bluuebunny/crossref_metadata_2025_split huggingface dataset:
Grouping sparse filtering: a novel down-sampling approach toward rotating machine intelligent diagnosis in 1D-convolutional neural networks
Authors: Wang Pengxin , Song Liuyang , Wang Huaqing , Han Changkun , Guo Xudong , Cui Lingli 
Published: January 2022

Abstract
Convolutional neural networks (CNNs) have weight-sharing and feature-learning abilities, and can efficiently and effectively be used for the health monitoring of industrial equipment. However, the pooling operation in a typical CNN can cause the loss of valuable impulse features during data down-sampling. We propose grouping sparse filtering (GSF) to overcome this problem. Instead of using a pooling operation, the GSF splits the channels of features obtained after convolution into equal-length groups. A feature selector with a feature aggregation function based on the channel importance factors and a lasso constraint is used to filter the groups to perform down-sampling. The GSF method preserves the impulse features due to the block sparsity of the vibration signal. Theoretical analysis demonstrates that the GSF has a similar computational complexity to using a pooling layer in a CNN for the same number of layers. Two experimental studies were conducted using data from a laboratory test and industrial environments. The experimental results show that the 1D-CNN with GSF provides better performance for retaining the impulse features of the rotating machinery signals and higher fault identification accuracy than a CNN with a pooling layer.
Nanostructured transition-metal phthalocyanine complexes for catalytic oxygen reduction reaction
Authors: Chen Siyu , Xu Zhanwei , Li Jiayin , Yang Jun , Shen Xuetao , Zhang Ziwei , Li Hongkui , Li Wenyang , Li Zhi 
Published: January 2022

Abstract
Oxygen reduction reaction (ORR) plays a key role in the field of fuel cells. Efficient electrocatalysts for the ORR are important for fuel cells commercialization. Pt and its alloys are main active materials for ORR. However, their high cost and susceptibility to time-dependent drift hinders their applicability. Satisfactory catalytic activity of nanostructured transition metal phthalocyanine complexes (MPc) in ORR through the occurrence of molecular catalysis on the surface of MPc indicates their potential as a replacement material for precious-metal catalysts. Problems of MPc are analyzed on the basis of chemical structure and microstructure characteristics used in oxygen reduction catalysis, and the strategy for controlling the structure of MPc is proposed to improve the catalytic performance of ORR in this review.
Advanced three-dimensional hierarchical porous α-MnO2 nanowires network toward enhanced supercapacitive performance
Authors: Su Xiaohui , Liang Zicong , He Qingqing , Guo Yanxin , Luo Gaodan , Han Shengbo , Yu Lin 
Published: January 2022

Abstract
Hierarchical α-MnO2 nanowires with oxygen vacancies grown on carbon fiber have been synthesized by a simple hydrothermal method with the assistance of Ti4+ ions. Ti4+ ions play an important role in controlling the morphology and crystalline structure of MnO2. The morphology and structure of the as-synthesized MnO2 could be tuned from δ-MnO2 nanosheets to hierarchical α-MnO2 nanowires with the help of Ti4+ ions. Based on its fascinating properties, such as many oxygen vacancies, high specific surface area and the interconnected porous structure, the α-MnO2 electrode delivers a high specific capacitance of 472 F g−1 at a current density of 1 A g−1 and the rate capability of 57.6% (from 1 to 16 A g−1). The assembled symmetric supercapacitor based on α-MnO2 electrode exhibits remarkable performance with a high energy density of 44.5 Wh kg−1 at a power density of 2.0 kW kg−1 and good cyclic stability (92.6% after 10 000 cycles). This work will provide a reference for exploring and designing high-performance MnO2 materials.
A mathematical model of cavitation behaviour in a single-ended magnetorheological damper: experimental validation
Authors: Kang Byung-Hyuk , Kim Bo-Gyu , Jung Dongsoo , Choi Seung-Bok 
Published: January 2022

