bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influence of the Thermal-force Effect on the Process of High-temperature Synthesis of the Ni3Al Intermetallic Compound
Authors: Ovcharenko V E , Akimov K O , Boyangin E N , Baohai Yu 
Published: October 2019

Abstract
In this work, the intermetallic compound Ni3Al was obtained by high-temperature synthesis under pressure at various values of the preliminary pressure on the initial powder mixture (3Ni+Al). The study of pressure-time and displacement-time diagrams gave a coherent picture of the synthesis passage over time. It was found that an increase of preliminary pressure leads to a decreasing of the powder compacts porosity. In this regard, the smallest displacement of the press plunger after initiating the synthesis reaction in the powder compact was observed at the highest value of the preliminary pressure on the compact. The role of preliminary pressure on the initial powder mixture in the process of the grain structure formation of the Ni3Al intermetallic compound synthesized under pressure was determined.
Semi-supervised Soft Sensor Modeling Method for Crude Furfuryl Alcohol Distillation Process Based on Cosine Similarity-Discount
Authors: Li L H , Dai Y S 
Published: October 2019

Abstract
In view of the dynamic time variation and multirate data problems in crude furfuryl alcohol distillation process, too few samples of modeling data caused poor generalization ability of soft sensor model and reduced the accuracy of soft sensor model prediction. To solve this problem, this paper proposes a semi-supervised soft sensor modeling method based on cosine similarity-discount(SSSMI-COSD). By calculating the cosine similarity between labeled and unlabeled samples in the same time interval, and combining with the proposed constraints, the data clustering between labeled and unlabeled samples is realized. In addition, in order to avoid the ill-conditioned problem caused by high-dimensional input variables, the clustered data are fused by discount factor value (DFV). An actual data of a crude furfuryl alcohol distillation process (CFADP) simulation experiment were carried out. The results show that the proposed SSSMI-COSD method can effectively improve the soft sensor model prediction accuracy for a crude furfuryl alcohol distillation process.
The Car-Following Model Based on Fuzzy Inference Controller
Authors: Li Guanzhen , Zhu Wenxing 
Published: October 2019

Abstract
Many existing researches mostly establish a theoretical car-following model from a perspective of statistical physics, but the vehicle driver is the decision-maker of driving behaviors. When modeling the micro-behavior of the vehicle, the driver’s driving intention and decision-making basis need to be considered. In order to consider human factors more in microscopic traffic simulation, this paper uses fuzzy inference technology as the basis, selects the headway distance, ideal speed difference, and reasonable spacing difference as the input variables of the control system, considering the free running and car-following state of the vehicle, constructs fuzzy rules and establishes a car-following model.
Research and Implementation of Intelligent ERP Platform for SMEs Based on Cloud Computing
Authors: Wen Yong 
Published: October 2019

Abstract
According to the characteristics and business needs of small and medium-sized business management, the paper analyzes and designs the functions and performance of ERP platform, applies Internet of Things and artificial intelligence technology, and builds a dynamic mobile intelligent SME ERP platform through business database, rule database and knowledge database. The platform adopts three-tier B/S architecture, uses MySql as the back-end database, uses Java to encode, implements financial management, supply chain management, manufacturing, human resource management and other functional modules, and finally carries out platform testing. The implementation results show that the intelligent SME ERP greatly reduces the workload of business personnel and improves the efficiency of real-time dynamic management of enterprises.
An Improved Location Model for Pedestrian Detection
Authors: Zou Tengtao , Cao Chen , Yang Shangming 
Published: October 2019

Abstract
Pedestrian detection in complex scenes has always been a research difficulty in computer vision. The performance of current methods was seriously degraded when the pedestrian is occluded or the size of pedestrians are too small, etc. In this paper, we propose a novel approach based on location model for the detection of multiple pedestrians, which aims to improve the efficiency of algorithms in complex scenarios. In the model, a fully convolutional neural networks for the classification of pedestrian and non-pedestrian are trained to learn pedestrian features first. Then trained model are used to search for pedestrian regions, and the regions where pedestrian may be present will be activated and marked with boxes. Finally, we fine tune the boxes to overlap them with the ground truth more precisely. Compared with the current methods on two pedestrian datasets, experimental results demonstrate the comparable performance of our approach in term of miss rates.
A Glucose Prediction Model based on Variational Mode Decomposition and Least Squares Support Vector Regression
Authors: Wen S , Li H R , Han H H , Yu X 
Published: October 2019

