bluuebunny/crossref_metadata_2025 huggingface dataset:
Australian Weevils (Coleoptera: Curculionoidea) IV
Authors: Oberprieler Rolf , Zimmerman Elwood
Published: November 2020

Australian Weevils: Volume IV covers the 11 smaller tribes of the weevil subfamily Entiminae (broad-nosed weevils), which comprises more than 100 genera and 700 described species in Australia. Around half of this fauna is covered in Volume IV, featuring keys to all the tribes, genera and described species as well as updated concepts and diagnoses of the tribes and summarising accounts of the taxonomy, nomenclature, distribution and known hostplants of all the genera and species.
All the introduced species of Entiminae in Australia, most of which are regarded as agricultural or horticultural pests, are included, as are descriptions of 12 new genera and eight new species and identifications of about another 240 undescribed species. The book also includes an overview of the salient characters of the Entiminae, illustrated on 18 colour plates of diagnostic features needed for identifying these weevils, alongside a further 180 colour plates illustrating the habitus and genitalia of all the genera and of several other species and their diagnostic characters. The volume further includes an obituary and full publication list of the late Elwood C. Zimmerman as well as an updated list of recent literature on the Australian Entiminae and other weevils.
The book is an essential reference work for researchers and students working with entimine weevils both in Australia and abroad. It is part of the Australian Weevils series.
Metabolomics reveals the influences of smoke-water and karrikinolide on the biosynthesis of flavonoids and terpenoids in Salvia miltiorrhiza
Authors: Sun Hui , Liu Qian , Zhou Jie , Guo Lanping
Published: November 2020

Metabolomics was used to study the influences of smoke-water (SW) and karrikinolide (KAR1) on the biosynthesis of flavonoids and terpenoids in Salvia miltiorrhiza Bunge. The results showed that a total of 178 and 199 differential metabolites were obtained in SW and KAR1, respectively, compared to the control. The differential metabolites were assigned to the corresponding metabolic pathways. The results indicated that some metabolic pathways in treatments of SW and KAR1 overlapped, suggesting that treatments of SW and KAR1 showed similar effects on the metabolic mechanism of S. miltiorrhiza. To obtain a clear overview of changes in metabolic regulation, TCA cycle, glycolytic pathway, biosynthesis of flavonoids and terpenoids and amino acids metabolism pathway were mapped into a network. We found that treatments with SW and KAR1 could significantly promote the biosynthesis of flavonoids and terpenoids in S. miltiorrhiza. This study could help us better understand the influences of SW and KAR1 on secondary metabolites and their underlying mechanism.
The characteristics of Queensland private physiotherapy practitioners’ interprofessional interactions: a cross-sectional survey study
Authors: Seaton Jack A. , Jones Anne L. , Johnston Catherine L. , Francis Karen L.
Published: November 2020

Effective interprofessional collaboration (IPC) contributes to superior patient outcomes, facilitates cost-efficient health care, and increases patient and practitioner satisfaction. However, there is concern that IPC may be difficult to implement in clinical settings that do not conform to formal team-based processes, such as mono-professional physiotherapy private practice facilities. The aim of this study was to describe the characteristics of private physiotherapy practitioners’ interprofessional interactions, including their experiences and perceptions regarding IPC. A custom developed cross-sectional online survey instrument was used to collect data from physiotherapists employed in private practice facilities in Queensland, Australia. In all, 49 (20% response rate) physiotherapists completed the survey. Only a small proportion (14%) indicated that their interprofessional interactions were a daily occurrence, and less than one-third of all respondents (31%) participated in formal, multi-professional face-to-face planned meetings. Most participants (76%) reported a moderate-to-high level of satisfaction regarding their interprofessional interactions. Despite low self-reported levels of interprofessional activity and other data indicating that IPC is necessary for holistic patient care, this study shows that physiotherapists were predominately satisfied when interacting with health practitioners from various professional backgrounds. Further research is required to inform the implementation of robust strategies that will support sustainable models of IPC in physiotherapy private practice.
The effect of medium‐term heat acclimation on endurance performance in a temperate environment
Authors: Corbett Jo , Massey Heather C. , Costello Joseph T. , Tipton Michael J. , Neal Rebecca A.
Published: November 2020