Abstract
This paper proposes a mathematical model of the cavitation behavior to occur in a single-ended magnetorheological (MR) damper (MRD), and the effectiveness of the model is validated through the comparison with experimental results. Several causes of the cavitation behavior of MRD are discussed with different conditions of the initial pressure of the gas chamber and the piston stroke speed. The model to capture the cavitation behavior is then formulated considering differential equations for gas volume, internal pressure, ideal gas law, and bulk modulus of MR fluid. To calculate the flow rate, which is difficult to solve from the differential equations, the model is approximated as a nondimensional equation the parameters of the yield stress and pole length. Subsequently, the field-dependent damping force of MRD is computed using the gaseous cavitation model and nondimensional equation. To validate the proposed cavitation model, a single-ended MRD is designed, manufactured, and tested. It is observed that the damping force characteristics under cavitation are revealed to be much different from those under regular operation without cavitation. More specifically, it is hard to calculate the dissipation energy and hysteretic damping due to highly nonlinear characteristics with respect to the stroke and velocity. However, the proposed model can fairly capture the cavitation behavior showing an excellent agreement between simulation and experiment. In this work, to confirm the internal influence of MRD by cavitation, which is difficult to confirm experimentally, the changes of the pressure distribution and the gas-to-liquid volume ratio are analyzed through the simulation of the nondimensional equation. In addition, the bubbles representing the cavitation behavior are visually observed from the lower chamber of MRD.
Hypercritical Accretion for Black Hole High Spin in Cygnus X-1
Authors: Qin Ying , Shu Xinwen , Yi Shuangxi , Wang Yuan-Zhu 
Published: January 2022

Abstract
Recent observations of AdLIGO and Virgo have shown that the spin measurements in binary black hole (BH) systems are typically small, which is consistent with the predictions by the classical isolated binary evolution channel. In this standard formation channel, the progenitor of the first-born BH is assumed to have efficient angular momentum transport. The BH spins in high-mass X-ray binaries (HMXBs), however, have consistently been found to be extremely high. In order to explain the high BH spins, the inefficient angular momentum transport inside the BH progenitor is required. This requirement, however, is incompatible with the current understanding of conventional efficient angular momentum transport mechanism. We find that this tension can be highly alleviated as long as the hypercritical accretion is allowed. We show that, for a case study of Cygnus X-1, the hypercritical accretion cannot only be a good solution for the inconsistent assumption upon the angular momentum transport within massive stars, but match its other properties reported recently.
Estimation of rotor and main bearing loads using artificial neural networks
Authors: Loriemi A. , Jacobs G. , Bosse D. 
Published: January 2022

Abstract
Wind energy is one of the most important technologies for a climate-neutral energy supply. However, the premature failure of wind turbines due to unknown loads leads to a reduction in competitiveness compared to other energy sources. Here, load monitoring systems can make a significant contribution to the prevention of such failures. Most load monitoring systems for wind turbines focus on strain signals of structural components as the tower, main shaft or the rotor blade root. Based on these signals, axial forces, torsion or bending moments, which are acting on these components, are calculated. But this provides only partial information about the complete load situation, as transverse forces are not considered. Other methods use simulation or measurement data to train artificial neural networks (ANN) to estimate damage-equivalent loads acting on a wind turbine. However, this approach is accompanied by a loss of information, because the individual load components are condensed to an equivalent. In this work a method is presented that enables a measurement of rotor and main bearing loads considering all their individual load components. For this purpose, an ANN is trained with elastic multibody simulation (eMBS) data. Based on displacement signals, acting rotor and main bearing loads are estimated. The results show that even with consideration of nonlinearities, including nonlinear stiffness curves and bearing clearances, an appropriate accuracy can be achieved using the method presented.
The influence of atmospheric stability on wind turbine performance: a quantitative study using measurement from SCADA system and mast
Authors: Do Minh-Thang , Li Ru , Jiang Zixiao 
Published: January 2022

