bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Hybrid Chaotic Oscillatory Neural Network (HCONN) Based Financial Time Series Prediction System
Authors: Qiu Yifu , Lee Raymond S. T. 
Published: October 2019

Abstract
Financial time series prediction is one of the most complex and challenging problems in both AI and finance engineering. In our research, we proposed a Hybrid Chaotic Oscillatory Neural Network (HCONN) model by replacing the traditional sigmoid-based activation function with chaotic oscillatory activation function, which provides significant performance in the global minimum convergence through the application of Adaptive Moment Estimation optimizer. In addition, by integrating the latest R&D on Quantum Finance Theory (QFT) and its Quantum Price Level (QPL) as the deep features’ extraction, we add the daily 8 nearest QPLs together with the time series price variables as the input of our HCONN. In terms of system implementation, 12 different forex products including the AUDCHF, AUDUSD, CADCHF, EURAUD, EURCHF, EURGBP, EURUSD, GBPAUD, GBPCAD, GBPUSD, USDCAD and USDCHF are used. System performance results reveal that HCONN outperforms other financial models including: Feedforward Backpropagation Neural Network (FFBPN) and Chaotic Oscillatory Neural Network (CONN) in terms of training performance and forecast accuracy.
A Deep Fully Convolutional Network for Distal Radius and Ulna Semantic Segmentation
Authors: Wang Shuqiang , Liang Wei , Wang Hongfei , Chen Zhuo , Lu Yiqian 
Published: October 2019

Abstract
Semantic segmentation is an essential step to do further image analysis and scene understanding tasks. In medical imaging analysis applications, it is even more challenging to do automatic segmentation due to tissues’ complicated boundaries. In this paper, a fully convolutional network (FCN) based model is constructed to segment distal radius and ulna (DRU) areas from hand X-ray images. We evaluated the proposed network on a clinical DRU dataset with different network configurations. The proposed network can achieve 98% accuracy and 96% mean Intersection over Union (IoU).
Short-term Time Series Data Prediction of Power Consumption Based on Deep Neural Network
Authors: Xu Kang , Hou Ruichun , Ding Xiangqian , Tao Ye , Xu Zhifang 
Published: October 2019

Abstract
Under the background of the rapid development of Internet technology and the popularity of smart grids, the analysis and prediction of short-term time series data of users’ power consumption has important guiding significance for grid planning, management decision of economic sector and optimization and allocation of power resources. Considering that the traditional statistical-based time series analysis method is weak in generality and can not handle the complex linear problem in prediction, the long-term dependence of the ordinary cyclic neural network model is insufficient, and the time series data has multidimensional problems, a deep neural network is proposed. The PCA-LSTM model is used for time series data prediction. The model firstly uses the PCA (principal component analysis) method to reduce the dimensionality of the electricity consumption time series data, optimizes the number of input variables, and inputs the data into the long- and short-term memory network LSTM for training prediction. The experimental results show that the LSTM network prediction based on PCA improves the accuracy of short-term time series data prediction, and also improves the convergence speed of LSTM network. It proves that the method has better prediction performance and versatility.
A Hybrid Meta-Heuristic Solution to Operation Optimization of Hot Oil Pipeline
Authors: Li Bo , He Miao , Kang Yang , Shi Haoyu , Zhao Jiali , Liu Yan 
Published: October 2019

Abstract
In the process of pipeline transportation, the temperature and pressure of waxy and high viscosity crude oil are decreasing. In order to complete the pipeline transportation task, stations are usually set at intervals to overcome the pressure loss caused by friction and impact in the flow and the temperature loss caused by the radial temperature difference. The main equipment of the station is pumps and heaters. The operating fee of pumps and heaters is the core costs of hot oil pipelines, so it is necessary to reasonably optimize the pump and heater operating schemes. Because the hydraulic and thermodynamic of the hot oil pipeline are coupled with each other, and there are a lot of nonlinear constraints and integer variables, this optimization problem is a kind of mixed integer nonlinear programming, which is difficult to solve by using the traditional optimization method. In this paper, a hybrid meta-heuristic algorithm based on particle swarm and pattern search is proposed, which can deal with the problem effectively. The operational cost of the optimized scheme is much lower than that of the actual operating scheme. So this method can provide guidance for the cost reduction and efficiency improvement of the hot oil pipeline.
On-line Data Analysis and Optimization of Crankshaft Dynamic Balance
Authors: Zhou Chuanhong , Ding Lei , Lin Lianghuang 
Published: October 2019

