bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Modification of Line Parameters in Distribution Network Considering the Electro-thermal Coordination
Authors: Tianyu Fang , Hengxu Zhang , Fang Shi , Yanruo Hou 
Published: June 2020

Abstract
Compared with the transmission network, the distribution network resistance parameters cannot be ignored. The resistance is closely related to the temperature, and the influence of temperature should be considered in the process of parameter estimation. In this paper, the line resistance is corrected in real time by combining the line temperature, resistance and power flow closely through the heat balance equation. Based on the theory of electro-thermal coordination, a method of temperature resistance correction for distribution network is proposed. This method integrates the heat balance equation and the least square method of state estimation, and proposes an extended Jacobian matrix circuit parameter correction algorithm considering the state vector. Then, this paper derives the deviation of the SCADA measurements, PMU measurements and the line temperature in detail. And the Jacobian matrix is extended to make the line temperature and node state can be calculated at the same time, which effectively corrects the overall line resistance of the distribution network. Finally, based on PSCAD / EMTDC IEEE 33 node distribution network model, this paper obtains the simulation data considering load fluctuation and measurement error and analysis the consistency deviation, which verifies the effectiveness and convergence of the algorithm. The results show that the algorithm significantly reduces the estimation error.
Voltage sag analysis based on cluster analysis and correlation analysis
Authors: Liu Hao , Zeng Qinghui , Liu Shaohui , Wu Zhuojun , Li Lei , Ma Rongrong 
Published: June 2020

Abstract
Based on data mining algorithm, a voltage sag analysis method is proposed, which first performs clustering analysis and then correlation analysis. Firstly, the climatic factors are used to cluster the data, and then the correlation analysis of each cluster is carried out according to the selected voltage sag feature dimensions, finally, the strong association rules are obtained. Through the analysis of the example, it is found that there are some certain correlations between the climatic factors, the voltage sag dimensions and the causes of the voltage sag. These correlations can provide a theoretical basis for the prevention and control of voltage sag.
Model Predictive Current Control of Dual Inverter Fed Open-End Winding PMSM Based on Fast Vector Selection
Authors: Wu Di , Zhu Rui , Chen Jifeng , Hu Jiaquan , Hua Guowu , Wang Ying 
Published: June 2020

Abstract
Aiming at the problem of model predictive current for the dual two-level inverter needs to optimize the objective function of all 19 voltage vectors, which makes the calculation of optimization too large for practical application. A MPCC scheme for open-winding pmsm based on voltage vector preselection was proposed. First, the pre-selection of the voltage vector is performed by calculating the reference voltage vector using the deadbeat solution. And establish a voltage vector based objective function to achieve predictive current control and simplify the control algorithm. Next, the switching frequency constraint is introduced into the objective function to select the optimal switching state and reduce the average switching frequency. Finally, the simulation is verified and the effectiveness of the method is proved.
Study on the reactive power coordinated control in hybrid parallel HVDC system
Authors: Han Yan , Rui Song , Xuan Wang , Shichang Zhao , Wenli Yang , Min Luo 
Published: June 2020

Abstract
Hybrid parallel HVDC system adopts line commutated converter high voltage direct current(LCC-HVDC) and voltage source converter high voltage direct current(MMC-HVDC) parallel structure, which combines the advantages of LCC-HVDC and MMC-HVDC. Based on the existing reactive power control of HVDC system, the reactive power coordination control strategy of hybrid parallel HVDC system is innovatively proposed in this paper. Firstly, the reactive power control of LCC-HVDC and MMC-HVDC are studied respectively. And then the voltage stability of LCC-HVDC fed weak AC system is analyzed. Furthermore, a reactive power coordination control strategy between reactive power compensation device of the converter station and MMC-HVDC is proposed. Finally, based on PSCAD/EMTDC simulation platform, a hybrid parallel HVDC simulation system is built. The reactive power coordination control strategy under steady and transient conditions is simulated and analyzed respectively. The analysis results show that the reactive power coordinated control strategy can suppress the voltage fluctuation of converter bus in steady state. At the same time, the reactive power coordinated control strategy can restrain the commutation failure of LCC-HVDC in the fault of receiving power grid and improve its fault recovery ability.
A Fiber-Optic Current Sensor Based on Fuzzy PI Control
Authors: Diao Zhaowei , Ji Zhongxiao , Liu Mei , Wu Yifei , Wu Yi , Yang Fei 
Published: June 2020

