bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Improved Algorithm Inspired by Natural Biology for Image Enhancement Used in Space Station
Authors: Wei Bo , Hu Lei , Shu Sihao , Yang Rong , Miao Jianguo 
Published: September 2019

Abstract
In the space station, the uneven illumination of image brings significant challenges for image enhancement in vision system due to high dynamic range of light intensity and the light reflection of metal facilities. In order to solve this problem in monitoring, unattended operation and robot operation, Retinex algorithm was proposed to estimate the luminosity. However, the effectiveness of this method is highly dependent on the selected path and consumes huge computational cost. This paper proposes an algorithm inspired by the Archimedes spiral in natural environment to improve the processing time and enhance the images quality. First, the structure and mechanism of Human Visual System (HVS) are analyzed. Second, the original image is decomposed into the brightness and color layers based on the structural human eyes mechanism. Third, the brightness layer is processed by the improved Retinex algorithm to enhance the original image by merging it with the color layer. The proposed algorithm is validated with the experimental results.
A GRU Model for Aspect Level Sentiment Analysis
Authors: Xing Yongping , Xiao Chuangbai 
Published: September 2019

Abstract
Sentiment analysis is a basic task of natural language processing, while aspect level sentiment analysis is an important topic in sentiment analysis. In the same sentence, different words have different influence on the sentiment polarity of aspect, so the key to solve the problem is how to build a relation model between the aspect and the words in the sentence. In this paper, by using two recurrent networks, we built a model for sentence and introduced attention mechanism to fuse aspect information, so as to achieve a better effect. An experiment on public dataset show that the proposed algorithm obtain a better result without carrying out complex feature engineering.
Research Status and Tendency of Intelligent Industrial Robot
Authors: Wen Bo , Tao Bo , Li Gongfa 
Published: September 2019

Abstract
In order to adapt to the overall industrial upgrading of the global manufacturing industry, adding the artificial intelligence technology to the field of industrial robots has a very realistic and far-reaching significance. This paper will analys the research status of intelligent industrial robots at home and abroad. Then we will focus on the introduction of industrial robots based on deep learning and machine vision. According to the present situation of industrial robot industry at home and abroad, we will put forward the development tendency of intelligent industrial robot in the future.
Double-layer Support Vector Machine for Robust and Efficient Classification
Authors: Wu Yuting , He Yigang , Shi Luqiang , Sui Yongbo , Huang Yuan , Cheng Tongtong 
Published: September 2019

Abstract
In this paper, double-layer support vector machine (DLSVM) for binary classification is proposed that can expeditiously learn a classification model under the premise of ensuring accuracy. In the first layer, least squares support vector machine (LSSVM) is used to evaluate all samples in the data set. Support vector machine (SVM) utilize a sparse data set to train the binary classifier in the second layer. In order to obtain excellent sparse data set for SVM, the normalized norm sequence and the normalized Lagrange multiplier Shannon entropy sequence are introduced. DLSVM provides low computation cost because the two-layer structure reduces lots of iterative operations. Experimental results show that the proposed method not only has similar classification accuracy compared to SVM but also has higher efficiency.
A Review of Online Sequential Extreme Learning Machines
Authors: Deng Chang Hui , Wang Xiao Jun , Gu Jun , Wang Wei 
Published: September 2019

Abstract
One of the challenges for machine leaning on big data is the effective and efficient leaning of large-scale and on-going explosion data which is always with the concept drift problem. To meet the challenge, learning algorithms/techniques performed well on large-scale data and also with the evolvable property are desired. The OS-ELM family has strong potential as viable alternative techniques for the computation of large-scale and on-going explosion data in more fields of applications/tasks. This work reviews the most important and latest works in OS-ELM family. The review consists of two topics, one related to the improved version of OS-ELM which aims at overcoming the disadvantages of OS-ELM, and the other related to the extended version the goals of which is to add some specialties to OS-ELM. It is expected that the review will support a certain research in the future.
Stability-Based Parameter Selection for Data-Driven Model-Free Adaptive Controllers
Authors: Deng Kai , Yang Chunhua 
Published: September 2019