Abstract
The crankshaft is a high-speed rotating component that is widely used in many fields. Dynamic balance of the crankshaft is an important process stage that affects the quality of the crankshaft. In order to optimize the quality of crankshaft dynamic balance machining, this paper illustrates the importance of the initial unbalance of crankshaft to the dynamic balance machining of crankshaft. For the initial imbalance distribution of crankshaft, the improved mean method, gradient descent method and mean shift algorithm are used to calculate the machining center. The position of the hole realizes the crankshaft imbalance correction with different effects and reduces the processing strength of the crankshaft dynamic balance phase. The study is applicable to the machining of crankshafts in geometric centering processes in batches.
Empirical Research on Connotation and Elements of Small and Micro Entrepreneurial Ability of Students in Vocational Colleges
Authors: Hua Hefeng , Bao Yanli 
Published: October 2019

Abstract
Higher vocational college students’ entrepreneurial ability is based on the characteristics of higher vocational students and that of vocational colleges. It has similarities in entrepreneurial ability, but also has their own characteristics. Different from emphasis on scientific and technological entrepreneurship, higher vocational college students focus on the fields such as service and sales, and the small and micro entrepreneurial ability of higher vocational students emphasizes the use of skills and opportunity development. This paper makes an empirical study on the small and micro entrepreneurial ability of higher vocational students, and finds that their entrepreneurial ability is composed of skills practice ability, opportunity development ability, entrepreneurial operation ability and failure handling ability. The small and micro entrepreneurship of higher vocational students is an important part of college students’ entrepreneurship. The development of their ability will improve the employment competitiveness of higher vocational students and the success rate of entrepreneurship.
Crawling Chinese-Myanmar Parallel Corpus: Automatic Collection, Screening and Cleaning Corpus
Authors: Xiong Kai , Yuan Rui , He Wenxue , Jing Yanmei , Wang Yansheng , He Qiqi , Li Huafu 
Published: October 2019

Abstract
The collection of Chinese-Myanmar Parallel Corpus (CMPC) is the key step in the natural language processing (NLP) and training Machine Translation Engine (MTE) of Southeast Asia minority languages. As the scarcity of CMPC resources that efficient corpus collection methods are worth studying extremely. Traditional corpus collection methods include manual collection, text recognition of books and Internet crawlers, etc. Among them, the most efficient method to collect corpus is internet crawler preached by many. Traditional Internet crawler algorithm is interfere easily by a lot of spamming and advertising that lead to the time-consuming and low-precision. We propose a web crawler mechanism combines acquisition automatically technology bilingual website list, crawling corpus and cleaning corpus to obtain high quality parallel corpus. Firstly, using the hyperlinks to recursively access related corpus websites through building the website graph. Furthermore, the breadth-first, Backline and PageRank crawler framework used to build a corpus selection model based on crawling with threshold, matching link, ranking the heat of page, through this, the CMPC can be found accurately. Finally, the corpus cleaning model based on the HTML parsing to determine a set of standardized token sequences. By testing the Chinese-Myanmar reptile algorithm established in this paper, the experimental results show that our benchmarks this model exceeds previous published benchmarks. Up to now, we have obtained 1.1 million parallel corpus pairs of Chinese-Myanmar.
End to End Person Re-Identification Based on Attention Mechanism
Authors: Li Yang , Xu Huahu , Bian Minjie 
Published: October 2019

Abstract
This paper treats person re-identification (re-id) as a sequential model, guide person re-id with person detection, combines recurrent neural network (RNN) with attention mechanism, and proposed an end to end person re-id method for surveillance scenarios. The feature of target person is firstly extracted using ResNet, and then the feature is added to the long short-term memory (LSTM) network to guide the attention model for the region of interest in the surveillance image. Finally, combined with the information observed from the image multiple times, the most similar candidate person in the image is deduced, and the feature distance is calculated and ranked for person re-id. This paper strengthens the relationship between person detection and person re-id, and reduces the error between models. Because the number of candidate person matched with target person is reduced, this method can process person re-id task with less calculation and time. This paper also verified the effectiveness of the proposed method by experiments comparing a variety of person detection and re-id methods on several person re-id datasets.
Research on Facial Expression Recognition Based on Voting Model
Authors: Fei Yang , Jiao Guo 
Published: October 2019

