bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-monotonic shallow nucleus-nucleus potential for heavy-ion elastic scattering2
Authors: Sujan Islam M , Majumder R , Hossain S , Nilima Athoy , Sayed M A , Shariful Islam M , Billah M M , Azad M M B , Tariq A S B , Uddin M A , Basak A K
Published: January 2021

‘Goldberg criterion’ [Goldberg and Smith, Phys. Rev. Lett. 29 (1972) 500] tells that at sufficiently high energies, where pronounced refractive scattering with nuclear rainbow oscillations are followed by an ‘exponential-type falloff’ in the angular distribution, discrete ambiguities are eliminated for the deep monotonic potential. The criterion is also confirmed in the work of Bartnitzky et al. [Phys. Lett. B 365 (1996) 23] on the 16O+16O elastic scattering in the energy range of 250 - 704 MeV. However, their finding ‘using model-independent potentials’ suggests that heavy-ion elastic scattering data unambiguously favour deep potentials. The Goldberg criterion is examined in our work for non-monotonic shallow potentials using the 16O+16O elastic scattering at energy region up to 350 MeV.
Anharmonicity in light nuclei near drip lines
Authors: Majumder Anup , Bary Malik F
Published: January 2021

Single-particle energy levels of nucleons moving in an anisotropic harmonic oscillator potential along with a spin-orbit and an orbit-orbit interaction have been calculated. The model proposed herein incorporates shapes generated by higher multipolar anisotropies other than the quadruple one. The gaps among the energy levels are strongly dependent on the degree of anisotropy and hence, affect the nucleonic separation energies. For large anisotropies, this characteristic disturbs the occurrences of the usual magic numbers and the sequential ordering of the shells characterized by principal quantum number, N, associated with the elementary shell model. For large asymmetries, the states characterized by higher N, may come down and lie below the energies of the states with lower N and vice versa. This feature of the model provides a natural explanation of intruder states or the island of inversion observed experimentally. For certain choices of the parameters, the large gaps in single nucleonic separation energies could occur at neutron numbers, 10,18,30, and 32 as observed, respectively, in 20Ne, 28Ne, 52Ti, and 54Ti. For neutron as well as proton number 14, the magnitude of this energy gap depends strongly on the choice of the strength of the spin-orbit and orbit-orbit terms. The model provides a simple understanding of the observed fact that some nuclei with proton or neutron number 14 sometimes exhibit this gap but not others. It also provides an interesting insight of having a low-laying (1/2
−
) state in 7He, and confirms the ground state spin and parity of 11Be to be (1/2+) and those of 9He to be (1/2+), both of which have been contentious. This investigation indicated the density distribution, i.e., the shapes of many of nuclei near drip line to be anisotropic having a symmetry about their body-fixed z-axis.
Growth and characterization of Li7La3Zr2O12 single crystals by using floating zone method for use as fast Li-ion conductor†
Authors: Razzaque Sarker Md Abdur , Watauchi Satoshi , Nagao Mashanori , Moazzem Hossain Md , Tanaka Isao
Published: January 2021

Large size high quality Li7La3Zr2O12 single crystals were grown successfully by floating zone (FZ) technique. The grown crystals were characterized by X-ray diffraction, etch pits density measurement, Impedance analysis, Vibrating sample magnetometry (VSM) and UV-Visible spectrometry. The investigation of structural, electrical, optical properties and defect density of the Li7La3Zr2O12 single crystals revealed that the crystals were high quality and free from defects. The chemical analysis showed that the grown crystals have uniform composition along cross-section and growth direction. The floating zone grown single crystals contain no low-angle grain boundaries, indicating that they can be used for high efficiency Li-ion batteries.
HDR brachytherapy using cylinder, tandem-ovoid (T&O) and interstitial implant with prognosis
Authors: Malik Sadiq R , Ahmed Mushfika , Reza Md Shohel , Bairagi Ashish K , Yeasmin Nilufa , Banu Parveen Akhter , Rukhsana Naheed , Naher Lutfun
Published: January 2021