Abstract
The algorithm of model-free adaptive control normally includes a control algorithm and a pseudo-partial derivative estimation algorithm. The selection of each parameter in the algorithm directly affects the control performance and stability of the system. Two artificial additional parameters have been introduced for the algorithm, but the corresponding theoretical basis needs to be improved. Although the range of artificial additional parameters is given in the control algorithm, the stability condition of the system cannot be guaranteed. In this paper, the theoretical basis for introducing two additional parameters is provided by modifying the cost function. Furthermore, the selection conditions of control parameters are given such that the controlled system stable and not entirely dependent on the trial-and-error. The simulation results are given to confirm the theoretical findings.
BiLSTM-CRF Chinese Named Entity Recognition Model with Attention Mechanism
Authors: Wan Zhongbao , Xie Jie , Zhang Wei , Huang Zhaohua 
Published: September 2019

Abstract
In order to make up for the weakness of insufficient considering dependency of the input char sequence in the deep learning method of Chinese named entity recognition task, this paper proposes a method, which integrate Bidirectional Long Short-Term Memory (BiLSTM), attention mechanism and add the information of word vector. Firstly, the proposed model obtains the char vector feature extracted from the text corpus, which is then input to the BiLSTM model; Secondly, the attention mechanism is used to calculate the relevance between the current input char and the other input char of the BiLSTM model; Finally, the global feature is obtained according to relevance, concatenating the word vector feature, which is introduced to the Conditional random field(CRF) layer to perform the mutual constraint between tags. Thus, the classified result can be obtained. Based on the corpus of the Chinese Peoples’ Daily Newspaper in 1998, our experiments show that the proposed method can improved the performance and efficiency of named entity recognition, compared to the existing deep-learning method that combines word vector and char vector.
A new type of fruit and vegetable preservation equipment based on PLC control
Authors: Xie Yonghui , Liu Kai , Zhou Quan 
Published: September 2019

Abstract
In this paper, a new air conditioning and fresh-keeping control system is designed and studied, PLC technology, frequency conversion technology, sensor technology, parameter self-tuning PID technology and configuration monitoring technology affecting the fresh-keeping of environmental factors such as temperature, gas composition, humidity intelligent, energy-saving control and dynamic monitoring, ultimately achieve prolong time for fruits and vegetables, reduce the loss of the fruits and vegetables preservation equipment operation and energy saving purpose.
Design and Research of Dynamic Stiffness Test System for Rubber Shock Absorber Based on LabVIEW
Authors: Zhang Dasen , Chu Jian 
Published: September 2019

Abstract
Rubber shock absorbers are an indispensable component in the fields of ships, automobiles, trains, airplanes and construction engineering. The dynamic stiffness value is an important indicator to measure the performance of rubber shock absorbers. Some existing dynamic stiffness testing methods are often difficult to meet the production enterprises and testing requirements of the testing unit. According to the testing requirements of rubber dynamic characteristics and the development of dynamic stiffness testing technology for rubber shock absorber at home and abroad, designing and developing a new type of rubber vibration damper dynamic stiffness testing technology. Through the USB6008 data acquisition card establishing the connection between the collected data and the data analysis platform based on LabVIEW software, realizing the real-time, accurate and efficient measurement of the rubber shock absorber.
Air Combat Situation Assessment Based on Improved RS Theory
Authors: Ji Huiming , Han Qisong , Yu Minjian , Li Xuyu , Zhang Zhenxing 
Published: September 2019