We investigated whether an 11‐day heat acclimation programme (HA) enhanced endurance performance in a temperate environment, and the mechanisms underpinning any ergogenic effect. Twenty‐four males (V̇O2max: 56.7 ± 7.5 mL·kg−1·min−1) completed either: (i) HA consisting of 11 consecutive daily exercise sessions (60–90 min·day−1; n = 16) in a hot environment (40°C, 50% RH) or; (ii) duration and exertion matched exercise in cool conditions (CON; n = 8 [11°C, 60% RH]). Before and after each programme power at lactate threshold, mechanical efficiency, VO2max, peak power output (PPO) and work done during a 30‐minute cycle trial (T30) were determined under temperate conditions (22°C, 50% RH). HA reduced resting (−0.34 ± 0.30°C) and exercising (−0.43 ± 0.30°C) rectal temperature, and increased whole‐body sweating (+0.37 ± 0.31 L·hr−1) (all P≤0.001), with no change in CON. Plasma volume increased in HA (10.1 ± 7.2%, P < 0.001) and CON (7.2 ± 6.3%, P = 0.015) with no between‐groups difference, whereas exercise heart rate reduced in both groups, but to a greater extent in HA (−20 ± 11 b·min−1) than CON (−6 ± 4 b·min−1). VO2max, lactate threshold and mechanical efficiency were unaffected by HA. PPO increased in both groups (+14 ± 18W), but this was not related to alterations in any of the performance or thermal variables, and T30 performance was unchanged in either group (HA: Pre = 417 ± 90 vs. Post = 427 ± 83 kJ; CON: Pre = 418 ± 63 vs. Post = 423 ± 56 kJ). In conclusion, 11‐days HA induces thermophysiological adaptations, but does not alter the key determinants of endurance performance. In trained males, the effect of HA on endurance performance in temperate conditions is no greater than that elicited by exertion and duration matched exercise training in cool conditions.
Spatially and temporally resolved H and OH densities in a nanosecond pulsed plasma jet: an analysis of the radical generation, transport, recombination and memory effects
Authors: Yue Yuanfu , Jiang Jingkai , Kondeti V S Santosh K , Bruggeman Peter J
Published: November 2020

Cold atmospheric pressure plasma jets (APPJs) enable the generation of a large flux of highly reactive species to heat sensitive substrates at near ambient temperatures. Nonetheless, the experimental determination of reactive species inside APPJs, while exceedingly important, has not been reported in significant detail. In this work, we report the time-resolved axial density of hydroxyl (OH) and hydrogen radicals (H) in an APPJ device and its effluent operating in a He–H2O mixture using 1D laser induced fluorescence (LIF) and two-photon absorption LIF (TALIF). The results show that H and OH are mainly generated between the electrodes in the APPJ rather than by the guided streamer. The produced H and OH inside the jet are convectively transported to the jet effluent and determine the H and OH densities in jet effluent. The dominant production and destruction mechanisms of H and OH were obtained from a 0D model. The different production mechanisms of H and OH can explain the change in memory effect observed for OH (and not for H) for varying pulse repetition rates of the plasma generation.
Why the anti-nodal quasiparticle dispersion is so flat in the superconducting cuprates?
Authors: Li Tao , Yao Da-Wei
Published: November 2020

The emergence of the coherent quasiparticle peak and the development of the peak-dip-hump structure in the anti-nodal region below T
c is the most prominent non-BCS signature of the under-doped high-T
c cuprates, in which no coherent quasiparticle can be defined in the anti-nodal region above T
c. The peak-dip-hump structure has been commonly interpreted as the result of the coupling of the electron to some Bosonic mode. However, such an electron–Boson coupling picture does not answer the question of why the quasiparticle dispersion is so flat in the anti-nodal region, a behavior totally unexpected for Bogoliubov quasiparticle in a d-wave BCS superconductor. Here we show that the sharp quasiparticle peak in the anti-nodal region should be understood as a new pole in the electron Green’s function generated by the strong coupling of the electron to diffusive spin fluctuation around the antiferromagnetic wave vector Q = (π, π), rather than a nearly free Bogoliubov quasiparticle in a d-wave BCS superconductor. More specifically, we find that the normal self-energy of the electron from the scattering with the diffusive spin fluctuation manifests itself mainly as a level repulsion effect and is responsible for the reduction of both the quasiparticle dispersion and the quasiparticle dissipation rate in the anti-nodal region. We argue that the peak-dip separation in the anti-nodal spectrum should not be interpreted as the energy of the pairing glue.
Solving the spherical
p
-spin model with the cavity method: equivalence with the replica results
Authors: Gradenigo Giacomo , Angelini Maria Chiara , Leuzzi Luca , Ricci-Tersenghi Federico
Published: November 2020

The spherical p-spin is a fundamental model for glassy physics, thanks to its analytical solution achievable via the replica method. Unfortunately, the replica method has some drawbacks: it is very hard to apply to diluted models and the assumptions beyond it are not immediately clear. Both drawbacks can be overcome by the use of the cavity method; however, this needs to be applied with care to spherical models. Here, we show how to write the cavity equations for spherical p-spin models, both in the replica symmetric (RS) ansatz (corresponding to belief propagation) and in the one-step replica-symmetry-breaking (1RSB) ansatz (corresponding to survey propagation). The cavity equations can be solved by a Gaussian RS and multivariate Gaussian 1RSB ansatz for the distribution of the cavity fields. We compute the free energy in both ansatzes and check that the results are identical to the replica computation, predicting a phase transition to a 1RSB phase at low temperatures. The advantages of solving the model with the cavity method are many. The physical meaning of the ansatz for the cavity marginals is very clear. The cavity method works directly with the distribution of local quantities, which allows us to generalize the method to diluted graphs. What we are presenting here is the first step towards the solution of the diluted version of the spherical p-spin model, which is a fundamental model in the theory of random lasers and interesting per se as an easier-to-simulate version of the classical fully connected p-spin model.
Run-and-tumble particles in two dimensions under stochastic resetting conditions
Authors: Santra Ion , Basu Urna , Sabhapandit Sanjib
Published: November 2020

