bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fault detection for PEM fuel cell using kalman filter
Authors: Ramamoorthy D , Che Mid E 
Published: January 2020

Abstract
Fault detection plays a key role in high cost and safe-critical systems to avoid the occurrence of an abnormal event. This work presents a model-based fault detection in the water management system of proton-exchange membrane (PEM) fuel cell stack using Kalman filter (KF). The KF is a model-based FD method relies on analytical redundancy to predict the related fault disturbed by some noise. Then, it corrected the faults with its mathematical model to minimize the error covariance between the normal and faulty scenarios by a repetitive process. Two faulty scenarios; flooding and drying which happen at anode and cathode side of PEM fuel cell are investigated. The proposed FD method using KF has successfully detected flooding and drying faults in PEMFC.
Triple-band antenna with defected ground structure (DGS) for WLAN/WiMAX applications
Authors: Ibrahim Amirudin , Arina Fazil Nur , Dewan Raimi 
Published: January 2020

Abstract
This paper presents a triple-band rectangular patch antenna that can be operated in three different resonance frequencies of 2.40 GHz, 3.50 GHz, and 5.80 GHz for WLAN and WiMAX applications. The FR-4 substrate with dielectric constant of 4.3 is used, with the dimensions of the substrate 34×30×1.6mm3. The performance of the rectangular patch antenna has been simulated and measured and the proposed patch antenna operates at three different resonance frequencies of WLAN and WiMAX which are 2.35-2.54 GHz, 3.45-3.57 GHz and 5.74-5.83 GHz. These results correspond to a simulated bandwidth of 197 MHz, 118 MHz and 90 MHz. The radiation pattern of nearly omni-directional is obtained. Overall, the results obtained show the designed antenna have a good performances of the three operating bands
Big Data and Automatic Detection of Topics: Social Network Texts
Authors: Silva Jesús , Hernández Palma Hugo , Niebles Núñez William , Ruiz-Lazaro Alex , Varela Noel 
Published: January 2020

Abstract
This paper proposes the analysis of the influence of terms that express feelings in the automatic detection of topics in social networks. This proposal uses an ontology-based methodology which incorporates the ability to identify and eliminate those terms that present a sentimental orientation in social network texts, which can negatively influence the detection of topics. To this end, two resources were used to analyze feelings in order to detect these terms. The proposed system was evaluated with real data sets from the Twitter and Facebook social networks in English and Spanish respectively, demonstrating in both cases the influence of sentimentally oriented terms in the detection of topics in social network texts.
Using Big Data to Determine Potential Dropouts in Higher Education
Authors: Viloria Amelec , Senior Naveda Alexa , Hernández Palma Hugo , Niebles Núñez William , Niebles Núñez Leonardo 
Published: January 2020

AbstractIn higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics.
Implementation of Micro CT in CAD/CAM dentistry for image processing and soft computing: a review
Authors: Yajvinder  , Gulati V 
Published: January 2020

Abstract
All This review discusses the merits and area of improvement of various radiology aids employed in current dentistry and also refers to scope in CAD/CAM dentistry. However, present radiology devices are playing an important role in clinical dentistry but their area of application is limited due to harmful effects of x-ray exposure to human and high operating cost. Radiology device like Micro CT (μ-CT) is powerful tool to assess the dental prosthesis fabricated using CAD/CAM but radiology devices can also be used in whole process of dental prosthesis manufacturing using CAD/CAM. This study retrieves the interaction of advanced radiology devices in current CAD/CAM dentistry and also highlights the correlation of Micro CT’s input variables and acquired image resolutions using available data in reviewed studies.
An efficient high speed squaring and multiplier architecture using yavadunam sutra and bit reduction technique
Authors: Deepa A , Marimuthu C N , Murugesan C 
Published: January 2020

Abstract
Vedic Mathematics, an ancient Indian technique can be used to solve any arithmetic problems in an easy and simple way. A novel high speed Vedic squaring and multiplier unit is designed using the principles of Yavadunam sutra and the bit reduction technique is projected in this paper. The complexity of the multiplier is reduced as the bit reduction technique is employed and later the Yavadunam sutra is implemented for the calculation of the deficiency. The size of the proposed N bit multiplier is reduced to N-1 bit and also considerable speed improvement is achieved. The architecture is designed and realized using Xilinx Spartan FPGA and synthesized using 90nm and 180nm technology synopsys device.
SPPBPS: A Secure Privacy-Preservation Bilinear Pairing Scheme for Bitcoin Cryptocurrency
Authors: Kofi Kwansah Ansah Albert , Lu Li , Adu-Gyamfi Daniel , Anokye Stephen , Tang Chenfeng 
Published: January 2020