Abstract
In order to improve the ability of the extraction and weight distribution of key air combat factors in air combat situation assessment, this paper improves the rough set theory and establishes an improved rough set theory air combat situation assessment model. Firstly, the air combat information is discretized and decoupled. Then, the improved rough set theory is used to reduce and assign the attributes, and the air combat situation is calculated and evaluated. Finally, the effectiveness of the model is verified by simulation.
A Multi-strategy Shuffled Frog-leaping Algorithm for Numerical Optimization
Authors: Tang Kezong , Zhan Tangsen , Li Zuoyong 
Published: September 2019

Abstract
This study proposes a multi-strategy shuffled frog-leaping algorithm for numerical optimization (MSSFLA), which combines the merits of a frog-leaping step rule, the crossover operator, and a novel recursive programming. First, the frog-leaping step rule depends on the level of attractive effect between the worst frog and other frogs in a memeplex, which utilizes the advantages of frogs around the worst frog, making the worst frog more conducive to the evolution direction of the whole population. Second, the crossover operator of the genetic algorithm is used for yielding new frogs based on the best and worst individual frog instead of the random mechanism in the original shuffled frog-leaping algorithm (SFLA). The crossover operation aims to enhance population diversity and conduciveness to the memetic evolution of each memeplex. Finally, recursive programming is presented to store the results of preceding attempts as basis for the computation of those that succeed, which will help save a large number of repeated computing resources in a local search. Experiment results show that MSSFLA has better performance than other algorithms on the convergence and searching effectivity. Therefore, it can be considered as a more competitive improved algorithm for SFLA on the efficiency and accuracy of the best solution.
Psychophysics of Consumer’s Sequence Preference of E-commerce Loan Repayments
Authors: Lu Yang , Li Yaning 
Published: September 2019

Abstract
As E-commerce shopping has been growing rapidly in China, many consumers use loans to make a purchase, and then payoff the loan by installments. Recently studies show that all amortizing loans can be viewed a sequence of payments, and consumers prefer a decreasing sequence for a car loan. In this paper, we investigated consumers’ preference of payment sequence for a less valued online purchased commodity, a smart phone. Results show consumers prefer constant sequence over decreasing sequence, indicating that consumers have unique perception of sequence for a smaller amount and shorter term loan, and they may choose constant sequence for convenience. E-commerce credit provider shall stick with the constant repayment plan.
Optimization of Radiation Model of Infrared Decoy with Neural Network
Authors: Zhou Guanwu 
Published: September 2019

Abstract
In this paper, a numerical model is proposed to calculate the infrared radiation characteristics based on the solution of radioactive transport. However, due to the demand of the real-time, a method with back-propagation neural network (BPNN) is developed to optimize the proposed model. The results of simulation experiments show that BPNN possesses good prediction accuracy, and can make the numerical model achieve the real-time requirement.
Construction of Cloud Platform for Mining Group Operation
Authors: Wang Hao , Hu Nailian , Li Guoqing , Hou Jie , Huang Shuwei 
Published: September 2019

Abstract
The operation important for mining group management. The traditional operation is complicated and inefficient for many reasons. Information technologies such as cloud computing, big data, and business intelligence, are inevitable requirements for the quality and efficiency of the mining group. In this study, a cloud platform structure for mining group operation was established with virtualization resource layer, cloud computing platform layer, and service access layer. Then the business logic on cloud platform was designed by using business model, computing logic, business intelligence tools, mathematical model and analysis method, which consists of distributed data acquisition, multidimensional data processing and storage, and business intelligence oriented analysis and decision-making. Finally, a case study was taken out at a large mining group which significantly increases the efficiency of management. The study suggests that Cloud Platform for Mining Group Operation can greatly improve the efficiency of information circulation and provide reliable decision support.
Identifying Users Based on Time-Frequency Characteristics
Authors: Gao Yaqiong , Chen Wanghu , Yang Bo , Mu Yuxiang , Li Jing 
Published: September 2019