We study the effect of stochastic resetting on a run-and-tumble particle (RTP) in two spatial dimensions. We consider a resetting protocol which affects both the position and orientation of the RTP: the particle undergoes constant-rate positional resetting to a fixed point in space and a random orientation. We compute the radial and x-marginal stationary-state distributions and show that while the former approaches a constant value as r → 0, the latter diverges logarithmically as x → 0. On the other hand, both the marginal distributions decay exponentially with the same exponent when they are far from the origin. We also study the temporal relaxation of the RTP and show that the positional distribution undergoes a dynamic transition to a stationary state. We also study the first-passage properties of the RTP in the presence of resetting and show that the optimization of the resetting rate can minimize the mean first-passage time. We also provide a brief discussion of the stationary states for resetting a particle to an initial position with a fixed orientation.
Research on intelligent heuristic design of industrial product
Authors: Xiong Zhiyong , Liu Yu , Chen Haonan
Published: November 2020

Effective and efficient design resource searching significantly inspires the product modeling design. This paper investigates the open innovation platform and design websites, constructs the retrieval algorithm model of design resources based on generative adversary network (GAN). The performance of retrieval algorithm model is tested by comparative experiments with that of Behance. Results show that the retrieval performance of the proposed model can reach the basically the same effects as Behance in conditional experiments, which shows that the GAN retrieval algorithm can be of high value and great potential for development in actual application. At the same time, results also show that taking the Red Dot award-winning works as the retrieval data can better stimulate the inspiration of designers and provide design reference for designers.
The Impact of Climate Change on Agriculture Sector in ASEAN
Authors: Nunti Chonrada , Somboon Kewalin , Intapan Chanamart
Published: November 2020

This study purposes to estimate climate change effect on agriculture sector in ASEAN by using the copula-based stochastic frontier approach to evaluate the technical efficiency and factors that affect agriculture production. Panel data of land, labour, fertilizer, and temperature in seven countries in ASEAN including Thailand, Vietnam, Myanmar, Philippines, Indonesia, Cambodia, and Malaysia collected from 2002 - 2016 were used for estimating the model. The results presented that the land, labour, and fertilizer consumption according to the agriculture have positive and significant effects on agricultural production. The most interesting point from this study, found that there is a negative effect on agriculture production related by the climate change. Additionally, this study provides the most appropriate tools to analyse climate change impacts on ASEAN agriculture and the potential options for adaptation in the agriculture sector.
Forecasting Chinese Tourism Demand for Thailand: Using Markov Switching Autoregressive Model
Authors: Chernbumroong Sainatee , Nunti Chonrada , Somboon Kewalin
Published: November 2020

This study purposed to forecast the Chinese tourism demand for Thailand. The time series data of Chinese tourists arriving in Thailand were estimated by using MS-AR Model, the consumer price index of Thailand, and the Thai exchange rate (THB/RMP) based on a monthly basis ranged between 2014 and 2019 collected from Ministry of Tourism and Sports, Bank of Thailand, and Ministry of Commerce, respectively. The results showed that the consumer price index of Thailand and the Thai exchange rate had a significant effect on Chinese tourism demand for Thailand. The most crucial point of this study demonstrated that the CPI could stimulate the tourism industry during the low season, so that the government can utilize or put some policies in effect for stimulating the tourism industry by controlling the CPI. In addition, this study provides the most appropriate tools to forecast the demand of Chinese tourism in Thailand and the potential options for adaption in the tourism sector.
Research and Practice of Association Rules Method to Solve the Problem of Allocating Places for Spring Major in Colleges and Universities——Taking Shanghai Jianqiao University as an Example
Authors: Zhang Lei , Zheng Yuqian
Published: November 2020

Aiming at the problem of the allocation of enrollment majors and the allocation of majors of students applying for admission in the spring recruitment of colleges and universities, through the FP-Growth algorithm in data mining, the relationship between the attributes, majors and reports of students applying for in-depth mining will be able to describe a class of students. The attributes and assignments of majors and reports are used as an item set, and its frequent patterns are tapped. Then, according to the set support degree, a set that can reflect the characteristics of students is selected, and a model is built based on this result. At the same time, this paper improves the evaluation criteria of the usual school registration situation. Through the calculation of variance, the overall professional distribution and registration rate are evaluated. Experimental results show that the model has good performance and can reasonably recommend students’ professional assignment.
Analysis of Database Programming Technology in Computer Software Engineering
Authors: Hong Yunguo
Published: November 2020