Abstract
The development of power grid has put forward higher requirements on the performance of current sensors. Traditional current sensors already can not meet these needs of grid metering and protection. This paper proposes a fiber-optic current sensor (FOCS) based on fuzzy PI control strategy which combines fuzzy control with PI control. These parameters of fuzzy PI controller can be adjusted online at different stages of the system response. Simulation results show that the FOCS based on fuzzy PI control has a fast dynamic response and no overshoot, compared with FOCS based on traditional PI control. These advantages of accuracy in metering and rapidity in protection of power grid, can be satisfied simultaneously.
Combination Forecasting Model of Daily Electricity Consumption in Summer Based on Daily Characteristic Meteorological Factors
Authors: Wang Ruimiao , Yao Xinyu , Li Chunyan , Hu Bo , Xie Kaigui , Niu Tao , Li Mushui , Fu Jian , Sun Qingsong 
Published: June 2020

Abstract
Daily electricity consumption in summer is affected by numerous meteorological factors. In order to improve the accuracy of daily electricity consumption forecasting, a summer daily electricity consumption combination forecasting model based on daily characteristic meteorological factors is proposed in this paper. Firstly, the relationship between various meteorological indexes and daily electricity consumption is analysed considering the coupling effect and accumulative effect of meteorological factors on daily electricity consumption, and a single forecast model of daily electricity consumption is established. Then the meteorological mapping function is designed to evaluate the impact of each meteorological index on daily electricity consumption, and the weight of each single forecasting model is calculated. Finally, a combination forecasting model of daily electricity consumption in summer is established. The parameters of meteorological mapping function are optimized by adaptive training and virtual forecasting, and solved by genetic algorithm. Taking the daily electricity consumption in Chongqing in the summer of 2018 as an example, the results have shown that the proposed combination forecasting model can effectively improve the accuracy of daily electricity consumption forecasting.
Study on the transformer protection scheme based on double thresholds and waveform similarity
Authors: Liang Yu , Yang Fan , Shi Dan 
Published: June 2020

Abstract
As the key device of power transmission, the power transformer has higher requirements for the protection sensitivity and reliability. However, the operation time of the differential protection is limit to the data window of the Fourier transform algorithm. In addition, the differential protection cannot recognize the slight transformer turn-to-turn fault. Moreover, it may maloperate when the current transformer (CT) is saturated. To solve above problems, a novel transformer protection scheme based on double thresholds and waveform similarity is proposed. By comparing the current waveform similarity with the double thresholds, the external faults, internal faults and CT saturation scenarios can be effectively identified, while the waveform similarity is obtained by calculating the Hausdorff distance of the current at each side of the transformer. The PSCAD/EMTDC based simulation results show that the proposed protection scheme can operate quickly and the operation time is as low as 5.5ms. In addition, it has high sensitivity and can effectively identify 0.5% or higher turn-to-turn faults, and it will not be affected by CT saturation.
A method of automatic generation of daily work report for power dispatching based on RoBERTa
Authors: Li Jie , Zhang Tao , Li Hao , Lu Jixiang , Lu Jinjun , Li Ziqi 
Published: June 2020

Abstract
Power dispatching operators need to read a large number of power dispatching logs every day and arrange them into daily work reports. It takes them a lot of time to do information extraction and transfer that information into work reports. In order to reduce the workload of power dispatching operators, we propose an intelligent method for automatic generation of daily work reports in this paper by adopting RoBERTa, which is an improved version of BERT. With the outstanding performance of RoBERTa in semantic analysis and entity recognition, the workload of power dispatching operators is reduced approximately 50% through our proposed method, which means the efficiency is greatly improved.
A Non-Linear Improved Double-Integral Sliding Mode Controller (IDI-SMC) for Modeling and Simulation of 6 (DOF) Quadrotor system
Authors: Ahmad Irfan , Liaquat Muwahida , khan owais zarin , Malik Fahad Mumtaz 
Published: June 2020