Abstract
With the rapid development of information technology, whether it is with regard to the personalized recommendation of the business or network warning by the public security organ, the identification of online users is a very important research topic. The user’s web browsing behavior data contains potential behavioral characteristics, in this paper, we utilize the time-frequency analysis method, which extracts the behavior characteristics when users accessing the network, so as to obtain the user behavior pattern and calculate the similarity between the behavior patterns for achieving the purpose of user identification. This method is verified by the weblog, and the experiment results show that the method extracts user network behavior features based on time-frequency analysis to ensure the accuracy of user identification.
AGRP and ESPL1 as Biomarkers of Brain-Metastasis in Lung Adenocarcinoma
Authors: Deng Jianzhi , Cheng Xiaohui , Zhou Yuehan 
Published: September 2019

Abstract
In the mortal diseases, lung cancer, approximately one fifth of lung adenocarcinoma(LUAD) patients are associated with brain metastasis(BM). And even some patients die of BM. In the present research, the 22753 genes data of 273 primary LUAD or BM samples were downloaded from gene expression omnibus(GEO) datasets. The 145 common differentially expressed genes(DEGs) both from GPL96 and GPL570 platform profiles were screened out by R package. Gene ontology(GO), pathway and protein protein interaction(PPI) network analysis of the DEGs was enriched by online tools. The 14 up- and 131 down-regulated genes were enriched in 18 GO terms and 147 signal pathway. Protein expression of AGRP and ESPL1 is lower in lung cancer than many other cancers. And the km-plots of lung cancer survival curves are also shown that AGRP and ESPL1 express higher in high risk groups. From the research in this paper, we can believe that AGRP and ESPL1 might be the biomarkers for the diagnostic clue of BM from LUAD.
Design of Air Combat Deduction System Based on UML Language
Authors: Ji Huiming , Han Qisong , Yu Minjian , Ding Haiyang 
Published: September 2019

Abstract
According to the characteristics of high cost and great difficulty in air combat exercise, relying on information equipment, this paper designs an air combat deduction model system. Firstly, the system is designed with the idea of ‘Human input, system deducts automatically’, and the key modules of the system are designed. Then the radar search model, missile motion model and the airborne fire control system model are established. Finally, the static model, dynamic model and data flow direction of the system are designed respectively by selecting system use case diagram, sequence diagram and coordination diagram, and the overall model of the system is built based on UML language.
Wavelet-transform Algorithm Application in Hybrid Power System Optimization of Electric Vehicles
Authors: Zhang Yulong , Yang Shichun , Zhou Sida , Chen Fei 
Published: September 2019

Abstract
In order to reduce the changing times of battery’s output power loads and currents, a novel energy management strategy was developed for hybrid power system including a battery pack and an ultracapacitor (UC) pack. The strategy is utilizing wavelet-transform algorithm to allocate power components with different frequency contents among the on-board energy sources. The models of hybrid power system were developed and simulated in MATLAB/Simulink. Simulation results show that: Compared with the logic threshold strategy, the wavelet control method can avoid battery to receive chaotic high-frequency components more effectively. Meantime, the utilization of ultra-capacitor can be improved. Thus battery can be protected and the efficiency of electric vehicles can be increased.
Research on Calibration of Optical See-Through Head-Mounted Display
Authors: Yu Hailong , Liu Guozhong 
Published: September 2019

Abstract
In order to successfully apply augmented reality technology and provide users with immersive 3D experience of augmented reality, it is necessary to calibrate the optical see-through head-mounted display (OST HMD) of augmented reality equipment. With the help of the video tracking system, a new display calibration method was proposed, in which the data of the video tracker were taken as input and the 3d coordinates of the visual virtual model were taken as output to find the projection transformation matrix between the 3d coordinates of the virtual space and the 3d coordinates of the real space. In addition, in order to obtain the video tracker data conveniently, the 3d calibration target with checkerboard was designed. The display calibration experiment was carried out on Microsoft Hololens platform. The experimental results show that the displacement error of the reprojection of the virtual object is 2.7cm within the active range of 80cm*80cm*100cm of the tracker.
Role of monsoon interannual variability on the climate model prediction of seasonal rainfall over Indonesia
Authors: Suaydhi  
Published: September 2019