Purpose: Among the therapeutic modalities available for the treatment of cervical cancer, irradiation is regarded to be the standerd treatment for all tumor stages, which includes external beam radiation therapy and HDR brachytherapy or a combination of these two. This modality of cancer, needless to say, is prevalent in Bangladesh as the women are coming forward for its detection. Dramatic advances have been made in brachytherapy for cervical cancer. Radiation treatment planning has evolved from two-dimensional to three-dimensional, incorporating magnetic resonance imaging and/or computed tomography into the treatment paradigm. This allows for better delineation and coverage of the tumor, as well as improved avoidance of surrounding organs. Brachytherapy applications are not restricted to one method only; any method could be combined with another, as well as other radiotherapy techniques. In cervical cancer, intracavitary brachytherapy (ICBT) has been used for practical reasons. Lately though, combination of ICBT and interstitial brachytherapy (ISBT) techniques are being evaluated in deference to feasibility, practicality and reproducibility. Advanced brachytherapy can achieve very high rates of local control with a reduction in morbidity, compared with historic approaches. This study provides an overview of state-of-the-art gynecologic brachytherapy, with a focus on recent advances and their implications for women with cervical cancer.

Materials and Methods: Three patients were selected on different staging. Three different types of applicator (Cylinder, Tandem and Ovoid and Interstitial Implant) were inserted according to the patient’s anatomy and staging. Prescribed dose were different for the implant technique.

Result: Progress from 2D- to 3D-based imaging and treatment planning for cervical cancer brachytherapy has improved local control, reduced toxicity, and improved overall survival for women.

Conclusion: The result of our study indicates that a precise delivery of treatment by applying a appropriate applicator and the choice of treatment planning technique is more important for a better outcome.
Structural, Elastic and Electronic Properties of Nitride Ti2CdN Phase in Comparison with the Carbide Ti2CdC Phase from First-principles Study
Authors: Roknuzzaman M. , Hadi M.A. , Nasir M.T. , Naqib S.H. , Islam A.K.M.A.
Published: January 2021

First-principles studies were conducted to investigate the structural, elastic and electronic properties of the Cd-containing synthesized MAX phase Ti2CdC in comparison with the predicted phase Ti2CdN. Our calculations show that the substitution of C by N in Ti2CdC mostly affects the lattice constant c; the lattice constant a remains almost unchanged. All the elastic constants and moduli increase when C is replaced by N. In comparison with the Ti2CdN phase, Ti2CdC is more compressible along the c-axis. The elastic anisotropy in Ti2CdC is higher in comparison with Ti2CdN. Both the two nanolaminates are brittle in nature. The calculated electronic band structures and density of states imply that the chemical bonding in these two compounds is a combination of covalent, ionic and metallic nature. Electrically, Ti2CdC is more conducting than Ti2CdN.
In-plane resistivity of hole doped cuprates: role of pseudogap and quantum criticality*
Authors: Afsana Azam M , Borhan Uddin M , Naqib S H
Published: January 2021

The anomalous behaviour of dc charge dynamics in high-Tc
cuprates cannot be explained by the prevalent Boltzman transport theory. In high-Tc
cuprates the in-plane and out-of-plane dc resistivities exhibit high anisotropy and unconventional temperature dependence. In this study we have modeled the temperature (T) and hole content (p) dependent in-plane resistivity, ρa
(T, p), of pure and Ca doped Y123 (Y(Ca)Ba2Cu3O7-

δ
). We have adapted and extended the formalism developed by Naqib et al. (Physica C 471 1598 (2011)) by taking account of two generic features present in all hole doped cuprates, namely - (i) the presence of the pseudogap in the quasiparticle spectrum and (ii) high-T linear behavior, to elucidate non-Fermi liquid ρa
(T, p) of Y(Ca)Ba2Cu3O7-

δ
over a wide range of temperature and hole contents. The characteristic pseudogap energy scale, εg
(p), extracted from the analysis of the resistivity data was found to agree well with those found in a variety of earlier studies. Other extracted parameters from the analysis of ρp
(T, p) data showed methodical variations with the variation of hole content. Important features of the analysis are briefly discussed.
Comparative study of optimization techniques in deep learning: Application in the ophthalmology field
Authors: Mustapha Aatila , Mohamed Lachgar , Ali Kartit
Published: January 2021

