bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new synergistic approach for crop discrimination in a semi-arid region using Sentinel-2 time series and the multiple combination of machine learning classifiers
Authors: Moumni A , Oujaoura M , Ezzahar J , Lahrouni A 
Published: January 2021

Abstract
Accurate monitoring of agricultural lands and crop types is a crucial tool for sustainable food production. Therefore, to provide reliable and updated crop maps, the improvement of satellite image classification approaches is essential. In this context, machine learning algorithms present a potential tool for efficient and effective classification of remotely sensed data. The main strengths of machine learning algorithms are the capacity to handle data of high dimensionality, and mapping classes characterized by strong complex dynamics. The main objective of this work was to develop a new synergistic approach for crop discrimination in the semi-arid region of Chichaoua province, located in the Marrakesh-Safi region, Morocco, using high spatio-temporal resolution imagery and a multiple combination of machine learning classifiers. This approach was developed based on 10m spatial resolution open access Sentinel-2 (S2) images and machine learning algorithms. The atmospherically corrected S2 images were accessed through the Theia Land Data Center. Reference dataset was collected from a field survey carried out during the 2018 agricultural season in order to train the classifiers. Artificial Neural Networks, Support Vector Machine, K-Nearest Neighbors, Bagged Trees, Naive Bayes, Discriminant Analysis and Decision Trees classifiers were trained over the study area and the accuracy metrics, mainly Overall Accuracy (OA) and Kappa coefficient (K), were assessed. The trained models were single classifiers to build the ensemble classifier system. The obtained results showed high OA and K values up to 96% and 0.95 respectively, achieved by the developed approach. Therefore, based on these results, the approach we developed using the combination of multiple classifiers has a significant impact on crop classification quality.
A new Approach of Documents Indexing Using subject modelling and Summarization
Authors: Ouadif L. , El Ayachi R. , Biniz M. 
Published: January 2021

Abstract
Document indexing is a field of research in Natural Language Processing (NLP) that has been rapidly evolving for 70 years. It is an operation that focuses on the synthetic representation of a document according to a model in order to facilitate their subsequent use. This work is concerned with document indexing. Two points are addressed. This work is concerned with document indexing, we are trying to accelerate the indexing process of large document datasets, two points are addressed. The first one concerns the development of a document indexing system using the system's operating process based on three phases namely pre-processing, weighting, and subject modelling. The second point concerns the proposal for a new system that integrates a new developed automatic summary subsystem, the goal of this point is to minimize indexing time.
Statistical analysis of the rocks mass using data mining technique: a study case in Morocco
Authors: Ghazdali O. , Guemouria A. , Tagma T. , Rziki S. , Moustadraf J. 
Published: January 2021

Abstract
Data mining is a very important element that has been developing and evolving for years in the analysis and study of the stability of the underground gallery. More advanced methods, essentially empirical and statistical analysis are used to describe the rock mass of the OUMJRANE mine, in order to determine adequate support to be implemented in its underground gallery.
Modeling Study of Red LED Spectral Characteristics
Authors: Peng Dongsheng , Liu Kailong 
Published: January 2021

Abstract
LED is widely used in various fields as a new style of light sources. Among the various characteristics of the LED, photoelectric characteristics are often the most intuitive and practical. This paper will discuss the photoelectric characteristics of red LED. A study on the relationship between the forward current and luminescence spectrum, peak wavelength, main wavelength, color purity, luminous flux, luminous efficiency is presented. The experimental results show that the spectrum, peak wavelength and main wavelength of the red LED will shift red with the increase of the forward current, and the color purity will remain at a high level during the period. In addition, the luminous flux and light intensity increase with the increase of forward current, the luminous flux from 0.5 lm to 3.5 lm, but the luminous efficiency decreases with the increase of forward current. from 40 lm/w to 32 lm/w.
Forecasting Framework Using Hybrid Modeling and Support Vector Regression
Authors: Xia Lingjuan , Liu Xueyong , Wang Lixian 
Published: January 2021