Abstract
Despite the existing advances on research and education in cybersecurity and its applications, the field is still under discovery and new technologies are evolving. In recent years, Bitcoin as a cryptocurrency cyber application that records all financial transactions present on a blockchain where users can reach a secure and robust consensus on transactions emerges as the most popular peer-to-peer electronic payment system. Cryptocurrencies mostly rely on blockchain, as a secure decentralised append-only ledger to exchange digital currencies, and thereby attracting a billion-dollar economy. Bitcoin allows input transactions to link addresses and this can be used by an adversary to reveal real user identity. This obviously attracts privacy issues such as user’s information and identity leakage. Several existing techniques are found not satisfactory to fulfil practical and compatible anonymity requirements for users and their transaction. We present in this paper, a secure privacy-preservation scheme based on bilinear pairing and other cryptographic primitives. We proved that our proposed scheme is robust and can be implemented to preserve users and transaction privacy in the Bitcoin system by theoretical analysis and evaluation.
Data Mining Applied in School Dropout Prediction
Authors: Viloria Amelec , García Guliany Jesús , Niebles Núñez William , Hernández Palma Hugo , Niebles Núñez Leonardo 
Published: January 2020

Abstract
In recent years, many studies have emerged about regarding the topic of school failure, showing a growing interest in determining the multiple factors that may influence it [1]. Most of the researches that attempt to solve this issue [2] are focused on determining the factors that most affect the performance of students (dropout and failure) at the different educational levels (basic, middle and higher education) through the use of the large amount of information that current computer equipment allows to store in databases. All these data constitute a real gold mine of valuable information about students. But, identifying and finding useful and hidden information in large databases is a difficult task [3]. A very promising solution to achieve this goal is the use of knowledge mining techniques or data mining in education, which has resulted in so-called Educational Data Mining (EDM) [4]. This new area of research is concerned with the development of methods for exploring data in education, as well as the use of these methods to better understand students and the contexts where they learn [5].
Parallel Algorithm for Reduction of Data Processing Time in Big Data
Authors: Silva Jesús , Hernández Palma Hugo , Niebles Núñez William , Ovallos-Gazabon David , Varela Noel 
Published: January 2020

Abstract
Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today’s applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics of current computer architectures to run several processes concurrently [2]. This paper proposes a parallel version of the FuzzyPred algorithm based on the amount of data that can be processed within each of the processing threads, synchronously and independently.
Single and Double-Threaded Offloading Strategies and Computational Analysis in Mobile Cloud Computing
Authors: Xia Siyu , Jiu Mingyuan , Fan Chenglong 
Published: January 2020

Abstract
At present, mobile devices are hard to support the increasing number of intensive applications due to their limited power and fixed storage size, therefore, how to improve the computing power and also reduce the energy consumption is a crucial problem in the development of mobile terminals. In order to solve this problem, a new offloading scheme is proposed based on the combination of double-thread computing scheme and offloading strategy in this paper. First, we combine single and double-threaded computing with mobile cloud computing and obtain the optimal offloading strategy through component partitioning and the single-threaded application theorem. Then the energy consumption is analyzed on the basis of double-thread calculation. Finally, we compare the performance of single-thread and double-thread offloading strategy in terms of energy consumption and delay of mobile cloud computing. The results show that the double-thread offloading strategy has the least energy consumption and has better characteristics.
Estimating Median in The Multi-sourced Heterogeneous Data Set: A distributed implementation
Authors: Gao Tian , Zhao Yanyu , Deng Xinyi , Li Wenze , Li Hongjun , Zhao Xin 
Published: January 2020

Abstract
The continuous running of enterprise applications produces a huge volume of business data that reside in different storage and system environments and owned by different companies or organizations, which forms a typical distributed, multi-sourced heterogeneous dataset. The multi-sourced heterogeneous data set provides big potential values for official statistics. While the median is a commonly used indicator in official statistics, it is not a trivial task to estimate the median in the distributed computing environment of multi-sourced heterogeneous data set due to its mathematical nature. In this paper, we proposed a distributed method to estimate the median value for the multi-sourced heterogeneous data set. Mainly considering the different size of multi-sourced data set and unevenly distribution of their data values, we first improve the traditional interpolation based median estimation method for the multi-sourced heterogeneous data set. Then, we propose distributed implementation for the proposed median estimation method based on web service technology. Finally, we evaluate the accuracy and performance of proposed method through experimental study.