AbstractIn the wind resource assessment, to estimate the energy potential of a site, engineers use the manufacturer power curve. This power curve is an ideal relationship between the wind speed of an ideal wind flow and the output power of the designed turbine. The industry standard IEC 61400-12-1 establishes some requirements to measure such power curves. In the real condition, with the same value of wind speed, a large range of output power could be obtained. This difference may be caused by atmospheric stability, which affects wind profiles across the turbine blades. This study focuses on the influence of atmospheric stability on wind turbine performance in real condition. It is shown that the atmospheric stability calculated using the standard wind measurements from a conventional weather mast could be applied for every turbine in a medium size wind farm (33 wind turbines). This influence is very sensitive to the shape factor k of the wind speed distribution. The loss of AEP between neutral/stable conditions could be neglected while the loss of AEP between unstable/stable conditions is significant and depends on the average wind speed. In practice, it is possible to consider this influence during the wind resource assessment study while knowing the percentage of each atmospheric stability condition and the distribution of wind speed on the site.
Dynamic load balancing algorithm for continuum mechanics problems with essential redistribution of workloads among the processes
Authors: Karpeev Artem 
Published: January 2022

Abstract
A number of important applications have computational requirements that vary over time inan unpredictable way. For the effcient usage of distributed-memory parallel systems when solving such problems a new dynamic load balancing algorithm is suggested and explored. It allows to raise the load efficiency for model task up to 70% with balancing frequency v
bal ≈cnbv0:1.
Evaluation Method of Partial Discharge Evolution Degree of Cable Intermediate Joint
Authors: Ou Xiaomei , Li Guowei , Chen Jiayang , Liu Yijun , Zhang Yin , Liang Nianbai 
Published: January 2022

Abstract
The evolution process of partial discharges generated under typical air gap defects of 10kV XLPE cable intermediate joints was experimentally studied by the built experimental model. In the experimental process, five characteristic parameters representing the severity of partial discharge are extracted, and their change trends with time are analyzed, and the value ranges of various characteristic parameters are divided. Using BP neural network algorithm, the nonlinear relationship network between PD severity and multiple characteristic parameters is established, and the evaluation system of partial discharge severity of cable intermediate joint is constructed. Finally, the correctness and effectiveness of the method is verified by the example testing.
Coordinated Control Strategy of Emulated Inertia and Damping for Grid-Forming Energy Storage Considering Capacity Characteristics
Authors: Li Ting , Tang Quan , Zhu Ke , Du Xinwei , Li Bo , Liu Fang , Zhen Tianwen 
Published: January 2022

Abstract
The grid-forming control method of the emulated synchronous generator can enhance the response capability of the electricity frequency and enhance the anti-interference capability of the new energy system. But the system frequency regulation ignores the fact that the active power of multiple energy storage is not proportional allocation, which can decrease the stability of the system. To solve this problem, a grid-forming control method of emulated rotational inertia and damping characteristics for multiple energy storage considering capacity characteristics is proposed. First, the control construction of the grid-forming inverter is presented, and the mathematical model of the grid-forming control of the emulated synchronous generator is established. Secondly, the frequency change and frequency deviation are introduced into the grid-forming control, and the grid-forming control method of emulated inertia and damping coefficient is designed. Each energy storage obtains the emulated rotational inertia and damping value through the real-time state of charge, which adjusts the frequency response of the power grid and active power of the distributed electricity storage system. And this method can realize proportional allocation according to the capacity of electricity storage system. Finally, the simulation analysis results of Matlab/Simulink simulation model is carried out to verify the effectiveness and feasibility of the proposed grid-forming control method.
Overview of High-voltage Large-capacity DC Transformer
Authors: Chen Meng , Li Yan , Bai Jinyang , Feng Gang , Peng Yujin , Guo Jiangzhen 
Published: January 2022