Abstract
Due to recent technological achievements, modeling and simulation of Unmanned Aerial Vehicles (UAVs) such as Quadrotor contracted great potential. In this paper, the Improved Double-Integral Sliding Mode Controller (IDI-SMC) scheme is presented to cope with the problem like chattering and tracking. Under-actuated six Degree Of Freedom (DOF) Quadrotor model is formulated through Newton-Euler-Lagrange (NEL) equations. The idea of design (IDI-SMC) is to implement Double Sliding Mode control with proper coefficient scheduling for switching surface with Hurwitz stability criteria. Furthermore, introducing the strong condition of the sliding surface made the results and Simulations became more efficient with Hurwitz stability for both translational and rotational motion of the 6 DOF Quadrotor system is achieved to show the effectiveness of the proposed design controller.
The optimized layout strategy of ring oscillator network for Trojan detection
Authors: Yang Lian , Yang Wenxiu , Li Huan 
Published: June 2020

Abstract
The method of Trojan detection based on the RON (Ring Oscillator Network) has been widely proposed and has a good effect. However, the method needs to integrate a RON on the chip to sense the voltage drop generated by the Trojan. Currently, the number of ROs (Ring Oscillator) required for a RON has not been discussed, which has a large impact on the additional hardware overhead. Therefore, based on the RON for Trojan detection, we propose an optimized layout strategy that can effectively control the number of ROs integrated into the circuit. By measuring the sensing range that can be perceived by a single RO, the sensing radius of the RO is obtained; then, ROs are placed in the circuit in a way of regular hexagonal lattice to clarify the number of ROs required in the network. Determining the appropriate number of ROs not only reduces the hardware overhead required for on-chip integration, but also ensures the reliability of Trojan detection, ensuring seamless coverage of the circuit with as few ROs as possible.
UFSP-Net: a neural network with spatio-temporal information fusion for urban fire situation prediction
Authors: Jin Guangyin , Zhu Cunchao , Chen Xiaoxuan , Sha Hengyu , Hu Xingchen , Huang Jincai 
Published: June 2020

Abstract
Capturing the dynamics of urban fire situation is a basic but challenging task, which takes an indispensable role in the field of urban security and fire emergency decision. Traditional methods approach the urban fire prediction via stochastic process based on physics or statistics, which may be interpretable but less practical in real applications. Recently, some data-driven models, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Graph Convolutional Neural Network (GCN), seem to be fruitful in capturing spatio-temporal dynamics with massive high-dimension data. In this paper, we process some regional urban fire dataset of recent six years in the fire situation awareness images (FSAIs) and extract pixel-level latent representations with CNNs while GCNs are applied to process some spatial graph structure auxiliary information to obtain graph-level latent representations. And then Urban Fire Situation Prediction Neural Network (UFSP-Net) is formulated as a novel urban fire prediction model, integrating these two different kind of spatial latent representations and RNN structure. In comparison to other traditional algorithms, such as Conv-RNN, UFSP-Net demonstrates its superior prediction performance for multi type urban fire in spatio-temporal scale.
Cattle identification using muzzle print images based on feature fusion
Authors: Sian Cong , Jiye Wang , Ru Zhang , Lizhi Zhao 
Published: June 2020

Abstract
Individual identification of animals is an important means to modernize the livestock industry. In recent years, the research on individual identification of cattle has also received more and more attention. Individual cattle identification is necessary for many important reasons including registration, traceability, production management and animal disease control. Biometric features are unique, which often do not change over time. In this paper, muzzle print is used as biometric feature. The fusion of texture features extracted from Weber Local Descriptor(WLD) and local binary pattern was used to represent individual cattle. Some improvements were made to WLD algorithm. Finally, support vector machine was employed to identify head of cattle from their fusion feature. The proposed method achieved 96.5% identification accuracy.
An enhanced stochastic algorithm for optimal contract capacity of furniture stores
Authors: Tu Chia-Sheng , Tsai Ming-Tang , Huang Chun-Chieh , Su Kuei-Ching 
Published: June 2020