The optimization is a discipline which is part of mathematics and which aims to model, analyse and solve analytically or numerically problems of minimization or maximization of a function on a specific dataset. Several optimization algorithms are used in systems based on deep learning (DL) such as gradient descent (GD) algorithm. Considering the importance and the efficiency of the GD algorithm, several research works made it possible to improve it and to produce several other variants which also knew great success in DL. This paper presents a comparative study of stochastic, momentum, Nesterov, AdaGrad, RMSProp, AdaDelta, Adam, AdaMax and Nadam gradient descent algorithms based on the speed of convergence of these different algorithms, as well as the mean absolute error of each algorithm in the generation of an optimization solution. The obtained results show that AdaGrad algorithm represents the best performances than the other algorithms with a mean absolute error (MAE) of 0.3858 in 53 iterations and AdaDelta one represents the lowest performances with a MAE of 0.6035 in 6000 iterations. The case study treated in this work is based on an extract of data from the keratoconus dataset of Harvard Dataverse and the results are obtained using Python.
Transfer Learning for classifying front and rear views of vehicles
Authors: Baghdadi Sara , Aboutabit Noureddine
Published: January 2021

Various computer systems have been proposed to classify vehicles according to several criteria (category, brand, model). Unfortunately, there is not much research on the classification of views, especially front and rear views. Several factors make this classification very difficult including similarity in shape, size, and color. This work aims to classify front and rear views of vehicles using the Transfer Learning (TL) approach. Here, we used a pre-trained CNN (AlexNet) that has been trained on more than a million images and can classify images into 1000 object categories. Thus, we transferred its learned knowledge and applied it to our new task (Classifying vehicle views). We conducted then two experiments. The first experiment has two scenarios: the first scenario is devoted to Transfer Learning using the AlexNet model, and the second scenario aims to build a network from scratch inspired from AlexNet. Experimental results reveal that the Transfer Learning approach gives high results. On the other hand, in the second experiment, we decided to use TL-AlexNet to extract features and train them with an SVM classifier instead of fully connected layers. And also, we combined the SVM with the fully connected layers. The accuracy rates have been improved after this experiment.
Multi-Agent Reinforcement learning Approach to IoT Coordination
Authors: Belkeziz Radia , Jarir Zahi , El Kassmi Ilyass
Published: January 2021

Nowadays, the IoT is evolving at a very fast pace and has proven its usefulness in several areas by creating better applications and services. However, more flexible approaches proving a well-defined architecture meeting the general requirements and building blocks of IoT are still needed despite the results obtained in the literature. In this paper, we focus on the IoT coordination challenge which represents a fundamental property allowing things to collaborate and make a decision when an appropriate change is detected in its environment. This contribution proposes an agent-based approach coupled with Q-learning which is a reinforcement learning technique, to compensate for coordination in its entirety, namely objective coordination and subjective coordination. To illustrate this approach, an evacuation use case is presented.
Colouring of graphs by HB colour matrix algorithm method
Authors: Bhange A A , Bhapkar H R
Published: January 2021

Graph Colouring is a chief element in graph theory with tremendous applicability in computer science like data mining, clustering, networking, image segmentation etc. And a variety of implementations in aircraft scheduling, register allocation, sudoku, mobile networking, etc. Various algorithms were contrived for vertex colouring. This paper defines the HB colour matrix method and its kinds. There are three types of such matrices, Vertex HB colour matrix (VHBCM), Edge HB Colour matrix (EHBCM), and Region HB colour matrix (RHBCM). Also, the HB colour matrix algorithm is developed using a special assignment method, which gives the chromatic number of the given graph. Further, the algorithm is used to develop the python program, giving time complexity O(n) and space complexity O(n2). Also, the output of the python program for some standard graphs is calculated. The Similar algorithm can be developed for edge and face colouring of the graphs. Colouring of the graph further can be extended to perfect colouring.
An Intelligent Procedure for Dynamic Evaluation in a Scientific Learning Session
Authors: Diyer Okacha , Achtaich Naceur , Najib Khalid
Published: January 2021