Abstract
DC transformer is the core equipment to realize the convergence and transmission of new energy such as solar energy, wind energy, etc. It also plays a key role in the construction of large-scale DC power grid in the future. Therefore, DC transformer has a broad application prospects in the future energy Internet era. This paper briefly summarizes the current research on DC transformer at home and abroad, and also summarizes the current research on DC transformer in the future. On the basis of the basic principle of decomposing DC transformers, the characteristics and applications of common DC transformers are classified and the problems to be solved are summarized.
Fault Diagnosis of Transmission Lines Based on High Frequency Electromagnetic Spectrum Distribution Characteristics
Authors: Miu Zhongling , Wu Nian , Yao Junwen , Ke Xianbin , Liu Pingyuan , Bie Shiguang 
Published: January 2022

Abstract
According to the recognition of the fault of the transmission line, the traditional methods of manual detection, image recognition and infrared observation can not distinguish the defects, but the line produces the electromagnetic interference level change is its produces the flaw the important characteristic. Therefore, the electromagnetic interference (EMI) can be used as a parameter in the inspection of high-voltage transmission line. this paper studies the correlation between the fault of the transmission line and the distribution of the high frequency electromagnetic spectrum, by analyzing the level change of the electromagnetic interference between the normal wire and the broken wire, the defects such as the old wire, the broken wire and the serious corrosion, which can not be distinguished by image, are detected, it provides a strong technical support for the condition assessment of transmission line conductors.
Research on Cloud-edge Interaction Technology of Power Internet of Things Based on MQTT Protocol
Authors: Liu Qing , Hou Jixin , Yan Yu , Wang Xiaochao , Liu Xiaohu 
Published: January 2022

Abstract
Under the development trend of power industry with energy Internet as the core, with the update of information technologies such as intelligent devices, mobile communication technology and cloud computing, the application of Internet of things (IOT) in power industry has developed from the concept of specific link service to the application of large-scale market. The core of the next power Internet of things construction is to build an open IOT management platform to realize the ubiquitous access of massive devices, the deep sharing of cross-professional data, business integration, and the provision of precise user services. Based on this, an industrial alliance will be formed and a power industry ecosystem with power grid enterprises as the core will be established. At present, the application of power Internet of things in China is still in the primary stage of research and application. The research on what the IOT management platform should manage and the interaction technology between the IOT management platform and edge agents is still in the stage of exploration and pilot. This paper mainly studies the cloud-edge interaction technology, including the interaction object, process and message format. Then it formulates a set of cloud-edge format specifications to meet the requirements of the platform’s own management and business system message transmission, so as to provide reference for the cloud-edge interaction of power Internet of things.
Short-term Load Prediction Based on the Combination of K-means and Random Forest
Authors: Chai Weiwei , Qin Lei , Dong Haiying , Sun Chunshan , Shen Wei , Qiao Shiyun 
Published: January 2022

Abstract
Aiming at the problem that the power supply and distribution system runs at low load rate for a long time and wastes capacity due to the expansion of the power supply and distribution system, a short-term load forecasting method combining K-means and random forest is proposed. The proposed method divides power users into four categories based on electricity behavior, based on which the corresponding category load data is selected as the input sample of the random forest model to obtain short-term load prediction results. Example analysis shows that this method can ensure the rapid clustering accuracy, and effectively realize the short-term prediction of power load based on the random forest, to achieve the purpose of improving the load rate.
A Real-time Detection Framework for Abnormal Devices in the Power Internet of Things
Authors: Miu Weiwei , Zhang Zhen , Wang Xiaochao , Hou Jixin , Sun Yunan , Liu Qing , Zeng Zeng 
Published: January 2022

