bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical Simulation of the anomalous electrodeposition of Nickel-Iron alloy on rotating disk electrode
Authors: El Malki Imane , Alaa Nour Eddine , Khalfi Hamza 
Published: January 2021

Abstract
Electrodeposition was originally used for the preparation of metallic mirrors and corrosion resistant surfaces. In this paper, we study a stationary one dimensional system modelling nickel-iron alloy electrodeposition. This system takes into consideration the following phenomena: dissociation, diffusion, electromigration, convection and deposition of multiple ion species. In this work, we present the finite element discretization of the main model supported with a numerical algorithm to effectively handle the nonlinearities. We compare the obtained numerical result with experimental data reported in the literature and show that our simulation method can predict the characteristic features of the Nickel-Iron system.
Blind Source Separation for Text Mining
Authors: Ghazdali Abdelghani , Metrane Abdelmoutalib , Ourdou Amal 
Published: January 2021

Abstract
Blind Source Separation (BSS) was originally developed for signal processing applications. It has been proven out that Independent Component Analysis (ICA) which is the technique used for separating independent sources, is a powerful tool for analyzing text document data as well, if the text documents are presented in a suitable numerical form. This opens up new possibilities for automatic analysis of large textual data bases: detecting the topics present in the corpus and grouping the documents accordingly or in other words Clustering documents, hence achieving two tasks of Text Mining at the same time using only one algorithm. In our study we use an appropriate BSS approach along with new weighting distance to transform the textual data to achieve higher level of accuracy.
Enhancing change mining from a collection of event logs: Merging and Filtering approaches
Authors: Hmami A , Sbai H , Fredj M 
Published: January 2021

Abstract
An event log is the key element of all change mining and process mining approaches. Those approaches bridge the gap between conventional business process management and data analysis techniques such as machine learning and data mining. In this day, companies and business organizations usually use a family of business processes that may face different variations and adjustments. Still, those processes are widely identical, with a slight difference in specific points. Consequently, performing a process mining or a change mining for each process will be a redundant task. The use of a configurable process model is a practical solution for redundancy problem. Thus, the process mining areas such as discovering verifying the conformity of a business process and enhancing processes, are reduced considerably. However, the configurable process models and the variability concept are rarely introduced in change mining approaches. The existing methods that analyse and manage event logs do not then consider the variability issue. Therefore, the fact of using a collection of event log becomes a challenging task. Our proposed approach is to merge and filter a collection of event logs from the same family with respect to variability. Our goal is to enhance change mining from a collection of event logs and detect changes in variable fragments of the obtained event log.
A modified ALNS algorithm for vehicle routing problems with time windows
Authors: Mehdi Nasri , Abdelmoutalib Metrane , Imad Hafidi 
Published: January 2021

Abstract
In this paper, we present an efficient heuristic for the vehicle routing problem with time windows (VRPTW), inspired by the Adaptive Large Neighborhood Search (ALNS) previously suggested by Røpke and Pisinger [21]. The proposed heuristic uses the Modified Choice function (MCF) of Drake [12] as an elegant selection mechanism to favor the most successful operators instead of the roulette wheel selection. This general method is denoted Modified Adaptive Large Neighborhood Search (MALNS). The computational experiments are performed and the comparison with the classical ALNS is given according to Solomon’s benchmark, and its extension the instances of Gehring and Homberger’s benchmark.
Digital document image restoration using a blind source separation method based on copulas
Authors: Ourdou Amal , Ghazdali Abdelghani , Metrane Abelmoutalib , Hakim Moad 
Published: January 2021

Abstract
In the last few decades, digital image degradation issues, such as blur and noise due to the scanning process or the presence of spots, underwriting, overwriting or bleed-through/show-through effects on the image’s background has been a popular research field. To solve this problem, many background removal methods has been introduced in the literature which are based on local or adaptive filters in order to deal with the low-contrast issue. For this paper, we will be focusing on the bleed-through/show-through effects, which is already resolved in literature by an analogy between the front-ground and the background of the image, that is to say, a recognition of two images is required. To fix that problem, we suggest a new restoration method using blind source separation based on copulas theory that models the dependency structure, with the aim of improving text readability and OCR efficiency.
Numerical simulation of a nonlinear problem modeling the cooling of a metal
Authors: Aboud Fatima , Nachaoui Abdeljalil 
Published: January 2021

Abstract
In this work, the finite difference method was used to calculate the heat distribution during cooling by the boundary part of a cylindrical material subjected to high temperature. A mathematical model of the process was formulated using cylindrical coordinates. The heat transfer coefficient occurring in the Robin condition at the cooled boundary is nonlinear. We use quasi-Newton techniques combined with gradient-like methods to solve the discrete nonlinear problem.
A comparative Plagiarism Detection System methods between sentences
Authors: Zouhir A. , El Ayachi R. , Biniz M. 
Published: January 2021