This article analyzes the application value of database programming technology. Its application value includes improving practical effects, improving information security, and reducing the probability of technical problems. Combining the application characteristics of database programming technology, this paper studies the selection of database types, database establishment, database storage mode selection, file encryption protection, programming technology optimization selection, software and hardware system update design. The purpose of this article is to improve the application effect of database programming technology and improve the design level of computer software engineering.
Multi-UAV Collaborative Search and Strike based on Reinforcement Learning
Authors: Liu Bo , Wang Xiaoping , Zhou Wen
Published: November 2020

The problem of multi-UAV collaboration is one of the important research contents of multi-UAV systems. Aiming at the optimal strategy of multi-UAV collaborative search and strike against learning targets for moving targets in an unknown environment, a multi-UAV collaborative search and strike algorithm based on multi-team reinforcement learning is proposed. First, we establish a search probability map for the environment to search for moving targets After finding the target, the moving target is allocated by auction to form an independent investigation team for different goals. Then each inspection team learns its optimal strategy in cycles at the same time. At the same time, by changing the learning speed and supervising the learning results during the learning process, the convergence and effectiveness of the learning results are guaranteed. The simulation results show that the UAV can better adapt to the dynamic environment through training. Under the condition of no prior information, it can effectively execute the search and strike task against the moving target with learning ability. And compared with the learning method of Markov decision process, the convergence speed and learning speed are faster.
Micro-cracks Detection of Polycrystalline Solar Cells with Transfer Learning
Authors: Zhang Nawei , Shan Shuo , Wei Haikun , Zhang Kanjian
Published: November 2020

As the photovoltaic (PV) systems are universally utilized in power systems, the defect of solar cells, the core components of PV system requires to be detected in a low-cost and high-efficiency method, because of its wide application and direct influence on power generation efficiency and system stability. The principal aim of this study is to apply transfer learning in defect detection of polycrystalline cells with image-level labelled mono images and unlabelled poly images. The experiment was conducted with DAN models based on a MK-MMD to represent the distance between source domain and target domain and Resnet-50 backbone to learn transferable features. Three contrast methods were designed to make comparison with DAN. It has been proved that DAN model based on MK-MMD and Resnet-50 shows a promising performance on transferring from monocrystalline tasks to polycrystalline tasks.
Research on Detecting Moving Objects in Football Match Video
Authors: Xu Hongyi , Lu Jialing , He Qun , Gu Lei , Feng Chi
Published: November 2020

In recent years, detecting moving objects in video has attracted extensive attention in the field of computer vision and pattern recognition. With football becoming one of the most popular sports, analyzing the process of football games is of commercial value and guiding significance. However, the previous research mainly focused on the event detection, and rarely on getting the trajectory of moving objects by directly extracting from the video. This article discussed the method of detecting moving objects in the football match video. We extract the marker lines and build the coordinate system based on LUV color space. And we use the 68 landmarks to recognize the human face and the Kalman filter approach to detect the football. Our research is aimed at helping the audience and football professionals in analyzing the player performance and team strategy.
Medical Image Segmentation Based on Combination Method
Authors: Liu Yi , Zhang Caiming
Published: November 2020

This paper studies the data segmentation of medical images based on compressed sensing. We do not use image data as interpolation data, but as constraints to construct surface patches with quadratic polynomial approximation accuracy, study the properties and correlation of pixels, and take these edge points as constraints, and map the discrete image data into continuous mathematical functions, so that the continuous function or point data not only has the shape recommended by the image data, but also has a higher segmentation accuracy, and then realize the fine segmentation of images, which provides a new research idea to solve the difficult problems of spatial data representation, and has good theoretical significance and application value.
Dual-Branch Feature Fusion Network for Single Image Super-Resolution
Authors: Wang Kai , Duanmu Chunjiang
Published: November 2020

Rcently, the research of single image super-resolution (SISR) based on deep learning has made great progress. However, most of the methods of super-resolution (SR) study use a simple chain structure to obtain higher super-resolution performance. Additionally, most methods do not fully utilize the hierarchical features generated in the middle of the network, thereby cannot achieve relatively high performance. In this paper, we propose a novel dual-branch feature fusion network (DBFFN) to address this two problems in image SR. The backbone of DBFFN is composed of multiple dual-branch feature fusion block (DBFFB) cascaded. The DBFFB has a dual-branch structure which mainly contains two parallel sub-networks, one extracts image fine features via densely connected convolution layers, the other one sub-network extracts image more contextual features by stacking dilated convolution layers. The features extracted from each hierarchical of the DBFFB are cascaded, and then feature fusion is performed. A skip connection is introduced to learn residual between input and output of the DBFFB. Especially, we use the deconvolution layer at the end of the network to enlarge the image. The results of the experiment are exciting, when the scale factor is 3, the performance of our network has 0.56dB, 0.6dB, and 0.79dB improvement on the set5, set14, and urban100 benchmark datasets compared with RDN.
A semantic segmentation method for exposed rebar on dam concrete based on Unet
Authors: Zhang Jun , Mo Jian , Xu Huajun , Liu Zixing
Published: November 2020