In this research, we are interested in the evaluation of scientific subjects’ teaching components such as course questions, examples, application and deepening exercises. Thereby, we have presented a method that allows the teacher to make decisions using a system that implements interpretations based on the decision tree. The proposed system does not only propose assessing students’ learning, but it also offers the teacher intervention phases to reduce the difficulties encountered by students. This evaluation approach offers the opportunity for each student to acquire the necessary skills needed in their learning according to their abilities. In our research, we are interested in an evaluation that is based on an intelligent strategy to adjust the students learning during the classroom session. We present the interaction strategy between the teacher, the intelligent system and the students. Last but not least, we build the decision tree and interpret the results obtained, its use by the teacher for any further improvement. Consequently, this indicates the importance of this evaluation conception through artificial intelligence and its impact on machine learning by offering it an interesting database.
Rectified non-linear unit for convolution neural network
Authors: El Jaafari Ilyas , Ellahyani Ayoub , Charfi Said
Published: January 2021

Convolution neural network takes in input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. At each layer, a linear transformation is carried out on the received data. Aiming for solving non-linear problems, a mandatory unit named activation function is used by neural networks. The activation function greatly influences the success of training deep neural networks. In this paper, a new rectified non linear function unit that we call Rectified non-linear unit (ReNU) is proposed. The presented activation function returns x − log(x + 1) for positive values, and zero for negative ones. The ReNU multiplies the received gradient by values between 0 and 1 depending on the importance of neurons ( 1 for the biggest neurons and 0 for the smaller ones) unlike the ReLU that returns the same received gradient for all positive values in its back propagation. Using the proposed activation function the CNN performance remarkably raises. The ReNU has been tested on MNIST and CIFAR-10 data sets and compared to the ReLU and ELU activation functions. The experimental results are satisfactory when compared to known activation functions in terms of convergence speed and CNN accuracy.
Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy images
Authors: Ellahyani Ayoub , El jaafari Ilyas , Charfi Said
Published: January 2021

Diseases of the digestive tract, such as ulcers, pose a serious threat to human health. In fact, many types of endoscopy are employed to examine the patient’s gastrointestinal tract. Recently, wireless capsule endoscopy (WCE) is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared to traditional endoscopies. This diagnosis usually takes a long time, which is tiring, and so the doctors may miss parts where abnormalities of the gastrointestinal tract may present. Therefore, automated diagnostic technics to detect symptoms of gastrointestinal illness in WCE images is adopted as an excellent enhancement tool for these doctors. In this work, a new computer-aided diagnosis method for ulcer detection in WCE images is proposed. After a preprocessing step, fine-tuned convolutional neural network (CNN) is used to extract deep features from these images. Since the number of ulcer images in the available data sets is limited, the CNN networks used in this work were pre-trained on millions of labeled natural images (ImageNet). After the deep features extraction, a random forest classifier is employed to detect ulcer from WCE images. The proposed approach demonstrates promising results (96.73 % and 95.34 % in terms of precision and recall respectively). Those results are satisfactory when compared to recent state-of-the-art methods.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Numerical Simulation on the Sloshing Characteristics of Gasliquid Flow in Cargo Tank and Anti-sloshing Methods
Authors: Zhang Qiong , Zhu Hanhua , Wei Wei
Published: January 2021

With the rapid development of society, the demand for liquid carrier ships and vehicles is increasing rapidly. Under the action of external load, the liquid in the tank would move relative to different degrees and produce hydrodynamic pressure on the local part of the liquid bulkhead, which will cause local damage or complete destruction of the structure, and then lead to leakage or other safety accidents. In this paper, computational fluid dynamics method is used to simulate the sloshing characteristics of the liquid in the cargo tank, and the instantaneous characteristic data of the free surface displacement fluctuation of the sloshing liquid are monitored. Through the pressure acquisition system, the hydrodynamic pressure distribution acting on the side wall of the cargo tank under external excitation is obtained. Through the free surface displacement fluctuation and the maximum hydrodynamic pressure acting on the side wall, the severity of liquid sloshing in cargo tank under external excitation is evaluated. The effects of different baffle combinations on liquid sloshing in cargo tanks are studied and discussed. The effects of baffles with holes on liquid sloshing are also discussed.
An Algorithmic Study to Maximize 5G Network Throughput Based on the Markov Decision Process
Authors: Xiao Zheng
Published: January 2021