CCS Concepts

Information systems ➝ Data management systems ➝ Information integration ➝ Mediators and data integration
A New Adaptive Differential Evolution Algorithms
Authors: Tian Yu , Li Tinghui 
Published: January 2020

Abstract
In this paper, we describe a New Adaptive Differential Evolution algorithm (NADE) based on adaptive mutation operator, crossover operator and new mutation strategy. It is mainly aimed at the existence of individual aggregation and the reduction of population diversity in the calculation process of Differential Evolution algorithm (DE), which makes the algorithm easy to get early. The problems of ripeness, slow convergence speed and low convergence accuracy are improved. The improved differential evolution algorithm is tested by five commonly used test functions, and the test results are compared with the other three algorithms. The results show that the proposed algorithm performs better in convergence speed, convergence precision and global convergence ability.
Research on Network Attack Effect Evaluation Based on Confrontational Perspective
Authors: li Jingxuan , Qiu Han , Zhu Junhu 
Published: January 2020

Abstract
The cyberspace battlefield is full of strategic confrontations between attack and defense sides. However, the existing evaluation methods do not consider the impact of defense strategies on attack effect, and only carry out unilateral attack effect evaluation from the perspective of attacker, which cannot reflect the dynamic changes of attack effect against different defense strategies. To solve this problem, we propose the Confrontation Oriented Attack Effect Evaluation Model (CO-AEEM) to evaluate the attack effect from the perspective of both attack and defense sides. By creating CO-AEEM, we establish a multi-level evaluation index system covering all-assests-elements of the target object, and propose an attack effect evaluation algorithm to reduce the impact of subjective factors and adapt to the complex characteristics of network confrontation, then design the comparison and evaluation process of attack test and confrontation test according to the changes of defense measures. Finally, the simulation experiments in various confrontation scenarios are carried out in network range, which show that the model can not only describe the actual attack effect in confrontation with different defense strategies, but also the effectiveness degree of defense strategies against various types of attacks. Thus, the evaluation results of CO-AEEM can provide scientific guidance of action decision making and equipment development in network counterwork.

CCS Concepts

•Security and privacy➝Formal methods and theory of security •Computing methodologies➝Modeling and simulation.
Real-time Online Prediction of Data Driven Bearing Residual Life
Authors: Wang Haiya , Yu Zhongqing , Guo Lu 
Published: January 2020

Abstract
In order to realize the predictive maintenance of key components under massive vibration data, real-time online prediction of the remaining life of different types of bearings, a data driven real-time online prediction method for bearing residual life is introduced. The method realizes the construction of the data-driven bearing residual life prediction model by selecting the bearing vibration data Spearman characteristic parameter selection, principal component analysis (PCA), health index fusion, and BP neural network fitting. The built model is continuously updated by real-time online acquisition of data to achieve real-time online prediction of bearing residual life. The accuracy and feasibility of the method for predicting the remaining life of different types of bearings are verified by experiments. Using this method to predict the remaining life of the bearing helps to achieve predictive maintenance of critical components, reduce unplanned downtime, increase production efficiency, and reduce production costs.
The Influence Analysis of Time Synchronization Error on Direct Source Position Algorithm
Authors: Zhao Yu , Xiao Ling , Si Chang Zhe , Liu Jianfeng 
Published: January 2020

Abstract
It is very difficult to eliminate the time synchronization error completely. The influence of time synchronization error on direct source position algorithm was researched. The signal model with time synchronization error was given firstly. Then the CRLB of position estimation under the condition that the time synchronization error is exist was derived. In the last, the influence was analyzed by Monte Carlo simulations. The simulation results show that the Cramer-Rao lower bound, under the condition the time synchronization error is exist, is deteriorated obviously compared with the condition the time is synchronized perfectly; when the time synchronization error is shorter than the signal sample period, the CRLBs are same with different time synchronization errors; however the position accuracy of direct source position algorithm drops sharply, if the time synchronization error is not being considered.
Research on Calibration of Short Base Metal Thermocouples
Authors: Zhe Wang , Songlin Yu , Xinming Zhao , Qiangguang Li , Wenjie Shen 
Published: January 2020

Abstract
Short and cheap metal thermocouple is a general term for detachable short base metal thermocouple (electrode length is about 300mm-500mm) and short base metal armored couple (metal sleeve length is about 300mm-500mm). Their electrodes are short in length. Traditional measuring equipment often fails to accurately measure the indication deviation in the high temperature region (above 300 °C). Through experiments, this paper finds out the reasons of inaccurate measurement. Then, this paper designs a thermostat at the terminal, which can solve this problem. The design can effectively reduce the extended uncertainty of short-form even value deviation.
Application of GBDT for division of petroleum reservoirs
Authors: Qin Yaqiong , Ye Zhaohui , Zhang Conghui 
Published: January 2020