Exposed rebar is an essential factor affecting the safety of the dam. In the past, manual inspection is a significant way to monitor exposed rebar risk. However, it is time-consuming, inefficient and difficult to quantitative evaluate, such as the exposed rebar area. A semantic segmentation method based on the Unet is proposed to replace the manual inspection for the dam exposed rebar automatic detection. Thirty-eight high-resolution images of dam exposed rebar are collected. Unet and the VGG16 backbone are adopted. The results indicated that Unet’s mIoU on the test set reaches 0.94, which proves to be an efficient way to detect the dam exposed rebar.
Adoption of Ensemble Empirical Mode Decomposition Algorithm and Back Propagation Neural Network in Net Surface Solar Radiation Prediction
Authors: Wang Yiting , Tan Wenan , Gong Yide , Guo Kai , Tang Shan
Published: November 2020

To improve the prediction accuracy (PA) of net surface solar radiation (NSSR), a net surface solar radiation (NSSR) prediction model, named EEMD-BPNN, is proposed by adopting the ensemble empirical mode decomposition (EEMD) algorithm along with back propagation neural network (BPNN). In this paper, EEMD is used to extract the signals to reduce the influence of noise with physical significance from the time series of the original NSSR, so as to obtain the intrinsic mode functions and residual terms of different frequencies. As well as BPNN is used to establish a corresponding prediction model for each component of mode function. The proposed model has been applied and verified in test the daily total NSSR in Aksu region, Xinjiang. In the case study, the mean percentage error (MPE), mean bias error (MBE), root mean square error (RMSE), and correlation coefficient are taken as evaluation indexes, and the accuracy and applicability of the EEMD-BPNN for NSSR prediction are analyzed by comparing the prediction results of the EEMD-BPNN with the BPNN and H-S model. The results show that the predicted values (PVs) of EEMD-BPNN are closer to the actual data and have better correlation coefficient (R2=0.9615) compared with the prediction results of BPNN (R2=0.8703) and H-S model (R2=0.8373), and the error analysis indexes of the predicted results are all small. It indicates that the PA of EEMD-BPNN is improved obviously, which means the EEMD-BPNN has superiority in NSSR prediction and provides a new reference method for NSSR prediction.
Fruit Recognition Based on Convolution Neural Network
Authors: Zhan Yuhui , Chen Yuefen , Li Xiaokang , Chen Mengyao , Luo Zhaoqian
Published: November 2020

Traditional fruit recognition is mainly manual, which is not conducive to automation. Deep convolution neural network (DCNN) has a strong ability of feature learning and expression. It is helpful to realize intelligence in fruit sales market if it is applied to the identification of fruits. Due to the lack of standard image databases and various types of fruits, image data sets used in this paper are obtained through taking pictures of fruits and network download. Considering the small number of samples, in addition to using common image processing technology for data expansion, transfer learning technology based on vgg16 model is also adopted for fine tuning, which can reduce the training time and alleviate over fitting. Finally, six kinds of common fruits are chosen for experiments, and the test results show that the average recognition accuracy reaches 94.16%.
MMUU-Net: A Robust and Effective Network for Farmland Segmentation of Satellite Imagery
Authors: Gao Xumin , Liu Long , Gong Huaze
Published: November 2020

Aiming at the multi-scale characteristics of satellite imagery, and the adhesion phenomenon in the farmland segmentation results which is caused by the close distance between different farmland blocks, this paper proposes a robust and effective network based on U-Net for farmland segmentation of satellite imagery, which is called MMUU-Net. On the basis of adopting the encoder with higher classification accuracy network, adding ASPP (Atrous Spatial Pyramid Pooling) layer in the middle, and designing the multi-scale feature fusion module in the decoder, so that the multi-scale feature information is fully utilized; in order to better fuse multi-scale information, a more robust loss function is designed; finally, we propose a segmentation strategy of the coarse and refined two-stage to eliminate the adhesion phenomenon. Through the comparative experiments, it is verified that MMUU-Net is better than other segmentation networks, and can be effectively applied to the task of farmland segmentation of satellite imagery.
Adaptive data fusion method based on Kalman filter and its application in piston shape and position detection
Authors: Nan Leiguang , Ma Xinqiang , Ge Hailong , Ma Qingzeng , Li Yanlei , Cheng Wei
Published: November 2020