Abstract
Power Internet of Things [1] is the application of Internet of Things in smart grid. By effectively integrating power system and communication infrastructure resources, the information level of power system and the utilization rate of existing resources are improved. Thus, important technical support is provided for power grid generation, transmission, transformation, distribution and electricity consumption. The Power Internet of Things contains a large number of terminal equipment, which involves the data interaction frequency of 100 million levels per second. Therefore, how to avoid the access of invalid data is an important topic in the construction process of power Internet of Things. In response to the detection requirements of abnormal devices, this paper, based on existing Intelligent IoT System architecture, designs a real-time detection framework that integrates a two-level control mechanism, which is universal, extensible, and supports multiple protocols, using the data interaction specifications formulated by the State Grid Corporation as the basic detection basis. As a result, the framework realizes the effective interception of abnormal devices in power Internet of Things.
Research on the Detection Technology of Audible Noise Sources of UHVDC Transmission Lines
Authors: Lv Jianxun , Zhao Penghui , Yuan Haiwen , Liu Xinyu 
Published: January 2022

Abstract
In order to directly and deeply study the law and characteristics of audible noise sources of UHVDC transmission lines, a method of detecting the sound intensity vector at the source position in the bundle conductor is proposed. First, the structure of the sound intensity detection device is designed, and the method for calculating the sound intensity vector is deduced based on this prototype. Then, the simulation of the electric field distribution on the surface of the device in the UHV extreme electric field environment is carried out. The change of the maximum electric field intensity and the influencing factors are obtained, which proves that the proposed detection device has the adaptability of the UHV electric field environment. Finally, the measurement error of the proposed detection device is analyzed. The results show that in the frequency range of 100∼5000Hz, the theoretical error of the sound intensity vector components and the total sound intensity level obtained by the measurement device in three directions are less than 2dB, which can meet requirements of sound intensity detection at the sound source.
Development and Application of Downhole Chokes with Pressure and Temperature Monitoring Function
Authors: Chen Jiaxiao , Tan Jinfeng , Yu Fan , Chen Ke , Jiang Mi 
Published: January 2022

Abstract
For wells employing downhole choking technology, the traditional well testing methods no longer accommodate the requirements for dynamic monitoring of production performance due to the existence of downhole chokes. On one hand, the wellhead pressure data of gas wells with downhole chokes can neither truly reflect the actual situation of gas wells nor directly provide effective data for the dynamic gas well performance analysis. On the other hand, because of the calculation error with the nozzle flow pressure drop in the calculation method, the calculation error of wellbore pressure is significant, resulting in failure of the annulus liquid level method to carry out monitoring as most wells are installed with packers. In this regard, the downhole choke with pressure and temperature monitoring function has been independently developed by combining the production features of the wells with downhole choking technology. Under the condition of no change of tubing string, no use of cable running and no impact on gas well production, the steel wire operation was implemented, achieving the downhole choking production of gas wells and the monitoring of pressure and temperature prior to the choking operation, thus to establish a wellbore pipe flow prediction model and a nozzle flow model. Through the field application and evaluation, the error between the calculation results of the flow model and the field measured data is controlled within 5%, which provides a theoretical calculation basis for the accurate pressure prediction of wells using choking technology as well as technical support for the dynamic analysis of downhole choking gas wells.
Improved Anomaly Detection Based on Image Reconstruction and Global Template Features for Industrial Products
Authors: Tang Huixiong , Hu Guanghua , He Wenliang , Tu Qianxi 
Published: January 2022

Abstract
Anomaly detection in industry applications is a challenging problem when negative (defective) samples are unavailable, especially in the case where there are missing parts or foreign objects occupied a relatively large region. Conventional reconstruction-based approaches cannot guarantee the restored image being a normal one, leading to poor segmentation results. In this work, we propose an unsupervised anomaly detection approach to tackle the problem of large-area anomaly detection by incorporating global template features into an Auto-Encoder like reconstruction model. In particular, our model infers the value of each pixel based on both the surrounding local-neighborhood information and the global information encoded at the same pixel position. During the reconstruction phase, any abnormal features are then replaced with normal ones, avoiding over-reconstruction of large-area abnormalities. The experimental results in comparison with other methods demonstrate its effectiveness for industrial anomaly detection.