Abstract
In order to improve the recognition rate of real-time classification of facial expressions, we proposed a method of facial expression recognition based on voting mechanism. Firstly, different neural network models are constructed to learn facial features. Then, the extracted features are fed into the classifier to obtain the posterior probability of various features. Finally, through the voting mechanism, the optimal decision-making level fusion is achieved to complete the facial expression classification. Experiments show that the average recognition rate of fer2013, CK+ and JAFFE database is 74.58%, 100% and 100% respectively. Compared with other recognition methods, experiental data show that this method has superior performance, improves the recognition rate and robustness of the algorithm, and ensures the universality of the algorithm.
Design of Attitude Controller of Two-Dimensional Correction Projectile Based on UAS-SMC Method
Authors: Zhong Yang , Wang Liangming , Fu Jian 
Published: October 2019

Abstract
For the spin stabilized two-dimensional trajectory correction projectiles with utation and precession phenomenon, an attitude controller with sliding mode control method and unidiational auxilairy surfaces (UAS-SMC) is proposed in this paper. And the underactuated control allocation in two-dimensional trajectory correction projectile with UAS-SMC controller is also discussed here. Simulation results show that the proposed controller can effectively control the attitude of the projectile and has strong robustness, which provides reference for engineering applications.
Bayesian Sparse Linear Prediction with Pearson Type VII Distribution
Authors: Wan Hongjie , Zhang Haiyun 
Published: October 2019

Abstract
The speech signal can be modelled as AR models with an innovation noise model. The Pearson type VII distribution is used to model the real excitation. Variational Bayesian framework is used to estimate the posteriors of the AR coefficients and noise model parameters. The model is not conjugate, so MCMC is embed into VB framework to estimate the degree of freedom (DOF) parameter of Pearson type VII distribution. The model order selection is carried out by setting ARD priors on the coefficients. Simulation is carried out on synthetic and real data, the results show that the algorithm performs well for linear prediction both for synthetic data and speech signal, and the results are better than using least square method.
The Natural Characteristics and Forced Response of O-shaped Plate Under Isotropic and Orthotropic Materials
Authors: Shi Dongyan , Ren Wenhui , Lan Zhongxin , Li Yongxin 
Published: October 2019

Abstract
In this paper, the O-shaped plate with different materials are used to analyze their natural frequencies at different boundaries and different plate thicknesses and the forced response under the action of point forces. By the improved Fourier series method, the discontinuity of coupling system at all boundaries in the overall solution domain is overcame. Through the energy principle and the Rayleigh-Ritz technology, the matrix equation of the system governing equations can be obtained. In addition, based on various materials, boundary conditions and thicknesses, some new results have been demonstrated, which may become the foundation for the future research.
Research on Artificial Post-Treatment Technology of FDM Forming Parts
Authors: Liu Jinjin , Gu Hai , Li Bin , Zhu Lu , Jiang Jie , Zhang Jie 
Published: October 2019

Abstract
3D printing technology is a high and new technology gradually developed from the mid-1980s, leading the manufacturing technology of the third revolution. The 3D printing process includes modeling, data processing, selective process printing and post-processing. FDM technology is the most widely used technology at present, but the molding accuracy is not high. Due to the “step effect”, the printed parts have rough surface, obvious stripes, poor surface quality, and cannot meet the customer’s or specified requirements, so post-processing is very important. This paper mainly studies and summarizes the manual post-processing technology of FDM printed parts, and provides the specific implementation method of post-processing, providing reference for the post-processing of FDM formed parts and other forming processes.
eMineProve: Educational Data Mining for Predicting Performance Improvement Using Classification Method
Authors: Rosado Jovel T. , Payne Angelica P. , Rebong Corazon B. 
Published: October 2019

