bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of dynamo magnetic fields on observational properties of accreting millisecond X-ray pulsars
Authors: Belay Zeleke Dugasa , Belay Tessema Solomon , Humne Negu Seblu 
Published: February 2021

Abstract
In this paper, we have investigated accreting millisecond X-ray pulsars, which are rapidly rotating neutron stars in low-mass X-ray binaries. These systems exhibit coherent X-ray pulsations that arise when the accretion flow is magnetically channeled to the stellar surface. Here, we have developed the fundamental equations for an accretion disk around accreting millisecond X-ray pulsars in the presence of a dynamo generated magnetic field in the inner part of the disk. We have also formulated the numerical method for the structure equations in the inner region of the disk and the highest accretion rate is enough to form the inner region of the disk, which is overpowered by radiation pressure and electron scattering. Finally, we have examined our results with the effects of dynamo magnetic fields on accreting millisecond X-ray pulsars.
Decoding and interpreting cortical signals with a compact convolutional neural network
Authors: Petrosyan Artur , Sinkin Mikhail , Lebedev Mikhail , Ossadtchi Alexei 
Published: February 2021

Abstract

Objective. Brain–computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery. Approach. We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. Main results. We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task. Significance. We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.
Did a skillful prediction of near-surface temperatures help or hinder forecasting of the 2012 US drought?
Authors: Kam Jonghun , Kim Sungyoon , Roundy Joshua K 
Published: February 2021

Abstract
This study aims to understand the role of near-surface temperatures in predicting US climatic extremes using the North American Multi-Model Ensemble (NMME) system. Here, the forecasting skill was measured by anomaly correlation coefficient (ACC) between the observed and forecasted precipitation (PREC)/2-meter air temperature (T2m) anomalies over the contiguous United States (CONUS) during 1982–2012. The strength of the T2m–PREC coupling was measured by ACC between observed PREC and T2m or forecasted PREC and T2m over the CONUS. We also assessed the NMME forecasting skill for the summers of 2004 (spatial anomaly correlation between PREC and T2m: 0.05), 2011 (−0.65), and 2012 (−0.60) when the T2m–PREC coupling was weaker or stronger than the 1982–2012 climatology (ACC: −0.34). We found that most of the NMME models show the bias of stronger T2m–PREC coupling than the observed coupling over 1982–2012, indicating that they failed to reproduce the interannual variability of T2m–PREC coupling. Some NMME models with skillful prediction for T2m show the skillful prediction of the precipitation anomalies and US droughts in 2011 and 2012 via strong T2m–PREC coupling despite the fact that the forecasting skill is year-dependent and model-dependent. Most of the NMME models show the limited seasonal forecasting skill of the PREC surplus from active Atlantic tropical cyclones in the summer of 2004 and thus fail to reproduce weak T2m–PREC coupling. Lastly, we explored how the role of sea surface temperatures in predicting T2m and PREC. The findings of this study suggest a need for the selective use of the current NMME seasonal forecasts for US droughts and pluvials.
Research on Optimization of PAA Method Based on Fault- Tree Analysis
Authors: Wang ZHIWU , Qing CHEN , Chen WEN 
Published: February 2021

Abstract
The third generation of nuclear power units with million kilowatt capacities which are highly developed now are required to reach the advanced technical level of the third generation of nuclear power on the basis of meeting relevant reliability requirements of URD (Utility Requirement Document) and EUR (European Utility Requirement). Forced unavailability not only influences economic indicators of nuclear power units like availability, but also seriously threatens safe operation of units. However, at present, it does not take into account the requirements of unavailability in China. In this paper, based on Probabilistic Availability Assessment (PAA) method, an improved method based on Fault-Tree Analysis is proposed to solve the problem that the unit shut down due to the dual or multiple failure of the equipment. And the Chemical and volume control systems RCV is taken as an example to show that the results of the fault tree method are more accurate and reliable, and the Probabilistic Availability Assessment method is optimized. It has a good reference for the design and development of new nuclear power units, system configuration, equipment selection and so on, and also provides the reference for the readers.
Research on Organizational Reliability Capability System of Electromechanical Product Manufacturing Enterprise
Authors: Mu Zong-Yi , Ran Yan , Zhang Gen-Bao , Yang Xin 
Published: February 2021