In order to meet the needs of production, this paper studies the detection data processing algorithm for the angle of the piston shape and position, the groove width and the runout of the groove bottom. An adaptive data fusion method based on Kalman filtering is proposed. This algorithm can use the adaptive method to calculate the different weight coefficients of each data point according to the standardized sampling data and the smoothed data points obtained by the cubic B-spline approximate fitting algorithm. This paper obtain the fusion data; then calculate the groove angle, groove width and groove bottom runout of the piston according to the conversion formula. Finally, experimental data verification is carried out, which shows the effectiveness and good robustness of the data processing of this algorithm. At the same time, a piston shape and position detection system was developed. The system rotates a circle at 90 degrees per second to obtain the measurement data. After the system software and hardware processing, the groove angle detection accuracy can reach ±2 minutes, the groove width and the groove bottom. The detection accuracy of the runout amount is within ±2 microns, and the measurement items in the measurement time and accuracy side meet the customer’s technical document requirements and achieve satisfactory results.
The nonlinear characteristics of transformer broadband
Authors: Huang Dongdong , Li Jiangtao , He Shuang , Zhu Yi
Published: November 2020

At present, the modeling of the transformer broadband nonlinear characters is based on the nonlinear features of 50Hz, so there are still some questions. In this paper, the magnetic hysteresis loop of the silicon steel in the the range of 50Hz to 100kHz is measured by using a measuring system composed of a 25cm Epstein square coil and an amh-1m magnetometer. The J-A parameters of the silicon steel core in the frequency wide range of the range of the optimized by using the hybrid means on the oneran fish swarm and L-M. Then, the nonlinear core model of transformer based on J-A parameters is set in PSPICE. Based on the model, the transformer simulation circuit is built. By applying sinusoidal voltage signal of the specific frequency to the nonlinear transformer model with the specific frequency, the non-linear characters of the transformer in wide frequency range areed. It is found the nonlinear characters of the transformer core change dramatically with the change of the frequency. Therefore, in the broadband nonlinear modeling of the transformer, using the nonlinear characters at 50Hz to nonlinear the broadband model will lead to large error. In the follow-up modeling research, scholars should consider the nonlinear characters of the transformer to the set the model, and try to use the nonlinear characters of the transformer under the frequencies for the broadbandlinear modeling.
Fault location method based on three-phase asymmetric phase current fault component and Euclidean distance
Authors: Liu Qingshi , Xu Huian , Li Yifei
Published: November 2020

When a single-phase ground fault occurs in the distribution network, the three-phase current fault components of the fault path and the non-fault path in the system will be difference which can be used as a criterion for fault line selection. However the three-phase load current in the system is much larger than the fault component current, the three-phase phase fault component current is not obvious, which will cause the failure of the method. This paper proposes an improved fault location method based on the three-phase phase current: the fault component of the three-phase current of the system is obtained by subtracting the phase current before the fault occurs from the after. And then the zero-sequence current of this line is subtracted from the fault component to enhances the difference between the lines, and the fundamental amplitude of the result is obtained. The algorithm uses the three-phase fundamental amplitude as the coordinate axis to establish a coordinate system. The difference between the fault path and the non-fault path is enhanced by calculating the Euclidean distance of the three-phase current fundamental amplitude. And the location of the fault section can be judged locally by comparing the overall differences between the lines. In this paper, the specific fault section location process and criteria are proposed, and the feasibility of the method is verified by simulation experiments and actual field data.
A Minimized Buffered Link Scheduling Algorithm for WirelessHART Networks Based on Graph Route
Authors: Luo Nanhang , Fang Mingjie , Zhao Kunming , Du Enwu
Published: November 2020

WirelessHART network is an emerging wireless sensor network technology, famous for simplicity, safety, and reliability. WirelessHART network uses Time Division Media Access (TDMA) and Frequency Hopping Spread Spectrum (FHSS) access mechanism to avoid collisions and mitigate interference from other wireless networks. Many formulations of the link scheduling problem have been proven to be NP-complete, which is about how to assign slots and channels to links. In this paper, we propose a minimized buffered link scheduling algorithm for WirelessHART networks based on graph route (LBLSGR), which utilizes edge coloring to scheduling time slots according to the resource demand of nodes. We supply more communication resource to the nodes located nearer to the gateway, because of frequent packet forwarding. Utilizing this algorithm, the WirelessHART networks can make the average network delay least, the average network power assumption lower, and the packet buffers minimized. At last, the validity of our algorithm is proved and the test result is provided.
Simulation Method of Load Characteristics of High-power PMSM Drive System
Authors: Lu En , Li Wei , Yang Xuefeng , Liu Yufei
Published: November 2020

This paper presents a simulation method of load characteristics of high-power permanent magnet synchronous motor (PMSM) drive system. According to the structural characteristics of the PMSM drive system of electric tractor, the load simulation experiment system is built to simulate the load characteristics of high-power PMSM. Next, on the basis of the similarity principle, the parametric equivalent scaling principle of the high-power PMSM drive system and experimental PMSM is established. Then, a dynamic load tracking algorithm is proposed based on the PI controller and the structure of load simulation experiment system, and the low-pass filter (LPF) is introduced to reduce noise interference caused by the differential term of speed. Finally, the numerical simulations and experiments are presented according to the schematic diagram of the load characteristic simulation and dynamic load tracking strategy. The results demonstrate that the proposed simulation method of load characteristics can effectively simulate the dynamic load of high-power PMSM drive system, which lays a foundation for the verification of the PMSM speed control strategy.
Research on AGV Scheduling System Based on Internet of Things
Authors: Wang Zhiyong , Ai Changsheng , Wang Zhe , Ren Gangchang , Xu Qiang
Published: November 2020