Abstract
After the era of the World Wide Web, information is easily accessible with a single click. But this progression has drawbacks despite the ease of access to information. Plagiarism has a growing challenge to society, which impact on the academic world, researchers, and students in particular. This work discusses the plagiarism process, types, and detection methodologies. It presents the different plagiarism detection techniques based on syntactic and semantic approaches. The result of this work is a comparative survey of plagiarism detection system methods using the identification of syntactic and semantic similarities based a sentence-to-sentence comparison, and no longer word-to-word like the classical systems because the similarity between the sentences is a complex phenomenon.
Modeling and Control for Spacecraft Hovering Considering J2
∼J4
Perturbation
Authors: Zhang Liang , Ge Peiqi 
Published: January 2021

Abstract
In this paper, the required control with the J
2∼J
4 perturbation is derived for the spacecraft hovering. The J
2∼J
4 perturbation is introduced into spacecraft relative motion model by Gauss’s variation equations, and the orbit elements affected by the J
2∼J
4 perturbation are obtained. Combining the nonlinear relative motion model of the spacecraft with the obtained orbit elements, the analytic expression of the hovering control is completely derived. The numerical verification results indicate that the derived control considering the J
2∼J
4 perturbation can ensure the high accuracy for the spacecraft hovering.
Modeling Analysis of Carrier-based Aircraft’s Scheduling on Flight Deck
Authors: Tang Junjie , Zhu Ziqian , Wang Yanyang , Liu Yang , Fang Yuheng 
Published: January 2021

Abstract
The scheduling efficiency of carrier-based aircrafts on flight deck is an important factor on improving the sortie rates of aircrafts. Based on the key process of the sortie of deck-based aircrafts, the main research directions in the field of the scheduling of carrier-based aircrafts are summarized, which are the path planning and collision detection of carrier-based aircrafts, the integrated scheduling method of carrier-based aircraft fleets, and the coordinated scheduling strategies of carrier-based aircrafts. Then, the researches of carrier-based aircrafts dispatch system are introduced. The progress and shortcomings of the existing research results were pointed out, also, the feasible development directions in the future are proposed, which provides a reference for the research on the scheduling of aircrafts on flight deck.
Modeling on Heterosynaptic Plasticity Based on Postsynaptic Membrane Potential and Current Density
Authors: Chen Huanwen , Xie Lijuan 
Published: January 2021

Abstract
It is still elusive whether different regions in pyramidal neurons have the same heterosynaptic plasticity. To explain the heterosynaptic plasticity, we created a model of synaptic plasticity in which synaptic changes depend on the postsynaptic membrane potential and current density. We found that, in a simulated L5 pyramidal neuron, LTP (long-term potentiation) of synapses in each region of the neuron leads to LTD (long-term depression) of synapses in other regions, which is consistent with the experimental results of heterosynaptic plasticity. However, the effects of heterosynaptic plasticity in different regions are different. Compared with the basal and apical regions, LTP of synapses in the tuft region can not induce significant LTD in other regions. Our research provides a new way to solve the runaway growth of synaptic weights in a neural network.
Ultra-short-term PV Power Forecasting Based on Cloud Image Modeling
Authors: Zhang Tianhao , Deng Jinxiang , Gan Zhongxue , Wang Hong 
Published: January 2021

Abstract
Due to the impact of many local sudden changes in photovoltaic (PV) output power, its ultra-short-term forecasting is facing great challenges. In a time scale of 1 to 2 minutes, the generation, movement, and ablation of clouds in the sky are the main factors that affect the output power. Therefore, a prediction method based on cloud images is proposed for ultra-short-term prediction of PV output power. First, apply image processing technology to extract image features that affect the changes in PV output power, including cloud coverage rate, direct sunlight rate, and image brightness; second, calculate the extraterrestrial irradiation and air mass at each time point; finally, use the above features as input factors, and use PV output power output data, and a long short-term memory network (LSTM) is used to construct a PV power prediction model to achieve ultra-short-term forecasting of PV output power.
An Evaluation Method of DC Magnetic bias Vibration for Transformer based on Prior Knowledge and Neural Network Modeling
Authors: Zhang Chenliang , Ma Huiyuan , Li Tao , Guo Longpu , Guo Chunlin , Huang Hui 
Published: January 2021