Abstract
Against the background of Industry 4.0, more and more attention have been paid to reliability of the product. However, in current researches on the reliability assurance of the product, most of the researches focus on the product itself and ignore that the manufacturing enterprise is the executor of the product’s reliability assurance, it is necessary to understand the current situation of enterprises’ organizational reliability capability (ORC). Therefore, this paper establishes the decomposition model of the ORC to understand its composition and the evaluation indicators system is established, then the principal components analysis of indicators are conducted to simplify the indicators system, lays a foundation for the subsequent work such as improvement of the ORC or establishment of the expert system.
Kuznets Curve based Analysis on the Relationship between Economic Growth and Environmental Quality in Beijing
Authors: Li Ying-chun , Chi Yuan-ying , Ni Xiao-ru , Liu Xiang-dong , Peng Rui 
Published: February 2021

Abstract
It is well known that the economic growth of big cities may cause risk on the environment, thus it is essential to study the relationship between economic growth and environmental quality. Particularly, this paper establishes the environmental Kuznets curve measurement models between Beijing’s economic growth and environmental pollution levels using 2000-2017 Beijing time series data. Economic development level is measured by GDP per capita. The level of environmental pollution is measured by the discharge of industrial waste water, the discharge of industrial waste gas, the production of industrial solid waste, and the discharge of industrial sulfur dioxide. The empirical results show that the environmental Kuznets curve of Beijing’s economic growth and industrial wastewater discharge has an inverted N-type characteristic. Second, the economic Kuznets curve of Beijing’s economic growth, whether it is related to the amount of industrial exhaust emissions or the amount of industrial solid waste produced, has an inverted U-shaped curve. In addition, the environmental Kuznets curve of Beijing’s economic growth and industrial sulfur dioxide emissions has linear characteristics. Based on the characteristics of these environmental Kuznets curves, this paper finds that there is a relatively good relationship between Beijing’s economic development level and the environment relationship after a comprehensive analysis. On this basis, this paper puts forward the suggestions of Beijing’s environmental Kuznets curve to Beijing’s good environmental level.
A Multi-objective Optimization for Simultaneous Determination of Spare Part Numbers and Imperfect Preventive Maintenance
Authors: Zhao Xiao , Yang Jianhua , Han Mengying 
Published: February 2021

Abstract
Preventive maintenance (PM) has played an increasingly important role in manufacturing. Many PM strategies have been proposed to determine optimal decisions with the assumption of perfect PM, which is seldom happened in reality. Besides, a great many studies have mainly considered single-objective optimizations instead of multi-objective issues. In this paper, we propose a model simultaneously determining spare part numbers and imperfect PM intervals based on multi-attribute utility theory (MAUT), one of the multi-objective decision making theories. Reliability, cost, and supply risk are seen as three main conflicting attributes. A numerical application is illustrated based on the data gathered from a steel firm. Sensitivity analysis is carried out to show the impact of parameters. The conclusion is shown that the model consists of facts.
A bias-corrected Nelson-Aalen estimator
Authors: Jiang R 
Published: February 2021

Abstract
The starting point to model an incomplete dataset of a non-negative random variable is usually to estimate its reliability function using a non-parametric reliability estimator. Two such estimators are Kaplan-Meier estimator and Nelson-Aalen estimator. Their performances have been compared and some drawbacks (e.g., biasness) have been identified in the literature. However, improvements on them are scarce. This paper aims to fill this gap by proposing a simple and almost unbiased estimator. This estimator is a weighted average of the Nelson-Aalen reliability estimates at two successive time points. An application of the proposed estimator in model selection is discussed and a real-world dataset is analyzed to illustrate the appropriateness and usefulness of the proposed estimator.
Reliability life analysis of blades of twin-shaft mixer based on wear failure
Authors: Li Nan-Xi , Cheng Hai-Ying , Liu Jiang 
Published: February 2021

Abstract
As an important part of mixing material, the study on the reliability life of the blade of mixer is very necessary. According to the Arcard wear formula, the mathematical model expression of the wear amount and the load on the blades of the twin-shaft mixer in a single cycle is established. The discrete element method is used to obtain the load on the blade in the mixing process. The law of load distribution of mixer blades is obtained by Lilliefers method. Furthermore, Lagrange interpolation method is used to analyze the static simulation results of the mixing blade, and the total wear amount when the blade reaches the critical thickness is obtained. By establishing the functional relationship between wear and blade life, Monte Carlo method is used to simulate the load distribution law to predict the reliability life of the mixer blade. The results show that the reliability of the life of the mixer blade is 85.02%.
ARIMA-Based Aging Prediction Method for Cloud Server System
Authors: Meng Haining , Shi Yuekai , Qu Yilin , Li Junhuai , Liu Jianjun 
Published: February 2021