Abstract
Today, Data mining has become a universal tool for converting data into useful information and knowledge. Student’s academic performance is a crucial factor in building their future [3]. In this study, the classification method is selected to be applied on the students’ data. Classification is a form of data analysis that extracts models describing data classes [4]. The data is collected from the Basic Education Department of the University of Perpetual Help System Laguna. The raw data set is a collection of 4, 250 data accumulated over two academic years regarding the basic information of the Grade 7 Junior High School students. The data mining technique applied was classification using Naïve Bayes. The data mining tool used to process the data into useful knowledge is RapidMiner. The main objective of this study is to predict the performance improvement of Grade 7 Junior High School students for A.Y. 2016-2017 and 2017-2018 using classification. It specifically aims to identify the general average of the Grade 7 Junior High School students when grouped according to gender, identify who perform better between male and female, identify the subject on which the students excel most, identify the subject on which students have difficulty, identify who performs best when group according to last school attended, identify the academic performance of the students based on their parent’s occupation, provide a predictive analysis of data to help the decision makers create a marketing strategy for those schools where only few students enrolled. Naïve Bayes produces accuracy 92.37% that shows it is possible to obtain a good prediction model based on the academic performance of the students. Based on the data for marketing strategy, the model has an accuracy of 30.97%.
FEM strategies for Large Scale Thermo-Mechanical Simulations with Material Non-linearity
Authors: De Bona Francesco , Benasciutti Denis , Moro Luciano , Novak Jelena Srnec 
Published: October 2019

Abstract
Numerical techniques based on the Finite Element Method (FEM) are mature to provide computational tools that permit multi-physical problems to be dealt with. A relevant case concerns thermo-mechanical simulations of industrial components and processes. Nevertheless, a thermo-mechanical FEM model generally requires a transient non-linear analysis where the variation of material properties with temperature, as well as plasticity and creep, have to be considered. It follows that large scale models are often obtained and unfeasible computational time is thus required. The aim of this work is to put in evidence the possible scale reduction that can be achieved introducing model simplifications based on a practical engineering approach.
The Numerical Analysis of Screw-type Centrifugal Pumps with Different Rotary Impellers
Authors: Yue Wang , Jin Liu , Juan Xu , Gang Liu , Yuanming Zhang , Zongrui Hao 
Published: October 2019

Abstract
Screw-type centrifugal pumps with different rotary layers have been numerically simulated. Internal flow fields of Single-rotary impeller and double-rotary impeller are simulated by Reynolds Navier-Stokes function and k-ϵ viscous model. Stream lines, velocities, and pressure variations are calculated. The stream lines of impellers of two types show that the flow directions are significantly different. The flow distribution inside the pump chamber is approximately the same, and the flow rate is quite different. The pressure distribution of the single-rotary impeller is more uneven than the double-rotary impeller. With different rotary layers, the pressure range has a big difference. The mechanism that the internal flow field of single-rotary impeller pump differs from double-rotary impeller pump rotating layers causes such a large difference in the flow field is discussed.
Technology Research and Application on Catalyst Entire Life-cycle Management of Coal Fired Power Plant
Authors: Zhang Yang , Feng Qianwei , Li Sha , Li Cunwen , Pei Yukun , Zhu Yue 
Published: October 2019

Abstract
Along with the comprehensive promotion of flue gas ultra-low emission reconstruction in coal-fired power plants, the stability, reliability and economic efficiency of SCR denitrification facility have emerged. SCR catalyst entire life-cycle management is the key to solving the problem. Given the present situation of the catalyst market, operation and maintenance, third-party service, a SCR catalyst entire life-cycle management technology have been proposed, including catalyst supply, quality control, operation and maintenance, effect optimization, life management and scrap disposal in the entire life-cycle of catalyst. By clarifying the accountability units at different stages, implementing corresponding quality control, operation and maintenance and evaluation and optimization technology, demonstration application suggest that the management technology could give full play to the professional expertise of participant companies, make the denitrification facility stable, reliable and economical, and bring obvious environmental and economic benefit.
Application Research on Magnetic Coupling Stress Testing Technique for In-service Steel Bridge
Authors: Weng Guangyuan , Shi Yun , Zhang Yumin , Dai Jianbo 
Published: October 2019