Abstract
Online prediction of subcutaneous glucose concentration plays a critical role in glucose management for type 1 diabetes. In this work, a new method combining Variational Mode Decomposition (VMD) and Least Squares Support Vector Regression (LSSVR) is proposed with three main stages to improve the prediction accuracy. Firstly, the time series of blood glucose are decomposed into different frequency series by VMD method. Secondly, the LSSVR model is trained to predict each subsequence. Finally, the predicted sequences are reconstructed to obtain the overall glucose predictions. The experimental results demonstrate the effectiveness and accuracy of the proposed model for short term glucose prediction.
Deep Neural Networks in the View of Ecological Holism
Authors: Yue Qiang , Yue Xun 
Published: October 2019

Abstract
In the view point of ecological holism, deep neural networks can be view as a machine cognitive agent with overall multi-level nested structure, the representation learning and problem-solving ability of machine cognitive agent with multi-modal perception function is also the results from the perception and judgment of ambient situation environment. In the paper, a machine cognitive method based on ecological holism was deconstructed. Then, two core questions to characterize learnability of AlphaGo were answered, one is how to explain the characteristics of the hierarchical gradient and update parameters between different layers of networks? Two is how to understand the effective ability to choose the drop sampling at the strongest level of game of Go. These core controlling structural and functional roles proposed in this study may be further improved to a new type of artificial intelligence law of machine cognitive system.
Knowledge-oriented Hierarchical Neural Network for Sentiment Classification
Authors: Wang Yanliu , Li Pengfei 
Published: October 2019

Abstract
Sentiment classification aims to classify the sentimental polarities of given texts. Lexicon-based approaches utilize lexical resources to explore the opinions according to some specific rules, whose effectiveness strongly depends on the goodness of the lexical resources and the rules. Traditional machine-learning methods tightly rely on feature engineering and external NLP toolkits with unavoidable errors. Deep learning models strongly rely on a large amount of labelled data to train their numerous parameters, which often suffer from overfitting issue since it is difficult to obtain sufficient training data. To address the issues, we design a model that combines Knowledge-oriented Convolutional Neural Network (K-CNN) and bidirectional Gated Recurrent Neural Network (biGRU) in a hierarchical way for sentiment classification. Firstly K-CNN is used to capture the n-gram features in sentences. Sentiment word filters are constructed in the knowledge-oriented channel of K-CNN based on the linguistic knowledge from SentiWv ordNet, which can capture the sentiment lexicons and alleviate overfitting effectively. Then biGRU with attention mechanism is utilized to model the sequential relations between sentences and obtain the document-level representation based on the relevance of each sentence to the final sentiment classification. Experiments on two datasets show that our model outperforms other classical deep neural network models.
Research on Tourism Destination Attraction Based on Deep Learning
Authors: Li Shanshan , Qiu Canhua , Jiang Mingming 
Published: October 2019

Abstract
Mining and analyzing online travel reviews and travel information is playing an increasingly important role in the tourism industry. Accurately capturing the uniqueness and attractiveness of the tourist destinations recorded in the travel notes is the key to tourism analysis and application. The current way to obtain the attraction of tourism is easy to cause bias due to the use of simple statistical methods. This paper proposes a model based on deep learning, which uses Bert pre-training method, based on Transformer, and mines travel notes through Attention to find the attraction point. The model can understand the chapter-level semantics of travel notes based on the context, so much so that the extracted features are closer to the meaning of the text. It also exhibits good performance in generating unique labels of tourist destinations and similar tourism clusters. The experimental results are consistent with the facts, the validity of the model is also proved.
Artificial Intelligence Promotes the Evolution of English Writing Evaluation Model
Authors: Su Zheqian , Miao Liu , Man Jiang 
Published: October 2019

Abstract
On the background of “Internet +” era, the domestic higher education is showing the trend of artificial intelligence. The reliability and scientificity of computer intelligent evaluation are further carried out, and the mode of intelligent evaluation and data analysis in optimizing the precise teaching of English writing is explored, which can lay a foundation for the large-scale use of the technology. Based on data-driven theory, this study further analyzed the role of AI in promoting in-depth learning by comparing AI writing review model with manual review model.
Study on Low Characteristic Formulation Optimization of Solid Propellant Based on Improved Particle Swarm Optimization
Authors: Zhao Jiu-Ling 
Published: October 2019

Abstract
The combustion product of solid propellant produces strong plume flow field downstream of nozzle outlet, which improves plume characteristics, reduces plume characteristic signals, and improves stealth characteristics and survivability of missile. In this paper, an intelligent algorithm is proposed to optimize the formulation of solid propellant with low characteristic signal. An improved multi-objective particle swarm optimization algorithm is used to solve the formulation optimization model, and several feasible formulations for realizing low characteristic signal are obtained. On this basis, considering the energy performance, a formula screening method is given to calculate various characteristic signals and energy properties of the feasible formulation. The comprehensive measurement can not only take into account the optimization target requirements of propellant energy performance and plume characteristic signals, solve the conflict problem of reducing multiple characteristic signals, but also improve the efficiency of formulation design, and provide a reference for formulation designers.
A Novel Multi-information Fusion Algorithm Based on Population for Wind Power System
Authors: Wang Yufeng , Xu Chunyu 
Published: October 2019