Abstract
Long-running software system tends to show performance degradation and sudden failures, due to error accumulation or resource exhaustion over time. This phenomenon is usually called software aging. Software aging is an important factor that influences software reliability. This paper presents a prediction method to investigate software aging in an OpenStack cloud system. At first, the performance data in an OpenStack cloud system is monitored and collected. Then, an autoregressive integrated moving averages (ARIMA) approach is used to predict the performance data. Finally, the experimental results and statistical analysis of collected data validate the presence of software aging in the OpenStack cloud system.
A Model of Resource Allocation for Intercepting the Missile of Mass Destruction Considering Detection Contest
Authors: Qu Li , Han Chenjing , Li Yingchun , Ma Ye , Gao Kaiye , Peng Rui 
Published: February 2021

Abstract
With the development of military technology, missiles have played an essential role in modern war. Particularly, missiles have become main carriers of the weapons of mass destruction to strike the objective in a long distance. Thus, it is essential for the defender to intercept the missiles launched by the attacker. However, the intelligence about the attacking missiles obtained by the defender is usually incomplete, which depends on the detection contest where the defender tries to detect the attacking missile and the attacker tries to disturb the detection. This raises a problem about how to balance a certain amount of resources onto the interception missiles and detection contest for the defender in order to minimize the damage caused by attacking missiles. Therefore, considering detection contest, a model of resource allocation for missile interception is proposed in this study. The results show that the defender can obtain the strategy with the lowest damage by adjusting the allocation of resources. This provides an analytical method to calculate the damage caused by missiles of mass destruction under the missile interception considering detection contest.
Information Fusion of Discrete Variables and Continuous Variables Based on D-S Evidential Theory
Authors: Yang H H , Sun H H , Chen H X , Jiang G J , Li C W 
Published: February 2021

Abstract
In reliability analysis of a target, information from various aspects should be analyzed comprehensively. In this process, information fusion plays a key role in integrating and analyzing information from different channels. Aiming at the reliability analysis of discrete variables and continuous variables existing simultaneously, an information fusion method based on Dempster’s rule of combination of D-S evidence theory was proposed to facilitate the reliability analysis by integrating information. Firstly, the continuous variables are discrete by using the continuous variables discretization algorithm based on the probability distribution of the job state with equal expectation scale, and the basic probability distribution of each discrete variable is carried out. Then Dempster’s synthesis rule was used to synthesize the discrete variables. Deng entropy was used as the judgment standard in the synthesis process, so that the entropy of each step of fusion was reduced to ensure the credibility of fusion was increased. Finally, the feasibility and effectiveness of the method are verified by a case.
Fault Diagnosis of Rolling Bearing based on Permutation Entropy Optimized Maximum Correlation Kurtosis Deconvolution
Authors: Liu Ping , Lin Zhonghai , Zhang Min , Gu Yingkui 
Published: February 2021

Abstract
An improved MCKD algorithm was proposed as an adaptive parameter selection approach. In this method, the optimal filter length of the MCKD algorithm was determined by the maximum permutation entropy value and the optimal fault period of the MCKD algorithm was determined by the maximum kurtosis value. The determined optimal filter length and optimal fault period were verified by simulation signals of the bearing as well as fault experimental signals of the inner and outer rings. Results demonstrate that in early fault diagnosis, the improved MCKD algorithm based on permutation entropy can extract faint characteristic information of early fault of rolling bearing effectively. It can highlight fault pulse signals which are covered by noises and get more ideal results than the minimum entropy deconvolution algorithm.
Reliable realization of target source detection technology under laboratory conditions
Authors: Asigen  , Gan Shi Ming , Bao Xiao Yan , Guo Yu 
Published: February 2021

Abstract
Target source detection technology has been widely applied in the fields of military, aviation, and navigation, etc. In order to enable students to master target source detection technology, it is necessary to be applied to the practical education of students. In the paper, one method of planar target source detection based on the time delay estimation and the sensor placed in a line is analyzed. To verify the reliability of the implementation of planar target source detection technology under laboratory conditions, with the aid of INV acceleration sensor, INV signal acquisition instrument and DASP analysis software, the detecting experiment of vibration target source on the plane was carried out by using the target source detection method that has been analyzed. The results of experiment showed that the relative errors of the distance and direction angle between the detected target and each sensor are less than 9%.
Heat reliability analysis of thermoelectric generators (TEG) based on heat transfer process
Authors: Li Xinchun , Cui Lijie , Zhang Liang , Wang Zhongwei 
Published: February 2021