Abstract
This paper integrated the Grey Theory and Particle Swarm Optimization to propose an Enhanced Stochastic Algorithm (ESA) for dealing with the optimal contact capacity of the furniture store. ESA is used to find the minimal total annual electricity bill and simultaneously find the optimal contract capacity under the operational and system’s constraints. The historical demand for a furniture store is first collected in this paper. Grey theory is used to predict the next year demand of every month. Based on the Time-Of-Use (TOU) rates employed by the Taiwan Power Company (TPC), the annual electric bill including basic charge and penalty charge is derived to formulate an objective function. Particle Swarm Optimization (PSO) was adopted to minimize the annual electricity bill and simultaneously find the optimal contract capacity. Practical load consumption data were used to prove the validity of the ESA. Users can able to estimate the contract capacity by themselves and curtail the electricity cost.
Investigations on electrochemical performance of La2NiO4+δ cathode material doped at A site for solid oxide fuel cells
Authors: Wu Xiusheng , Gu Chunye , Cao Jufang , Miao Lina , Fu Chao , Liu Wei 
Published: June 2020

Abstract
In this work, the perovskite compounds La1.5A0.5NiO4+δ
(A = Ca, Sr or Ba), which is one kind of mixed ionic electronic conductors (MIEC) with perovskite structure, was explored as cathode materials for proton conducting solid oxide fuel cells (H-SOFCs). In order to understand how the size of A-site cations affects material properties, the electrical conductivity and the electrochemical performances of La1.5A0.5NiO4+δ
(A = Ca, Sr or Ba) were compared. The results are more interesting. In the middle temperature region (600 °C–700 °C), Ca doping is the best choice among the three alkaline earth elements. In the low temperature region (300 °C–600 °C), Sr-doped single cells Sr-doped single cells have better performance. However, Ba doping easily generates BaCO3 impurities, which reduces the ion transmission capacity and increases the interface resistance, resulting in poor performance of the single cell. This work shows that La1.5A0.5NiO4+δ
(A = Ca and Sr) is the main cathode candidate material for H-SOFC.
Pan-Arctic analysis of cultural ecosystem services using social media and automated content analysis
Authors: Runge Claire A , Hausner Vera H , Daigle Remi M , Monz Christopher A 
Published: June 2020

Abstract
In the Arctic, as in many parts of the world, interactions with the natural world are an important part of people’s experience and are often recorded in photographs. Emerging methods for automated content analysis of social media data offers opportunities to discover information on cultural ecosystem services from photographs across large samples of people and countries. We analysed over 800 000 Flickr photographs using Google’s Cloud Vision algorithm to identify the components of the natural environment most photographed and to map how and where different people interact with nature across eight Arctic countries. Almost all (91.1%) of users took one or more photographs of biotic nature, and such photos account for over half (53.2%) of Arctic photos on Flickr. We find that although the vast majority of Arctic human-nature interactions occur outside protected areas, people are slightly more likely to photograph nature inside protected areas after accounting for the low accessibility of Arctic protected areas. Wildlife photographers travel further from roads than people who take fewer photographs of wildlife, and people venture much further from roads inside protected areas. A large diversity of nature was reflected in the photographs, from mammals, birds, fish, fungi, plants and invertebrates, signalling an untapped potential to connect and engage people in the appreciation and conservation of the natural world. Our findings suggest that, despite limitations, automated content analysis can be a rapid and readily accessed source of data on how and where people interact with nature, and a large-scale method for assessing cultural ecosystem services across countries and cultures.
Machine learning and excited-state molecular dynamics
Authors: Westermayr Julia , Marquetand Philipp 
Published: June 2020