Abstract
Reserve capacity optimization of power system plays an important role in large-scale Wind Power System. In this paper, a novel multi-information fusion algorithm based on population (MIFA-P) is proposed, which can balance the problem of reserve capacity optimization. MIFA-P determines the type of optimization problem by counting the maximum number of projections on the principal characteristic axis of the optimal individuals of the current population. Therefore, the corresponding class of evolutionary algorithm is chosen to solve the problem. In the process of evolution, each evolutionary algorithm achieves the organic integration through sharing population information. Finally, the validity and applicability of MIFA-P algorithm for large-scale decision variable black box optimization problem are verified by solving a practical scenario optimization problem of reserve capacity distribution in power system after wind power integration.
Grey Level Co-occurrence Matrix (GLCM) as a Radiomics Feature for Artificial Intelligence (AI) Assisted Positron Emission Tomography (PET) Images Analysis
Authors: Tamal Mahbubunnabi 
Published: October 2019

Abstract
Positron Emission Tomography (PET) allows tumour microenvironment to be studied in vivo with high sensitivity and specificity. Inter- and intra-tumour morphological and phenotypic heterogeneity or pattern provided by PET images are of critical importance. The traditional practice of visual interpretation of these images are not sufficient enough to extract all the information embedded in the images. On the other hand, simultaneous development of automated and reproducible analysis methodologies makes it possible to extract large amount of quantitative features from these images which is termed as radiomics. Analysis of these radiomics feature using artificial intelligence (AI) can significantly improve individualized treatment selection and monitoring. Grey level co-occurrence matrix (GLCM), a member of texture based radiomics feature family is widely used as a biomarker of heterogeneity and can provide information of the tumour microenvironment. The GLCM can subsequently be used for artificial intelligence (AI) assisted tumour diagnosis, monitoring of progression and treatment planning as well as for monitoring response to therapeutic intervention. This aim of the study was to investigate the accuracy and robustness of PET based GLCM in varying image acquisition and analysis conditions using phantom data. It has been observed that GLCM based textural features (e.g., correlation, entropy, homogeneity, energy contrast and dissimilarity) are not only dependent on the volume but also on the quantization level. They are also dependent on signal-to-noise ratio (SNR) and image contrast. The dependencies of these features to the varying imaging conditions are also not linear and cannot always be directly related. To use these GLCM derived textural features as biomarkers for AI assisted analysis, all the information regarding the textural features should always be included along with the changes in volumes and contrast of the PET images in the training dataset.
Unsupervised Learning of Depth and Visual Odometry using Photometric Calibration
Authors: Yu Tianhong 
Published: October 2019

Abstract
Depth and visual odometry estimation are two essential parts in SLAM systems. Compared with traditional algorithms, supervised learning methods have shown promising results in single view depth estimation and visual odometry estimation. However, they require large amounts of labeled data. Recently, some unsupervised approaches to estimate depth and odometry via minimizing photometric error draw great attention. In this paper, we present a novel approach to learn depth and odometry via unsupervised learning. Our method ameliorates the original photometric loss to enhance the robustness to illumination change in real scenarios. In addition, we propose a new structure of Pose-net and Explainability-net to achieve rotation-sensitive odometry results and more accurate explainability masks. The experimental results have demonstrated that our approach achieves better performance than existing unsupervised methods in both depth and odometry results.
Research on Intrusion Detection and Target Recognition System Based on Deep Learning
Authors: Hu Xianwei , Li Tie , Wu Zongzhi , Gao Xuan 
Published: October 2019

Abstract
Intrusion target detection and recognition are of great significance to security protection of oil and gas fields. An intrusion detection system is built with the integration of infrared image acquisition module, infrared image processing module, moving target detection module and recognition module. Traditional target recognition algorithm highly relies on manual design feature extraction algorithm, which requires designer to have adequate prior knowledge, and cannot avoid the influence of subjective factors of people. Intrusion detection and target recognition system are proposed based on deep learning, which uses neural network algorithm. Deep learning model is built through feature extraction and training of acquired images of intrusion objects, and thus subsequent invasion objects are detected and recognized. Intrusion detection is achieved through simulation of human brain, which boasts of more intelligent recognition process and more accurate recognition results compared with traditional recognition method. According to applications in real scenario, the system proposed has better detection and recognition results and great practical value.
Research on Action Recognition Method Based on Weighted DTW Algorithm
Authors: Zhang Zhen , Zhang Ya 
Published: October 2019