Abstract
Thermoelectric generator (TEG) for output power is generated in different temperature. Its heat reliability is effect on the life length. In this paper a one- dimensional transient heat- transfer model of thermoelectric generator has been proposed and the temperature field of TEG has been given. The reliability model of TEG has been established for analyzing the TEG’s life by considering the TEG structure compositions. The TEG life length has been studied under different heat flux per unit time conditions according to reliability distribution function. The results show the output power of TEG is increased when the temperature of TEG structure is increasing. But, the TEG life length is reduced with increasing temperature. Therefore, the TEG temperature is large effect on the life length of TEG.
Fuzzy FMECA for CNC machine tool spindle system
Authors: Chen Hong-Xia , Gong Yan-Qing , Baosiriguleng  , Qi Wei-Long 
Published: February 2021

Abstract
The spindle systems of the state-of-the-art computer numeric controlled (CNC) machine tools are increasingly important than ever, for that the even distinguished demand of higher efficiency, reliability, and productivity of today’s manufacturing industry lead to the aforementioned system works under higher rotational speed and higher cutting performance. This paper extended the conventional failure modes, effect and criticality analysis by integrating which with fuzzy theory to finitizing a failure analysis of an electric spindle system of a CNC machine tool aims to avoid unwanted failures of the system and achieve safe and reliable operation of the device. Critical components, critical failure modes of the spindle system that call for special attention are ascertained. Root causes of the critical failure modes as well as recommendations e.g. periodical inspection of the bearings as well as robust design for electrical and electronical complements for avoiding their occurrence are obtained.
System reliability analysis of forage crushing machine based on fuzzy FMECA
Authors: Zhai X Y , Zhai Z P , Lan Y Z , Wu Y M , Cheng H Y , Zhang C C 
Published: February 2021

Abstract
This paper aims to address the problems of high failure rate, poor reliability, and incomplete failure data associated with forage crushing machines. First, the failure mode effects and criticality analysis (FMECA) is combined with the fuzzy theory to transform the influencing factors on failure modes into fuzzy variables. Then, the analytic hierarchy process (AHP) is used to determine the weight of each influencing factor, and a comprehensive criticality degree is calculated for each failure mode to determine the key component posing the greatest criticality of disrupting the operation of the equipment. The results of the analysis show that fuzzy FMECA can be used to rank the comprehensive criticality degree of each failure mode. Furthermore, the results are consistent with the criticality ranking of failure modes obtained under actual operating conditions and can therefore be to determine the most critical failure mode and the key component. Fuzzy FMECA also provides valuable reference data for improving the design and system reliability and to effectively reduce the failure rate of forage crushing machines.
A novel ISLE-ESE method for mixed-variable experiment design
Authors: Yang J W , Wu Z P , Wang W J , Li J X , Zhang W H , Sun J B , Zhao H L , Wen Q , Zhen X 
Published: February 2021

Abstract
Discrete-continuous mixed-variable problem is an inevitable problem in engineering design. However, few researches pay attention to discrete variables. In this paper, a novel method is proposed for solving mixed-variable problem by combing the Enhanced Stochastic Evolution (ESE) method and the improved Successive Local Enumeration (SLE) method. The continuous part of design variables is generated first. And then the improved SLE method is utilized to generate the discrete part. Finally, an overall optimization is conducted utilizing ESE method taking the continuous part of design variables as input. Results of numerical tests show that the proposed method performs better in both space-filling property and orthogonality. Besides, the computation effort is also saved compared to the ESE method.
Remaining Useful Life Prediction of r-out-of-n: G System
Authors: Zhao Q , Song Z L , Jiang P , Qian F C , Zhang J W 
Published: February 2021

Abstract
Remaining useful life (RUL) prediction has been gradually concerned in reliability engineering. In addition, r-out-of-n: G system is one of the most typical systems. Therefore, it is meaningful and significant to predict the RUL of r-out-of-n: G system with high precision. Motivated by this problem, relationship between the reliability and the RUL is discussed in this paper. Further, on the assumption that the components are independently exponentially or Weibull distributed, the closed-forms for RUL of r-out-of-n: G system are derived, respectively. For components following more complex distributions, a simulation method has also been provided. The numerical examples prove that the proposed method is rather accurate and efficient, and it is worth learning in engineering practice.