distribution network communication system is proposed in this paper from the perspectives of predisaster prediction and postdisaster rush repair. First, based on multivariate data, the space-time mesh fusion method is used to predict the fault position of the distribution network communication system, guiding the accurate allocation of emergency repair resources, and the fault position is corrected using man-made statistics after a disaster. Then, combining fiber optics, mobile nets and emergency communication vehicles, rush repair rules for the distribution network communication system after extreme disasters are put forward, and the shortest route search algorithm is used to solve the minimum rush repair time under the rules, which is the optimal rush repair scheme for the distribution network communication system after an extreme disaster. Finally, the proposed strategy is applied to a practical topology, and the results demonstrate that the strategy is reasonable and effective.
Prediction of Short-term PV output power Based on PCA-Stacking under different weather conditions
Authors: Xiaofei Zhu 
Published: June 2020

Abstract
With the continuous expansion of photovoltaic scale, the accurate prediction of photovoltaic power generation is increasingly important for grid dispatching and grid optimization operations. In this paper, the photovoltaic power generation mainly uses meteorological factors and historical data as the input and output of the neural network. The input quantity is large, the data is redundant, and the network is difficult to converge, which always has a great adverse effect on the accuracy of photovoltaic output prediction. Firstly, different weather types are classified according to the trend graphs of different weather types. Principal components analysis (PCA) is used to analyze less comprehensive features from multiple meteorological factors and reduce the input of predictive models. At the same time, aiming at the problem that the prediction accuracy of a single prediction model such as the existing neural network and wavelet analysis method is limited, the idea and method of integrated learning are introduced, and a short-term prediction method based on Stacking method combined with SVM and Xgboost is proposed. Compared with the single model of SVM and Xgboost, the results show that the proposed method has a significant improvement compared with the accuracy of a single prediction model.
Study on Characteristics of no-load Converter Transformer closing Accessing to High-Cable Rate Network
Authors: Wang Tongtong , Zhu Shengyi , Jin Haiqiang , Wen Jun 
Published: June 2020

Abstract
When the converter transformer is closed at no load state, the transient voltage is too large, which may cause the transformer core to saturate; meanwhile, the capacitance of the cable line to the ground is much larger than that of the overhead line, which may cause ferromagnetic resonance phenomenon. Taking the parameters of typical transmission line and 500kV converter transformer as an example, the simulation results of Matlab are used to analyze the possible effects of no-load closing of converter transformer under different cable-forming rates and different outlet modes, and put forward targeted measures, which have certain reference value for the future design of the converter station outline.
Simulation Analysis of Magnetizing Inrush Current of 500kV Transformer
Authors: Zhu Shengyi , Wang Tongtong , Jin Haiqiang , Wen Jun 
Published: June 2020

Abstract
The 500kV power grid is the most important high-power AC power system in China. The 500kV transformer is responsible for the regular uploading and sending tasks. It is important electrical primary equipment that connects two or even three different voltage levels. Taking a new 500kV transformer in Chongqing as an example, this paper discusses the influencing factors of 500kV transformer inrush current when connected to the grid through PSCAD/EMTDC software, and puts forward feasible measures to suppress the inrush current.
Kalman Filter Based Sensor Fusion for Altitude Estimation of Aerial Vehicle
Authors: Bashir Muhammad Asad , Malik Fahad Mumtaz , Akbar Zeeshan Ali , Uzair Muhammad 
Published: June 2020

Abstract
Stabilization of Aerial Vehicles requires altitude estimation. In this paper sensor fusion method is developed for aerial vehicles which focuses on estimation of the altitude. Altitude measurement using sensors is too noisy and biased so Kalman filter based sensor fusion is used to estimate altitude. The proposed method is divided into two parts: attitude estimation and altitude estimation. The method is implemented using IMU and barometer sensors. The results show that altitude is estimated accurately and errors are modelled in few seconds after initialization.
3-D Localization of UAV and Detection based on Harmonics Index and Spectral Entropy Criteria
Authors: Iqbal Muhammad Amjad , Zhao Zhao , ZhiYong Xu , Rehman Saad Ur 
Published: June 2020

Abstract
The popularity of drones or unmanned aerial vehicles (UAVs) will be increased enormously in future smart cities due to their usage in several domains. Significantly it’s an emerging research to localize and timely detect such kinds of objects to avoid threats. This paper considered to deploy an array of 4-microphones for acoustics localization enhancement and UAV detection optimization. For high accuracy of acoustics localization in 3-D, the cumulative algorithm of GCC-PHAT and least square (LS) based on TDOA is implemented to estimate acoustic direction of arrivals (DOAs). However, detection optimization is achieved by a spectral framework, where harmonics index estimation effectively determined sound and noise range in each frame, while spectral entropy featured noise and sound correlation, the least possible value of entropy verified the presence of UAV and optimize its detection. Moreover, experimental results confirmed the efficiency of proposed techniques, our system has robust performance for localization as it featured less than 5% errors, while detection accuracy is more than 75%.
Gait Phase Classification Based on sEMG Signals Using Long Short-Term Memory for Lower Limb Exoskeleton Robot
Authors: Yuan Ye , Guo Ziming , Wang Can , Duan Shengcai , Zhang Lufeng , Wu Xinyu 
Published: June 2020

Abstract
In this work, we present a Long Short-Term Memory Model (LSTMM) for gait phase classification based on sEMG signals to control the lower limb exoskeleton robot which can recognize 2 phases (Stand and Swing) of leg phases between the foot and ground. This model only needs four sEMG signals to control the lower limb exoskeleton robot helping the hemiplegia patient walking. Compared with the existing methods, the proposed model not only avoids the complex sensor systems but also enhances the accuracy of gait phase classification. The experimental results first verify the availability of sEMG data acquisition system on the lower limb exoskeleton robot made by the Shenzhen Institutes of Advanced Technologies (SIAT) by quantify the system with gold standard optoelectronic system Vicon, then show that the proposed LSTMM is significantly higher on prediction accuracy and has better robustness for gait phase classification to control the lower limb exoskeleton robot with different speeds. Finally, the maximum accuracy of LSTMM on the gait phase classification is 97.89%.