Abstract
Time series forecasting for financial market has increasingly attracted the interests of investors and academic researchers. In recent years, some hybrid models have been constructed to improve the predictions since some methods cannot extract useful information from stock time series with noise to conduct prediction. In this research, a prediction framework is proposed to forecast the stock market behavior using the methods of wavelet coherence, multiscale decomposition and support vector regression (SVR). First, a combined method is applied to the raw data and remove noise to get useful information. Then, a SVR model is applied to improve the prediction performance of multidimensional nonlinear data. Furthermore, the comparison experiments were performed with both Shanghai Composite Index and Dow Jones Index to examine the effectiveness of the framework. The results indicate that the proposed framework performs better than other advanced models.
Research on Classification Model based on Neighborhood Rough Set and Evidence Theory
Authors: Zhang Yuzhuo , Wang Yunqiong 
Published: January 2021

Abstract
The forward greedy numerical attribute reduction algorithm based on the neighborhood rough set is used to reduce the continuous numerical evaluation metrics of the health condition of complex equipment. This eliminates the risk of data loss and the additional processing time, due to avoiding discretization of continuous numerical evaluation metrics. Furthermore, the reduced evaluation decision table is processed to construct the basic probability assignment function (BBAS). Finally, multiple evaluation metrics are fused by Dempster’s rule of combination to get the health condition grade, and the relationship between evaluation metrics and health condition grade is mined further. The theoretical analysis and experimental results show that the proposed model is effective and efficient for classification.
Modeling and Simulation Research of Heavy-duty AGV Tracking Control System Based on Magnetic Navigation
Authors: Zhou Xinmin , Jin Lingli , Liu Yuyan 
Published: January 2021

Abstract
This paper studies tracking control problem which is applied on the 16-wheel heavy-duty AGV(Automated Guided Vehicle). Firstly, the trajectory detection scheme is designed based on magnetic navigation sensors. Secondly, the 16-wheel heavy-duty AGV control model is simplified as a two-wheel AGV model according to Ackermann steering principle, and fuzzy trajectory control regulation is established based on car driving experience. Finally, a trajectory control simulation system for circular/straight trajectories is built in MATLAB Simulink to verify the effectiveness on trajectory control.
Simulation and Design of DC Sensor Based on Tunnel Magnetoresistance
Authors: Chen Zechun , Shi Hong , Zhao Cong , Chen Yue 
Published: January 2021

Abstract
In order to measure the DC current measurement, a method of current measurement and interference error elimination based on tunnel magnetoresistance (TMR) DC sensor is established. Firstly, the basic principle of TMR sensor is described, and its open-loop transfer function is derived, and the parameter variables related to its open-loop gain are analyzed; an adaptive filtering algorithm based on least mean squares (LMS) is proposed to detect the magnetic induction intensity. Taking three magnetic sensors as an example, the magnetic field estimation value under this condition is calculated. Finally, the correctness and effectiveness of the design method are verified by simulation.
Dynamic Property Simulation Research of Hybrid Electric Vehicle based on ADVISOR
Authors: Zhang Li 
Published: January 2021

Abstract
Energy conservation and environmental protection are two major themes in today’s world. Simulation analysis can save a lot of experimental costs in the production of cars. To improve the dynamic property of hybrid electric vehicles, a vehicle simulation model and battery simulation model were established in the ADVISOR to match and select the engine and motor parameters, and the performance of hybrid electric vehicles such as maximum speed, 0-100km acceleration time and SOC change of battery system was simulated and analyzed. The results show that the improved hybrid performance can meet the requirements, the vehicle’s power performance and fuel economy was improved.
Research on Application of LSTM-QDN in Intelligent Air Combat Simulation
Authors: Hu Dongyuan , Zuo Jialiang , Zheng Wanze , Zhang Ze , Zhao Yu , Zhang Qiang 
Published: January 2021