Abstract
At present, the number of fault samples is insufficient in the field of transformer fault diagnosis based on vibration, and the experimental data of existing research results mostly come from laboratory conditions, which is difficult to popularize in a large scale. Therefore, combining with the idea of fuzzy mathematics, this paper integrates the prior knowledge into the neural network, and extracts the function of characteristic quantity and fault probability from the trained neural network model by numerical test, which is the basis of fault diagnosis. Then, the effectiveness of this method is verified on the test sample set. The results can be used as a means to incorporate prior knowledge into intelligent algorithms, and help to study the relationship between different feature quantities. In general, this article provides a way of applying prior knowledge to vibration diagnosis algorithms, which has broad development prospects.
A Synaptic-plasticity Model Inspired by Metabolic Energy
Authors: Chen Huanwen , Xie Lijuan 
Published: January 2021

Abstract
Inspired by the study of metabolic energy, a new synaptic plasticity model was established based on postsynaptic membrane potential and membrane current density. In this model, the change of synaptic weights is expressed by the difference between the resting energy state and firing energy state. The simulation results in L5 pyramidal neurons show that the proposed model can reproduce the triplet and quadruplet experiments of synaptic plasticity, which indicates that our model is feasible. The results of this paper will help to expand the synaptic plasticity model and the understanding of learning and memory from the perspective of energy.
Research on Text Classification Based on BERT-BiGRU Model
Authors: Yu Qing , Wang Ziyin , Jiang Kaiwen 
Published: January 2021

Abstract
Text classification is a typical application of natural language processing. At present, the most commonly used text classification method is deep learning. Meanwhile there are many difficulties in natural language processing, such as metaphor expression, semantic diversity and grammatical specificity. To solve these problems, this paper proposes the structure of BERT-BiGRU model. First, use the BERT model instead of the traditional word2vec model to represent the word vector, the word representation is calculated according to the context information, and it can be adjusted according to the meaning of word while the context information is fused. Secondly BiGRU model is attached to the BERT model, BiGRU model can extract the text information features from both directions at the same time. Multiple sets of experiments were set up and compared with the model proposed in this paper, according to the final experimental results, using the proposed BERT-BiGRU model for text classification, the final accuracy, recall and F1 score were all above 0.9. It shows that BERT-BiGRU model has good performance in the Chinese text classification task.
Simulation Technology and Analysis of Military Simulation Training
Authors: Yao Kai , Huang Shaoluo 
Published: January 2021

Abstract
In view of the vigorous development of simulation training technology, the classification, research status and development direction of simulation training technology at home and abroad are studied, which provides reference basis for equipment simulator research. Modern simulation trainer is a multi-component platform by means of simulation technology, which has the characteristics of more pertinence and more comprehensive training elements than traditional training mode.
Simulation Study on Intelligent Bolt Monitoring System Based on Cloud Service
Authors: Xu Lichao , Zhang Jimin , Luo Jinnan 
Published: January 2021

Abstract
As a common connector in the industrial field, bolts play a key role in many fields. However, it is hard to obtain the stress value of a bolt directly in traditional ways. In order to achieve that, an intelligent bolt monitoring system based on Cloud Service was studied in this paper. An ultrasonic measurement technology based on the Sono-elasticity Principle was presented first, which laid a foundation for measuring the displacement of a bolt when it was stressed. Then, we did some experiments on emulating the whole process of the system through software simulation. Finally, Cloud Service was introduced to restore simulated data of a bolt, which made it possible for professionals to monitor online. The result shows that the system can work well. In conclusion, an intelligent bolt monitoring system based on Cloud Service can greatly improve work efficiency and bring many benefits to related fields and industries.
Simulation of the Worst Failure Probability of Sharing Platform under Unknown Demand Distribution
Authors: Wang Changjun , Gong Xueling 
Published: January 2021

Abstract
The sharing economy integrates the idle resources to match the fragmented market demand via the Internet platform. Different from the supply side, the demand is highly uncertain, in which, significant spikes and valleys exist. Therefore, it is assumed that only the statistical characteristics, instead of precise distributions, are known. The matching efficiency of supply and demand in sharing platform is studied. Firstly, a linear programming model under the moment constraints is proposed to describe the worst failure possibility in demand satisfaction. Then, the equivalent semi-infinite program is developed by Lagrange duality, and the analytical expression of the worst failure probability can be given. Finally, simulation study is implemented. This work not only riches the studies of moment-based bound, but also provides the managerial insights to the demand management of the sharing platform in the uncertain setting.
Pure Electric Vehicle Dynamic Performance Design and Simulation Research Based on ADVISOR
Authors: Liu Benchao 
Published: January 2021