Abstract
Magnetic effect of ferromagnetic materials was discussed, the magneto-mechanical properties of ferromagnetic materials were studied, as well, Jiles Atherton constitutive mode was studied. Through the establishment of concrete filled steel tubular arch bridge structure calculation model, analysis the boom in tension and compression of the magnetic coupling. Results show that the resultant magnetic coupling model of Q345 steel derrick, the boom and load changes, the change of the magnetic parameters is more sensitive, can be better applied to the stress testing of such components.
Multi-terminal electronic transport in boron nitride encapsulated TiS3 nanosheets
Authors: Papadopoulos Nikos , Flores Eduardo , Watanabe Kenji , Taniguchi Takashi , Ares Jose R , Sanchez Carlos , Ferrer Isabel J , Castellanos-Gomez Andres , Steele Gary A , van der Zant Herre S J 
Published: October 2019

Abstract
We have studied electrical transport as a function of carrier density, temperature and bias in multi-terminal devices consisting of hexagonal boron nitride (h-BN) encapsulated titanium trisulfide (TiS3) sheets. Through the encapsulation with h-BN, we observe metallic behavior and high electron mobilities. Below  ∼60 K an increase in the resistance, and non-linear transport with plateau-like features in the differential resistance are present, in line with the expected charge density wave (CDW) formation. Importantly, the critical temperature and the threshold field of the CDW phase can be controlled through the back-gate.
Lattice SUSY for the DiSSEP at λ2 = 1 (and λ2 = −3)
Authors: Johnston Desmond A 
Published: October 2019

Abstract
We investigate whether the dynamical lattice supersymmetry discussed for various Hamiltonians, including one-dimensional quantum spin chains, by (Fendley et al 2003 J. Phys. A
36, 12399424; Yang and Fendley 2004 J. Phys. A
37, 893748; Hagendorf and Fendley 2012 J. Stat. Phys.
146, 112255) and (Hagendorf et al 2013 J. Stat. Phys.
150, 60957; Hagendorf and Liénardy 2017 J. Phys. A
50, 185202; Hagendorf and Liénardy 2018 J. Stat. Mech. 033106) might also exist for the Markov matrices of any one-dimensional exclusion processes, which have the additional constraint of zero column sums by comparison with the spin chains. We find that the DiSSEP (Dissipative Symmetric Simple Exclusion Process), introduced by Crampe et al in (Crampe et al 2016 Phys. Rev. E
94, 032102; Vanicat [arXiv:1708.02440]), provides one such example for suitably chosen parameters. The DiSSEP Markov matrix admits the supersymmetry in these cases because it is conjugate to Ising (λ
2 = 1) and Δ = −1/2 XXZ (λ
2 = −3) spin chain Hamiltonians which also possess the supersymmetry. The consequences for the spectrum of the DiSSEP Markov matrix are discussed. We also note that the length-changing supersymmetry relation for the DiSSEP Markov matrix M

L
and the supercharge Q

L† for L sites,

M

L

Q

L
†

=

Q

L
†

M

L
−
1

, is reminiscent of a ‘transfer matrix’ symmetry that has been observed in other exclusion processes and discuss the similarity.
An interactive web-based tool to guide the preparation of neutron imaging experiments at oak ridge national laboratory
Authors: Zhang Yuxuan , Bilheux Jean-C , Bilheux Hassina Z , Lin Jiao Y Y 
Published: October 2019

Abstract
An interactive web-based tool has been developed at Oak Ridge National Laboratory (ORNL) to guide the end-user sample preparation for neutron imaging experiments. The tool is capable of estimating transmission through the sample using the cold neutron spectrum at the High Flux Isotope (HFIR) CG-1D imaging beamline. It can also predict the position and height of the resonance peaks at the Spallation Neutron Source (SNS) SNAP beamline when performing neutron resonance imaging with neutron energies higher than 1 eV. This tool provides robust and user-friendly sample input and utilizes measured/simulated beam spectrum at corresponding beamlines for accurate transmission/attenuation calculations. By using this tool, users who are interested in neutron imaging can test their ideas promptly and can better prepare samples for their experiments.