Abstract
Aiming at the problem of the lack of intelligence of virtual machine opponents in the human-machine confrontation semi-physical simulation environment, it is proposed to apply the deep reinforcement learning method into tactical making-decision for building an AI virtual pilot with self-confrontation and self-learning ability. First, flight dynamics and kinematics are used to build basic flight models in the simulation environment, and a missile attack area is established for weapon model; Second, inspired by the framework of interaction between the agent and the environment in reinforcement learning, a tactical decision architecture for flight agent based on the one-to-one tactical confrontation process is organized. Finally, the improved DQN method is used to fit the value function in the continuous state space, and the network training is completed by means of agent self-antagonism and human-machine confrontation. the well-trained AI model can undertake the role of virtual opponents in human-machine confrontation environment, and shows a certain degree of intelligence in the confrontation process with pilots.
Analysis of the Impact of Typical Severe Thermal Hydraulic Condition on the Evolution of Droplet Characteristics in a Spray: the Numerical Simulation
Authors: Huang X. Q. , Zhang T. B. , Malet J. 
Published: January 2021

Abstract
Numerical simulation has been widely applied in the research of spray systems during a severe nuclear plant accident. In order to improve the predictive accuracy of the calculation, the impact of the thermal hydraulic conditions on the development of the spray have been examined by the researchers. The current study presents the influence of the initial droplet distribution at the injection point of a full cone spray. By comparing the velocity of the droplets of different sizes with the experimental results, a reasonable initial droplet size distribution is suggested. Discussions are also performed to explain the divergence between the experiments and the numerical simulation.
Design and Application of Distributed Computing Environment Based on Data Drive
Authors: Zhu Zhenguo , Bi Chao , Zheng Rong , Zheng Song 
Published: January 2021

Abstract
This paper proposes a distributed computing environment based on data-driven mechanism, which can be deployed on different levels of Industrial Internet, designs and implements an Industrial Internet architecture with high reusability and rapid deployment. This architecture has the consistency of the computing environment in the cloud and terminal control layer, which is beneficial to the realization of remote management and collaborative control of Industrial Internet. Data driven technology enables heterogeneous industrial equipment to achieve interconnection and interworking at a low cost. The multi-engine computing environment constructed by it is not only an effective method to solve the application design and development of complex systems, but also a key technology to realize edge computing and fog computing. This paper designed a corresponding test bed platform, test results show that the technology can not only provide connectivity of heterogeneous data source and efficient technical support, and to ensure that the distributed industrial equipment real-time and synchronization of data transmission, but also has the strong edge computing capabilities to cope with the constantly generated data stream, and reducing the pressure of the cloud server storage, computing.
Visualization Technology Framework of Industrial Cloud Computing
Authors: Lu Zhe , Chen Zhicheng , Zheng Rong , Liu Chaoru , Wu Xiaolin , Chen Songbin , Zhang Wang , Zheng Song 
Published: January 2021

Abstract
Industrial cloud computing is considered to be an essential technology for future industrial enterprises. Under the influence of Industry 4.0 and Industrial Internet concepts, industrial cloud computing related technologies are becoming a hot topic in recent research. How to observe and analyze the intermediate and final results of industrial cloud computing is the first problem to be solved in this research field. This paper proposes a new industrial PaaS (Platform-as-a-Service) architecture, which has a very important relationship with the visualization of industrial cloud computing, and it deploys a large number of structurally symmetric Data Engine computing units. Configuration and reusable methods are used in this paper to form a visualization technical framework and establish a data mapping relationship with the Data Engine, which significantly improves the efficiency of visualization application development. Finally, we validated the technology using an industrial internet test bed. The experimental results show that this visualization framework technology can provide industrial users with an advanced industrial application development environment, and effective realtime monitoring of industrial cloud computing through the internet.
Optimization Research on Abnormal Diagnosis of Transformer Voiceprint Recognition based on Improved Wasserstein GAN
Authors: Zhu Kejia , Ma Huiyuan , Wang Jianing , Yu Changshu , Guo Chunlin , Xiao Ce 
Published: January 2021