Abstract
Hindcast produced by a model used in a numerical model-based seasonal prediction system is an essential part of the operational seasonal prediction system. This paper is aimed at evaluating the performance of POAMA and CFSv2 models in predicting the interannual variability of seasonal rainfall over Indonesia. The data used in this research are obtained from POAMA m24 model and CFSv2 model. A seasonal empirical orthogonal function analysis is used for examining the year-to-year variation of seasonal rainfall. The results reveal the influence of ENSO on seasonal rainfall over Indonesia. These results are well simulated by both models, although there is a decrease in the accuracy of the models for longer lead-time. The areal average may suggest that POAMA m24 model with a resolution of T24 has better accuracy for longer lead time, but CFSv2 with a resolution of T126 is superior in simulating the spatial pattern of rainfall over Indonesia.
Estimation of intrinsic vulnerability of shallow groundwater in Jombang District, Jombang Regency, East Java, Indonesia based on Aquifer Vulnerability Index
Authors: Siswoyo H , Azhar R F 
Published: September 2019

Abstract
The level of safety of groundwater to pollution hazards can be identified based on the level of vulnerability of the groundwater to pollution. Determination of groundwater vulnerability intrinsically can be done by using the Aquifer Vulnerability Index (AVI) method based on two physical parameters i.e. the thickness of each layer above the uppermost of saturated aquifer surface and the estimated hydraulic conductivity of each of these sedimentary layers. The objective of this study was to determine the level of groundwater vulnerability intrinsically based on the vulnerability index value of aquifers by using the AVI method. This study was conducted based on sample of 25 dug wells scattered in the Jombang District of Jombang Regency, East Java, Indonesia. Based on the results of the analysis using the AVI method, it can be stated that the hydraulic resistance logarithm in the study area were found to be in the range from -5.22 to 3.84 with the intrinsic vulnerability of groundwater to pollution hazards from extremely high to low.
The implementation of connecting, organizing, reflecting, extending to improve mathematics connection grade 11 science student at one of Christian Senior High School in Rantepao
Authors: Virginiawaty K K , Saragih M J 
Published: September 2019

Abstract
Someone with mathematical connection skills can have a deep understanding of mathematics and have a complete view of mathematics without being separated. From the observations, it was found that the mathematical connections of the grade 11 science students in one of the Christian high schools in Rantepao were still low, where all students could not solve the problem with the mathematical connection indicator beyond the minimum score that already given by the school. This study aims to determine whether or not the CORE model can improve students’ mathematical connections. The data sources used are student test sheets, mentor checklist sheets, student reflection journals, and researcher reflection journals. The research method used by researchers is classroom action research according to Pelton. The results showed an increase in students’ mathematical connections with the application of the Connecting, Organizing, Reflecting, Extending models of XI IPA students at one of the Christian High Schools in Rantepao.
Clarification and encapsulation of Stevia rebaudiana Bertoni water extract
Authors: Martono Y , Pranawati F E R , Riyanto C A , Muninggar J 
Published: September 2019

Abstract
This study is carried out for clarification and encapsulation of water extract from Stevia rebaudiana Bertoni. Clarification was performed using adsorption method of acid activated kaolin. Encapsulation of S. rebaudiana extract was carried out using spray drying method. Product evaluation was performed based on concentration of stevioside and rebaudioside A, moisture content, hygroscopicity, and sensory. The content of stevioside and rebaudiosida A was determined by HPLC. Clarification of S. rebaudiana water extract by using acid activated kaolin was successfully reduce 95% green pigment and 65% yellow pigment. The content of stevioside and rebaudioside A in the optimal encapsulation products is 0.83% and 2.25%, respectively. Water content and hygroscopicity of sweetening products obtained were 9.8% and 12.91%, respectively. Sweetener products still have a lower level of sweetness based on organoleptic test results.