Abstract
Action recognition technology is an important part of artificial intelligence. In order to improve the rate of action recognition, this paper designs an action recognition method based on Weighted DTW algorithm. Firstly, the Kinect2.0 is used to obtain the three-dimensional data of the human joint points. Then the quaternion method is used to define the action sequences. The weight of joint is calculated according to the participation in different types of action. Then the improved DTW algorithm is used to design the action recognition experiment. The experimental results show that the action recognition algorithm designed in this paper has better recognition rate and timeliness than traditional DTW algorithm and F-DTW algorithm.
A Case Study on Three-Dimensional Single-Beacon Localization for Autonomous Underwater Vehicles
Authors: Yu Huapeng , Han Chao 
Published: October 2019

Abstract
Circular trajectory is most frequently used when an autonomous underwater vehicle calibrates positions of underwater acoustic beacons or corrects its own navigation errors using beacons. However, the space-time effect on control and navigation data processing and the impact of the angle of attack on three-dimensional localization performance of AUVs have rarely been mentioned in previous studies. To improve the three dimensional localization performance of AUVs with a single-beacon aiding, methods for space-time trade-offs from a control perspective and determination of the optimal angle of attack are investigated in this study. Through theoretical deduction aided with numerical simulation, concerned quantitative values with their corresponding preconditions are given, and some general conclusions which can greatly improve the navigation capability of AUVs have been made.
The Simulation of Magneto-Mechanical Properties of Magnetorheological Elastomers
Authors: Zhao Dan , Wang Bing , Zhao Zhenghang , Wang Shuaihu , Yang Song , Dong Na 
Published: October 2019

Abstract
Magnetorheological elastomers is an intelligent material which can sense and judge the external magnetic field and change its mechanical properties. In this paper, functional carbon nanotubes are added to polyurethane magnetorheological elastomers. According to the micro-mechanical finite element calculation, the mechanical parameters of the equivalent matrix composed of carbon nanotubes and polyurethane elastomers are obtained. Multi-scale simulation analysis of the magneto-mechanical properties of isotropic magnetorheological elastomers with different volume fractions of carbon nanotubes in tensile, compressive and shear modes using magnetic coupling software. The simulation results show that the magnetorheological elastomer with larger volume fraction of carbon nanotubes has higher storage modulus in the three modes than the magnetorheological elastomer with smaller volume fraction of carbon nanotubes.
Model Experiment of Construction Process of Long-Span Concrete Arch Bridge Rigid Skeleton
Authors: Zhang Shuangyang , Zhao Ren-da 
Published: October 2019

Abstract
High-speed railway passenger dedicated line have high demands for track smoothness, beipanjiang bridge as shanghai-kunming high-speed railway control project, mainspan 445m, constructed by using rigid skeleton, C80 high-strength concrete filled steel tube structure for the first time used in railway bridge; rigid skeleton in the process of construction has the traits: long span, long construction period, multi-step, mechanical behavior was extremely complex. In this paper, the design principle and similarity relation of rigid skeleton model are introduced, simplified the model construction process, and the finite element simulation analysis model is established, compared with measured values, it is shown that model test can well simulate the long span rigid skeleton arch bridge stress state in the construction process; the original construction steps have more safety reserves than simplified; the indicators of model test are within the scope of design, the results can provide technical support for the bridge construction.
Air to Air Heat Exchanger Mimicked from the Directional Passive Water Transport Featured by the Skin Morphology of Phrynosoma Cornutum
Authors: SÁNCHEZ Federico , Zhu Shifan 
Published: October 2019

Abstract
A biomimetic approach is proposed to improve the performance of an intercooler using only the passive water transport featured by the lizard species Phrynosoma Cornutum for racing applications. The lizard is able to transport in a preferred direction, water over its integument whilst consuming no energy to do so. That feature is used in the external surface of the intercooler and an evaporation rate is desired. The model started with the air conditions after the turbo charger stage, Then, the convection of air in the heat exchanger is modelled by the boundary layer theory which is applicable for the scale used justified by the Péclet number value, for both the heat and mass transfer. The convection heat transfer was satisfactory modelled and the values reached inside the expected values for the optimal 2 mm wall thickness selected to demonstrate the enhancement due to the mass transfer and a surface temperature of 62.02 °C was stabilized for the conditions selected under a few iterations. According to the mass convection theory a mathematical model of the heat exchange was made and for the heat lost there exist a drawback due to a deviation from real values due to the highly turbulent flow under the conditions selected and the very small value of the Schmidt number obtained in the literature and pooled to the lack of a proper equation to compute the Sherwood number, which was not found and modelled outside the Schmidt number range. To solve this, a correction factor of the water evaporation is introduced which assumes that not all the water from the channels evaporates and some part is taken by the turbulent air as water droplets, but it needs further validation by real experiments, which is expected to be the next stage of the project.