Abstract
Transformer is an important infrastructure equipment of power system, and fault monitoring is of great significance to its operation and maintenance, which has received wide attention and much research. However, the existing methods at home and abroad are based on model analysis for detection and diagnosis techniques, and their application is limited by the maturity and applicability of expert knowledge. In recent years, deep learning artificial intelligence has made important breakthroughs, however, the relevant research is still mainly focused on the field of speech, and intelligent diagnostic techniques on machine voice patterns have just begun. Therefore, this article adopts an unsupervised learning anomaly detection method based on WGAN-GP to diagnose the electrical and mechanical anomalies of transformer equipment. The experimental results show that the method can effectively identify transformer anomalies and provide an idea for artificial intelligence-based machine voice pattern recognition.
Optimization and Application of High Power Thulium-Doped Fiber Laser
Authors: Zhang Ning , Chen Ming , Sun Huijuan , Yang Yansong , Chen Tingting , Chen Xiaodan , Xiao Mingming , Chen Bingjun , Luo Yang 
Published: January 2021

Abstract
In recent years, with the continuous progress of laser technology, fiber laser has been developed rapidly. Compared with the traditional laser, fiber laser has the advantages of good beam quality, small volume, easy integration, high luminous efficiency, strong anti-interference ability and good heat dissipation. As a kind of fiber laser, thulium doped fiber laser not only inherits the above advantages, but also has the rich energy level structure and spectral characteristics of thulium ion, which determines that thulium doped fiber laser plays an important role in the research field of long wavelength fiber laser. This paper discusses the influence of the length and concentration of doped fiber on the output of high power laser system. For a given laser system, all parameters have an optimal value. In order to make the high power thulium doped fiber laser work in the best state, we need to carefully select these parameters. Based on the rate equation, the relationship between the dopant concentration and the length of the optical fiber is given in theory. The experimental results show that the optimal fiber length is different for different pump power. The higher the pump power, the larger the optimal fiber length. The experimental results have guiding significance for the manufacturing technology of high power fiber lasers.
Optimization and Transmit Power Control Based on Deep Learning with Inaccurate Channel Information in Underlay Cognitive Radio Network
Authors: Zong Xuekai , Zhu Xiaomei , Zhu Aichun , Byrne John Oliver 
Published: January 2021

AbstractIn cognitive radio network(CRN), power control often faces the complicated iterations and large calculations, resulting in poor real time performance of the system. In this paper, a deep learning-based power control is proposed for CRNs, where the secondary users (SUs) can share the same channel of primary users (PUs) without causing excessive interference to the communication of PU. In the novel scheme, the DNN model is used to treat the input and output of the power control algorithm as unknown non-linear mappings and fit them, which determines the proportion of transmit power allocated to each SU, considering the interference caused to the PU. With this scheme, the maximization of the SUs sum-rate can be achieved. Furthermore, due to some errors in the practical samples of channel information, an auto-encoder is used to compress the channel coefficient through an encoder and reconstruct them through a decoder before DNN training. The simulations results show that the power control method using a combination of auto-encoding and DNN can improve the real-time performance of the system. And the sum-rate of SU is improved while the interference caused to the PU can be regulated even with the inaccurate channel information.
Analysis of various health parameters for early and efficient prediction of sepsis
Authors: Parashar Abhishek , Mohan Yukti , Rathee Neeru 
Published: January 2021