In this article, the network throughput optimization problem is investigated based on the theory of Markov decision process, combining the device-to-device direct selection problem with the finite stage discount MDP model problem. First, models for the device-to-device communication selection using MDP are built; second, the optimal mode selection strategy is derived using a finite stage backward iterative algorithm; and finally, the given mode selection strategy is evaluated by conducting a large number of simulation experiments. The results show that the MDP-based mode selection method proposed in this article has better performance in maximizing throughput and can yield better mode selection strategies with the advantage of obtaining larger system throughput.
AGV Path Planning based on Improved Dijkstra Algorithm
Authors: Sun Yinghui , Fang Ming , Su Yixin
Published: January 2021

Aiming at the path planning problem of Automated Guided Vehicle (AGV) in intelligent storage, an improved Dijkstra algorithm that combines eight-angle search method and Dijkstra algorithm for path optimization is proposed. The grid method is used to model the storage environment, and the improved Dijkstra algorithm is used to optimize the route of the AGV. The simulation test of the AGV path planning process with Matlab shows that the AGV can effectively avoid obstacles by using the traditional Dijkstra algorithm and the improved Dijkstra algorithm, and then search for a collision-free optimized path from the start point to the end point; and the traditional Dijkstra algorithm In comparison, the path length planned by the improved Dijkstra algorithm is shorter and the turning angle is less, indicating that the improved algorithm is correct, feasible and effective, and has a strong global search ability.
A New Strategy using Date Integration for Identifying Interactions between Genes and Environment
Authors: Zhou Jiabin , Tian Sucai , Wang Jiaqi , Guo Yixing
Published: January 2021

The detection of gene-environment (GE) interactions is very important for dealing with complex diseases. Although some methods have been proposed to detect the interactions between genes and the environment, however, in the case of rare variants, these methods are limited. In this paper, we extend the existing ADA method and proposed a combined strategy (i-ADA) to identify the gene-environment interaction for rare variants. Firstly, we use Levene test to test the equality of variances under different sites. Secondly, based on the first-sep P-values and combined with the ADA method, we design a novel interaction test. Finally, we use the GAW17 dataset to illustrate the practicality of the proposed method.
Piecewise Multi-Parameter Tangent/Cotangent Function and Its Application in Chaotic Pseudorandom Bit Generator
Authors: Wu Qi
Published: January 2021

To increase the number of parameters of 1-dimensional discrete chaotic mappings, employing translation & scaling, this letter creatively designs a piecewise 5-parameters Tangent/Cotangent Function. Both bifurcation diagram and Lyapunov exponent spectrum illustrate that the proposed function seems to be inappropriate for cryptographic applications, due to its narrow chaotic area. However, after applying it to devising chaotic pseudorandom bit generator, it outperforms skew tent mapping, whose chaotic area is full. It might be applied to some other fields as well, such as cryptographic hash functions, which will possibly be paid attention to in the future.
Solving Double Counting Problems with Dynamic Programming Algorithm
Authors: Jiang Wenrong
Published: January 2021

In view of the repetitive calculation problems that often appear in algorithmic questions, this paper analyzes the common thought methods and dynamic programming for solving such problems. Through several types of classic questions, such as the backpack problem, the longest common subsequence, etc., the analysis methods and scope of application of dynamic programming are summarized.
An Optimized Method of Packet Loss Rate under Backup Path Protection in OBS Network
Authors: Meng Aodong , Chen Yucheng , Dai Jiahao , Chen Yongqun
Published: January 2021

In order to calculate the packet loss rate in OBS network, this paper proposes a method, which performs good in both accuracy and rapidity, based on Erlang B formula to estimate the packet loss probability in OBS network using backup path protection mechanism. This method can be used to optimize the design of OBS network protocol, and it also provides an accurate and efficient approximate calculation method of packet loss rate in OBS network and can be conductive to the study of using copy transmission to enhance the network survivability. Based on the formula of Erlang B, this paper briefly introduces the derivation process and principle of this method. The basic experimental principles of simulation and estimation processes are designed in this paper, and the experimental results of the proposed method under a typical network topology (ARPA network) with no protection mechanism and 1 and 2 backup paths protection mechanisms are analyzed to verify its rapidity and accuracy. The results of experiments show the accuracy and efficiency of this method.