In this paper, in order to solve the needs of a pharmaceutical factory’s logistics system for multiple automated guided vehicles (AGV), a set of AGV scheduling system architecture was developed on the basis of the outdoor heavy-duty AGV that has been developed. Firstly, a WiFi wireless network communication environment is established in the factory area. The AGV side establishes a TCP/IP connection with the dispatching system through the MOXA bridge. The communication application layer uses the MQTT protocol widely used in the Internet of Things, and combines the production beats, considering the task distribution and path Planning and other possible realistic factors, developing scheduling software, and establishing a factory environment scheduling model. Through function test and performance test, the feasibility of the system is verified. This solution is conducive to improving the scheduling system’s ability to handle high concurrent tasks and increasing the cargo transportation throughput of the logistics system.
Simulation of Sensorless Control for FSPMLM with high-frequency signal injection
Authors: Meng Gaojun , Zhang Feng , Yuan Ye , Zhu Zhiying , Sun Yukun , Tao Xia
Published: November 2020

This paper probes into a sensorless control method of flux-switching permanent linear magnet (FSPMLM) with high-frequency signal injection, and analyses the FSPMLM structure through salient pole verification. Specifically, a high frequency sinusoidal voltage signal was superimposed into the estimated d-axis, and the mover position was demodulated after the regulation of q-axis current, which is relative to error angle. Ultimately, the exact mover position and speed were obtained. To solve the starting problem of the FSPMLM, improved magnetic pole identification was performed based on space vector pulse width modulation for an arbitrary initial position. Through the simulation of the FSPMLM and the control system, it is confirmed that the proposed approach is of high accuracy and effectiveness.
FPGA implementation of eight-direction Sobel edge detection algorithm based on adaptive threshold
Authors: Zhang Kaiqiang , Liao Qiang
Published: November 2020

As the basic content of digital image processing, edge detection is increasingly applied in machine vision, face recognition, target tracking and other fields. In particular, edge detection based on Sobel operator is generally applied in industrial practices. However, most current edge detections based on PC-side serial processing are extremely arduous to satisfy real-time processing. This paper proposes FPGA implementation of eight-direction Sobel edge detection algorithm based on adaptive threshold. Specifically, an eight-direction Sobel edge detection algorithm based on adaptive threshold is presented. Subsequently, the specific process of implementing the algorithm on FPGA using the design philosophy of parallel pipeline is described. Finally, a comparison of the performance between traditional Sobel and improved algorithm reveals that the improved algorithm offers distinct advantages in accuracy and detection effectiveness, and the parallel implementation of FPGA hardware can obviously enhance the detection speed and fulfil the real-time requirement.
Monitoring systems of the combustion processes based on optical spectral devices
Authors: Vaganov M A , Kazakov V I , Moskaletz O D
Published: November 2020

The article considers the application of optical spectroscopy methods to address the problems of physical and technological process control. Such processes often run under extreme conditions. Therefore, to eliminate the negative influence of these conditions on a spectral device, it is proposed to use an optical fibre transmission system. It is shown that these devices can be described as multidimensional systems, the characteristics of which are given in matrix form. This enables forming up to N informative parameters characterising the course of the process.
Effect of pressure on the elemental composition and magnetic properties of lithium-doped ZnO thin films
Authors: Kuz’mina A S , Kazantsev A V , Kuz’min M P
Published: November 2020

The paper presents the results of a study of the effect of pressure in a vacuum chamber on magnetic characteristics of Zn1-xLixOy thin films obtained by pulsed laser deposition on sapphire substrates as well as the results of elemental analysis across thickness of the samples. Secondary ion mass spectrometry demonstrated that the method of pulsed laser deposition allows for the synthesis of zinc oxide based thin films characterized by a high uniformity of elemental composition across thickness. It was established that different pressure in the vacuum chamber affects the number of oxygen vacancies in thin films, but not the distribution of elements across thin film thickness. Weak room temperature ferromagnetism was observed in Zn1-xLixOy thin films synthesized at a pressure of 3.15·10−5 Torr; it increases upon the increase in lithium concentration. Interstitial lithium atoms are considered to play a important role in its occurrence. The results of the study allow for expansion of the body of knowledge about the nature of room temperature ferromagnetism in zinc oxide based thin film structures as well as for developing technologies for high-quality thin film materials for recording and storing information.
Models of diffusion of emergency emissions from railway tanks with petroleum products
Authors: Kulneva V V , Zvyagintseva A V , Sazonova S A , Akamsina N V
Published: November 2020