Abstract
Sepsis is a major health issue causing mortality, morbidity and health care financial crisis to people around the globe. To resolve this issue, many researchers and clinical practitioners have worked hard to predict the onset of sepsis using various parameters of patients. The proposed work is an attempt of authors to analyse the various parameters (8 vital parameters, 26 laboratory or clinical parameters, and 6 demographic parameters regarding hospital stay) given in Physionet Challenge dataset so as to devise the best features for early and efficient prediction of sepsis. Authors have also addressed another important issue of missing values of some parameters of some patients by applying Gaussian Mixture Model to estimate the missing value in pre-processing steps. The pre-processed data is then fed to Extreme Gradient Boosting algorithm (XGBoost), which is a state of the art performer algorithm for prediction purposes in data analysis field. The experimental results show that by real time monitoring of data from cloud, sepsis can be predicted 6 hours prior to the onset of sepsis with an accuracy score of 0.994 and AUC score of 0.867. It is also observed that demographic parameters play a vital role in sepsis prediction. The Since the parameters used for early prediction can be easily acquired with the help of sensors, the proposed approach proves its potential for development of mobile and website applications for patient monitoring, real-time prediction of sepsis and generation of appropriate alert system.
Deep learning perspectives a review
Authors: Jithendra Uppu , Mittal Usha 
Published: January 2021

Abstract
Deep learning is a very successful field in the area of machine learning. This has made tremendous achievements in a variety of fields, such as voice recognition. Computer vision and natural language perception. Large data with today ‘s vast data give large possibilities and transformative opportunities to different industries and face unprecedented challenges in using information and data. As the figures rise, profound learning goes on. It is important to develop analytical tools for big data. This paper discusses some of the perspectives of profound learning.
Input Data Characterization Using Machine Learning and Deep Learning
Authors: Shukla Rati , Kumar Vinod , Yadav Vikash , Rahul Mayur 
Published: January 2021

Abstract
Seeing the current trends in Information technology, a large volume of heterogeneous data is produced widely across the world by means of social media sites such as Facebook, Instagram, Google plus, etc. and electronic gadgets used by humans for instance sensors. The generated data act as garbage and makes no sense until they are categorized. A strong need of data analytics or data filtering came into existence to filter the data into concrete categories. The generated data contain enormous features with huge dimensions. The high dimension data must be reduced to low dimension data to avoid curse of dimensionality and to build a better machine learning (ML) model.
A ML model is built to perform the classification task that result into labeling of the data. In the first phase of work, a framework has proposed to process the data having features belonging to text and images using ML and DL algorithms. In the second phase of work, we have shown data related behavior using ML and DL framework. The proposed work is generic in sense of FS techniques and FE techniques using ML classifiers and DL classifiers. After carefully implementing the algorithms on the datasets we have evaluated the results taking accuracy as our performance metric. After carefully analyzing the accuracy it can be concluded that DL algorithms give better results than ML algorithms on both text and image dataset.
Identifying and sensing emotional quotient weightage in the outcome using Speech Dialogue
Authors: Yadav Rajat , Mishra Anurag 
Published: January 2021

Abstract
Emotion is a very important part of any interaction whether it is human-human or human- computer interaction as an emotion conveys the essence of the interaction. Decision making is also influenced by the emotional state of a person. If we can capture emotions from a human-computer interaction then it will be very beneficial for the computer to learn from that interaction. It can be efficiently used by an artificial intelligence system to understand and give response accordingly. The method proposed in this paper uses a machine learning based approach to detect emotion from the text obtained from the speech dialogue.
A comparative study of classifier algorithms for Twitter’s sentiment based spam detection
Authors: Kumar Santosh , Kumar Ravi , Haider Mohammad , Dubey Anil 
Published: January 2021

Abstract
In today’s time Social media is a vital part of everybody’s life. People are expressing their feeling and emotions on social media platforms. Emotions are associated with the daily life experiences of everyone. Twitter is one of the mostly used social media platform. From analysis on social media, we can predict the mental status of users. In this paper a comparative study of user’s posts on Social media has been done. The approach is based on relevant keywords. Sentiment analysis and classification of emotions is done using Bayes Network Classifier, Naive Bayes Classifier, Logistic Regression, Simple Logistic, SMO, J48 pruned tree, and Random Forest. Finally, accuracy of classification is evaluated by different classifiers.