Abstract
Aiming at the problem of certain nonlinear and time-varying uncertainty in driving conditions and driver operation of electric vehicles, the control algorithm of whole vehicle controller is studied, and a dual-fuzzy control strategy of driving speed distribution is proposed. The vehicle dynamics model and driver input parameter model were established for the pure electric vehicle. The basic speed fuzzy controller and compensation speed fuzzy controller were used to solve the problem, and the driving speed value was optimized. Based on the ADVISOR platform, the performance simulation analysis of the electric vehicle with dual fuzzy control unit was carried out. The results show that the dual-fuzzy control unit can meet the dynamic requirements of pure electric vehicles.
Control Strategy of Microgrid Switching Station based on B2G Mode of Fully Charged Battery
Authors: Li Tao , Guo Chunlin , Guo Longpu , Zhang Chenliang 
Published: January 2021

Abstract
After the electric vehicle exchange station is connected to the microgrid, the battery in the exchange station can be used to stabilize the output fluctuation of renewable energy or provide auxiliary services such as frequency modulation and rotary standby through battery to grid (B2G), so as to reduce the construction cost of microgrid and improve the operation profit of the power station. In this context, the following work has been done: (1) A scheme of battery swapping to participate in B2G service is proposed, which solves the problems of insufficient available power and difficult charging plan when selecting batteries to be charged and batteries being charged; (2) Considering the service reliability of power exchange station and the power fluctuation of tie line interaction, a day ahead charging scheme is proposed and the operation cost of the power station is reduced by making the plan and scheduling the battery within a day; (3) The effectiveness of the proposed control strategy is verified by a simulation example. The control strategy of battery scheduling effectively participates in the B2G service of microgrid, which improves the power supply reliability of microgrid and gains some benefits.
Evolution of the Public’s Attitude toward NIMBY Incidents based on Opinion Dynamics Theory: An Agent-based Model
Authors: Liu Huimin , Song Shuang , Hu Shuya , Wang Tong 
Published: January 2021

Abstract
NIMBY (Not in My Back Yard) incidents often affect the progress of infrastructure construction and sometime may cause social instability. The public’s NIMBY attitude is affected by various factors, which also lead to the development of NIMBY incidents. To explore the influence of the public’s internal composition and external environment on the evolution of the public’s NIMBY attitude, a bounded trust model of the public’s continuous viewpoint to a NIMBY event is constructed in this study based on opinion dynamics theory. The evolution process of the public’s attitude in different internal and external situations is also simulated. Among the public, the evolution of the individual public’s NIMBY attitude is affected by the individual communication in their network of relations. According to their degree of influence, the public can be divided into three categories: ordinary individuals, opinion leaders, and diehards. In external environment, the public’s attitude is influenced by the timeliness and compensation degree of the government’s response to a NIMBY incident and the information transmitted by the media. The simulation results show that during the public’s opinion exchange, opinion leaders and diehards in a group usually have a strong impact on the evolution of the public’s NIMBY opinion. Moreover, the government’s environmental compensation can only alleviate the NIMBY conflict temporarily. The findings also indicate that the government and the media should maintain the authenticity of information disclosure, and public participation and multiparty cooperation mechanism should also be encouraged.
A New Method for Constructing the Basic Probability Assignment Function in D-S Evidence Theory
Authors: Geng Junbao , Wei Zhangjian 
Published: January 2021

Abstract
The basic probability assignment function is the basis of D-S evidence theory and the premise to obtain accurate evaluation conclusions. A new method to construct the basic probability assignment function is proposed in this paper, which uses nonlinear function to construct the basic probability assignment function, and takes into account the relationship between the change trend of index and the change trend of the basic probability assignment value. This construction method can be applied to the construction of the basic probability assignment of the index, with the decrease of whose value, the condition will degrade gradually.
A Multi-seed Nodes Selection Strategy for Influence Maximization Based on Reinforcement Learning Algorithms
Authors: Nie Gege , Tang Ming 
Published: January 2021

Abstract
Identifying influential individuals in the dissemination of information is an important topic in the study of social networks. Up to now, most of the previous works of Influence Maximization on social networks has been limited to selecting seeds based on a certain structural feature of the networks. These algorithms only consider a certain structural feature and cannot effectively select suitable seeds on social networks when the network has complex and changeable structures. Most of them only get good results on a special kind of networks. In order to find the most suitable nodes as the initial seed nodes in various social networks, we designed a new seeds selection algorithm which is based on reinforcement learning (IMQ). Our approach takes advantage of the characteristics of reinforcement learning’s agent that can continuously interact with the environment, this algorithm can be adapted to select the most suitable nodes as seed nodes on various social networks. It fully considers the influence of the network structure characteristics on the influence propagation process, so that this method can select the best nodes as seeds on social networks with different topologies. In order to demonstrate the superiority of the approach, we conducted comparative experiments on six real social networks. Experimental results show that IMQ can be applied to various structural social network, and has stronger universality than traditional methods.