In the event of the destruction or explosion of tanks with oil products, basic schemes for the distribution of pollutants have been developed in the area of a technical stop of a train at a distance of up to 1000 meters in all directions, taking into account all structures located in the study area. On the basis of an analytical assessment of the consequences of an emergency, it was shown that in the event of the destruction or explosion of railway tanks with oil products, from 90 to 95 % of economic and public facilities will be located in the zone of exceeding the MPC by 2-300 times. The limits for exceeding the permissible concentrations of polluting components in the parking area and flushing of tanks intended for the transit of oil and oil products have been established. The greatest excess will be at a distance of 300 meters from the tank car parking, where, for all priority polluting components, it is possible to exceed the MPC by 300 times. For a distance from the source of emissions more than 1000 meters in each direction of light, the calculation of the maximum permissible concentration of polluting components was performed.
Simulation of the fuel gas and raw material supply system to the furnace of the atmospheric oil distillation unit and development of a computer training software
Authors: Shulaeva E A , Pavlov V B , Burdov A E
Published: November 2020

Software products designed to simulate technological processes are currently very relevant, since they help to prepare workers in advance for difficult production situations. It has been proven that simulation systems can improve design efficiency by 20% with quality workflow management tools, and capital cost savings of up to 30% can be achieved with the right materials for security design. Currently, there are various software and hardware tools for computer modeling of technological objects, selection of technological modes and adjustment of control loops. These tools include Unisim. Unisim is a unique software package from Honeywell that combines a variety of products, including an extensive list of components and thermodynamic properties packages, used to simulate complex process systems.
Analytical evaluation of equivalent energy loss in causal frequency selection filters in the processing of quadrature amplitude modulation signals
Authors: Andronov V G , Mukhin I E , Babanin I G , Sevryukov A E , Koptev D S , Chuev A A
Published: November 2020

In this paper, we consider the need to assess the influence of the parameters of a linear time-invariant causal system in the processing of quadrature amplitude modulation signals of various positionality and constellation diagram irregularity. We describe the equations that determine the energy balance of a radio line and the total losses arising in route. We have developed mathematical expressions to assess the equivalent energy losses caused by the irregularity of amplitude-frequency and phase-frequency characteristics of the frequency selection filters. We propose a procedure to assess the non-noise losses in the frequency selection filters when designing high-speed radio systems for transmitting the information.
Method for controlling the cognitive systems functioning in non-equilibrium stability mode
Authors: Antsyferov S S , Fazilova K N , Rusanov K E
Published: November 2020

Cognitive systems as self-organizing systems have a number of special properties, including the ability to function in a mode of non-equilibrium stability, which, in turn, provides the possibility of self-organization of the system by transforming its structure. In this case, one of the most important is the task of monitoring the functioning of the system in the mode of non-equilibrium stability. The development of a method for controlling the functioning of cognitive systems in the mode of non-equilibrium stability is based on the previously proposed model of non-equilibrium stability, which assumes a probabilistic assessment of the effectiveness of the structural elements of the system. To assess the effectiveness of structural elements, a method based on the principle of comparative preference is proposed. The method is implemented using an appropriate algorithm, which made it possible to numerically simulate it in order to determine the accuracy of evaluating the effectiveness of structural elements. Modeling has shown that the accuracy of the assessment depends on the number of structural elements and on the number of performance indicators. As a result of the simulation, the areas of non-equilibrium stability of the system functioning were determined for a different number of structural elements and the probability of their effectiveness. The proposed control method makes it possible to determine the position of the area of non-equilibrium stability in the phase space of the system functioning and, on the basis of this, evaluate both the current state of the system and predict the possible state of the system when it leaves the region of non-equilibrium stability.
On the solution of some types of optimization problems
Authors: Tikhanychev Oleg V
Published: November 2020

There is a necessity to optimize resources and efforts when it comes to solving almost any organizational management problems. The urgency and relevance of this problem are determined by the fact that these resources are often expressed by integer values, and integer optimization methods may not be used for all types of problems. Consequently, the problem arises in rounding the results obtained through non-integer optimization methods to give a physical meaning to the resulting solution. To solve this problem, we propose to use a compensation approach and take into account that in the vast majority of cases distribution takes place with reservation, and a reserve magnitude is set relatively randomly. As a result, after using reservation errors as a margin for rounding optimization results, it becomes possible to compensate for one uncertainty at the cost of another. Based on the analysis of a typical resource allocation problem, the article synthesizes an algorithm for using the proposed approach and describes some characteristics of its application. The proposed approach has been tested in the solution of a number of applied management automation problems and has confirmed its functionality.
Selection of the optimal stage of the group’s life cycle for its transformation into a network structure based on the plasticity coefficient
Authors: Novikov S V , Komarova N V , Dadyan K E
Published: November 2020

The article shows the relevance of the completed study. The goal is identified, the decomposition of it is carried out, and other components of the research logical structure are determined. There are hypothesis and methodological foundations of the study. A model presented allows choosing the optimal stage of transformation as the stage of the project’s work life cycle, and the stage of the general scheme of the project group life cycle.