Abstract
Traditional methods of dividing petroleum reservoirs are inefficient. The machine learning method has been applied in the two-class and three-class tasks of the reservoir, but there is no research on the classification of all classes. In this paper, the GBDT (Gradient Boosting Decision Tree) model is proposed for the reservoir classification problem in argillaceous sandstone areas, and the model is implemented by XGBoost algorithm. The classification effect of this model is better than that of multiple-hidden-layer neural networks, and the macro-average AUC is 0.89.
Short-term Prediction of the CSI 300 Based on the BP Neural Network Model
Authors: Ning Shunyu 
Published: January 2020

Abstract
In this paper, BP neural network is adopted to predict the Shanghai-Shenzhen 300 Index (CSI 300) during a short period of 60 days (beginning from March 7th, 2018), results of which are used to determine the prediction effect of the BP neural network model on CSI (China Securities Index) 300. In addition, the present study explores the prediction effect of the BP neural network during different time periods by grouping predicted results according to the length of time. It is found that the BP neural network model performs well in predicting the CSI 300. The prediction results reveal that the price would overall decline, the fluctuation frequency would be small, and the price fluctuations would be small. Comparison between the groups during different time periods shows that prediction during a time period of less than 20 days or more than 50 days incurs larger errors, the prediction during a time period of 30 to 40 days is more accurate, and predictions errors increase sharply during the time period of more than 50 days.
Research on Customer Satisfaction Measurement of Online Tourism Platform Based on Value Chain- Take Company X for example
Authors: Wang Ying , Liu Xi 
Published: January 2020

Abstract
With the vigorous development of the economy and Internet, improving customer satisfaction has become an important issue faced by online tourism platform providers. How to scientifically evaluate customer satisfaction is the key factor to accurately define the impact of customer satisfaction, and also the premise and basis to effectively improve customer satisfaction. This paper decomposes the value chain of Online Travel Agency (OTA) and constructs OTA customer satisfaction model from the perspective of OTA value chain activities. According to the characteristics of OTA value chain flow; this paper puts forward OTA customer satisfaction measurement scale, and takes Company X as an example to make an empirical analysis. Entropy weight method calculates the weight of all levels of indicators. Fuzzy comprehensive evaluation method is used to determine the level of customer satisfaction of all levels of indicators. Through the analysis of empirical results, countermeasures and suggestions are put forward to improve the overall service quality of OTA and customer satisfaction, which can provide reference for the service quality of online tourism industry in China.
Business Value of Telecom Operators’ Big Data
Authors: Hong Ying , Li Zheng , Wang Jun 
Published: January 2020

Abstract
Big data is one of the infrequent resources in the 21st century, especially for Internet & Mobile Internet industries. Telecom operators have large amount of precious data. Through analysis of the features of telecom operators’ data, this study highlights the value of telecom big data in business and classifies it into two major categories. The first one is enhancing existing business to optimize traditional telecom services, like customer experience, telecom product design and network management. The other is developing derivative business such as advertisement, marketing, financial or credit services based on the strength of telecom big data. This preliminary research provides insights into the value development of telecom operators’ data on account of targeted discussion about practical application for telecom big data.
Sampling Statistics Research Based on Quality Evaluation of Electricity Meters from Different Manufacturers
Authors: Gu Zhen , Zhao Fang , Chen Jintao , Jiang Jianfeng 
Published: January 2020

Abstract
It is of great significance to study and analyze the measurement performance and sampling inspection methods of smart meters, and to conduct sampling inspection according to the quality status of smart meters, so as to improve the reliability of smart meters. On the basis of qualified rate test and error test of electric energy meters, radar chart and Wilcoxon rank sum test method are adopted to carry out quality inspection comparison of electric energy meters of the same specification from different manufacturers, and sampling statistical test method is studied accordingly. The method described in this paper is suitable for quality analysis and sampling inspection of electric energy meters.
Identification of maize disease based on transfer learning
Authors: Sun Xiaolin , Wei Jiangshu 
Published: January 2020

Abstract
To reduce the time and energy needed by agricultural experts to evaluate maize diseases and improve the accuracy of image recognition of maize diseases, considering that the maize disease dataset is a small sample dataset, a method of image recognition of maize disease based on transfer learning is proposed, which is to fine tune the pre-trained convolutional neural network model. This paper compares and analyzes the experimental results of training only the last layer of Inception-v3 / Inception-v4 model and training all parameters of Inception-v3 / Inception-v4 model, and compares the results with the initial training VGG16, AlexNet and other models. The experimental results show that the method of transfer learning can reduce the training time of network, and the trained deep convolutional neural network can recognize maize diseases.