Abstract
Machine learning is employed at an increasing rate in the research field of quantum chemistry. While the majority of approaches target the investigation of chemical systems in their electronic ground state, the inclusion of light into the processes leads to electronically excited states and gives rise to several new challenges. Here, we survey recent advances for excited-state dynamics based on machine learning. In doing so, we highlight successes, pitfalls, challenges and future avenues for machine learning approaches for light-induced molecular processes.
Why choose geriatric medicine? A national survey among French postgraduate medical students
Authors: Prud’homm Joaquim , Corvol Aline , Aguillon Aude , Olivieri Marine , Rousseau Valentine , Somme Dominique 
Published: June 2020

Abstract
Since 2017, geriatric medicine has been available as a postgraduate specialty to French year 7 medical students. We investigated the incentives of the 171 French medical students who opted for geriatric medicine as a postgraduate specialty subsequent to year 6 national qualifying examinations in 2017. A prospective quantitative survey-based study was conducted by means of a questionnaire compiled online and sent by email between December 2017 and May 2018. The questionnaire comprised 43 questions, including 14 single or multiple choice questions, 28 scaled questions evaluating factors of influence using a 5-point Likert scale, and one open-ended contingency question. Of the 171 students, 139 responses were received. The national response rate to this questionnaire was 81.2%. One hundred fourteen students (82.6%) had previous experience of training in geriatric medicine, which for 95 (84.0%) students took place between years 3 and 6 of medical training. This training influenced the choice of 102 respondents (90.2%). Factors reported as having exerted a strong or very strong influence were in particular the rewards of working with older adults; positive personal encounters with older adults in the past; the appeal of interprofessional teamwork; the challenge of cases involving complex diagnostic and therapeutic decisions; the challenge of patients with chronic conditions. The present study is the first to focus on the reasons why French students choose to specialise in geriatric medicine. The results emphasise the importance of training programs in geriatric medicine to promote enthusiasm for this specialty.
Diversity and Political Leaning: Considerations for Epidemiology
Authors: Allen Bennett , Lewis Ashley 
Published: June 2020

Abstract
The positive effects of increased diversity and inclusion in scientific research and practice are well documented. In this issue, DeVilbiss et al. (Am J Epidemiol. 2020;189(10):998–1010) present findings from a survey used to collect information to characterize diversity among epidemiologists and perceptions of inclusion in the epidemiologic profession. They capture identity across a range of personal characteristics, including race, gender, socioeconomic background, sexual orientation, religion, and political leaning. In this commentary, we assert that the inclusion of political leaning as an axis of identity alongside the others undermines the larger project of promoting diversity and inclusion in the profession and is symptomatic of the movement for “ideological diversity” in higher education. We identify why political leaning is not an appropriate metric of diversity and detail why prioritizing ideological diversity counterintuitively can work against equity building initiatives. As an alternative to ideological diversity, we propose that epidemiologists take up an existing framework for research and practice that centers the voices and perspectives of historically marginalized populations in epidemiologic work.
Food for Thought: Opportunities to Improve Diversity, Inclusion, Representation, and Participation in Epidemiology
Authors: Jackson Chandra L 
Published: June 2020

Abstract
DeVilbiss et al. (Am J Epidemiol. 2020;189(10):998–1010) have taken on the noble and worthy cause of improving diversity, inclusion, representation, and participation across the Society for Epidemiologic Research (SER) membership—a reflection/microcosm of society. The objective of this commentary is to underscore the importance of diversity and to offer initiative ideas, which should be centered around inequity stemming from the widespread historical and contemporary maldistribution of power (e.g., decision-making) and resources (e.g., funding) within institutions and organizations. Nonexhaustive strategies could include SER becoming an opportunity and information hub that helps to fill resource gaps. It is also recommended that SER leadership learn from existing associations and scientific initiatives to improve the culture of science in general by equitably incorporating policy, systems, and environmental interventions throughout the career spectrum. Examples include the provision of tools and incentives to address explicit or implicit biases, enhance mentoring skills, and remove predictable barriers (e.g., financial). Explicitly labeling diversity/inclusion efforts should be avoided, and the initiative should be evaluated based on impact rather than intent. Our fates are interconnected, and we can all help increase diversity, inclusion, representation, and participation to improve our science in hopes of equitably improving public health.