bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved magnetostrictive properties of cobalt ferrite (CoFe2O4) by Mn and Dy co-substitution for magneto-mechanical sensors
Authors: Keswani Bhavna C. , Patil S. I. , Kolekar Y. D. , Ramana C. V.
Published: November 2019

The present study explores the effect of Dy3+ rare-earth ion substitution on the crystal structure, morphology, and magnetic properties of magnetostrictive Co0.7Mn0.3Fe2O4 spinel ferrite and demonstrates their potential applications in magnetomechanical sensors. The intrinsic CoFe2O4 and Dy-substituted Co0.7Mn0.3Fe2−xDyxO4 (x = 0.0–0.1) were prepared by the standard solid-state chemical reaction method. X-ray diffraction studies along with the Rietveld refinement confirm that all the samples exhibit single-phase cubic spinel structure with space group Fd3¯m. Raman and Mössbauer data analyses reveal that the cation redistribution with Mn and Dy cosubstitution in CoFe2O4 and confirm the presence of a mixed spinel structure. Electron microscopy analysis indicates the significant effect of Mn, Dy cosubstitution on the microstructure of CoFe2O4. All the samples exhibit the magnetic hysteresis (M-H) loops at 5 K and 300 K. Saturation magnetization (Ms) and the cubic anisotropy constant (K1) values increase with Mn substitution, while with Dy substitution, Ms reduces due to the decrease of magnetic interactions with Dy substitution. However, the coercive field decreases with Mn and increases with Dy substitution. Higher values of magnetostriction coefficients (λ11 = −95 ppm and λ12 = 52 ppm) and the strain derivative (dλ/dH=0.075ppm/Oeat600Oe) observed make Co0.7Mn0.3Fe1.95Dy0.05O4 a suitable candidate for designing torque/stress sensors and a magnetostrictive phase for making magnetoelectric composite. Chemical composition optimization yields higher values Ms (89 emu/g, i.e., 3.73 μB) at lower coercivity (Hc = 241 Oe) for Co0.7Mn0.3Fe2O4 and higher values of λ11, λ12, and dλ/dH at a lower magnetic field (below 800 Oe) for Co0.7Mn0.3Fe1.95Dy0.05O4. The results suggest and demonstrate that Co0.7Mn0.3Fe2O4 and Co0.7Mn0.3Fe1.95Dy0.05O4 are the potential candidates for designing magnetomechanical sensor applications.
Investigation of terahertz radiation based on forward and backward wave feedback circuit
Authors: Gao Luanfeng , Hu Yulu , Zhu Xiaofang , Hu Quan , Li Jianqing , Li Bin
Published: November 2019

A forward and backward wave feedback circuit for a vacuum terahertz radiation source is proposed and demonstrated. In the feedback circuit, two bunched electron beams with different velocities interact with two spatial harmonics, respectively. The simulation reveals that the forward wave and the backward wave form a feedback circuit and affect each other through beam-wave interaction. Furthermore, the maximum gain of the forward wave of the feedback circuit is about 12.4 dB higher than the single beam traveling wave tube. When observing the excitation of the backward wave, it is found that the starting current is 44.4% lower than the traditional backward wave oscillator. The forward and backward wave feedback circuit is a promising choice for the watt-level tunable compact terahertz source that can be potentially used in a transmitter for Terahertz radar detection.
Non-radioactive electron source with nanosecond pulse modulation for atmospheric pressure chemical ionization
Authors: Bunert Erik , Berger Marc , Kirk Ansgar T. , Zimmermann Stefan
Published: November 2019

Ion mobility spectrometers (IMSs) are well-known instruments for fast and ultrasensitive trace gas detection. In recent years, we introduced a compact nonradioactive electron source providing a defined current of free electrons with high kinetic energy at atmospheric pressure for initiating a chemical gas phase ionization of the analytes identical to radioactive sources. Besides its nonradioactivity, one major advantage of this electron source is its controlled electron emission current even in pulsed mode. By optimizing the geometric parameters and developing faster control electronics, we now achieve electron pulses with extremely short pulse widths down to 23 ns. This allows us to kinetically control the formation of reactants and analyte ions by chemical gas phase ionization (e.g., reducing discrimination processes caused by competing ionization), enhancing the analytical performance of the IMS. However, this paper concentrates on the pulsed electron source. For its characterization, we developed a measurement setup, which allows the detection of nanosecond electron pulses with amplitudes of only a few nanoamperes. Furthermore, we investigated the spatial ion distribution in the ionization region depending on several operating parameters, such as the kinetic electron energy or the ionization time.
Time dependent adaptive configuration interaction applied to attosecond charge migration
Authors: Schriber Jeffrey B. , Evangelista Francesco A.
Published: November 2019

In this work, we present a time-dependent (TD) selected configuration interaction method based on our recently introduced adaptive configuration interaction (ACI). We show that ACI, in either its ground or excited state formalisms, is capable of building a compact basis for use in real-time propagation of wave functions for computing electron dynamics. TD-ACI uses an iteratively selected basis of determinants in real-time propagation capable of capturing strong correlation effects in both ground and excited states, all with an accuracy—and associated cost—tunable by the user. We apply TD-ACI to study attosecond-scale migration of charge following ionization in small molecules. We first compute attosecond charge dynamics in a benzene model to benchmark and understand the utility of TD-ACI with respect to an exact solution. Finally, we use TD-ACI to reproduce experimentally determined ultrafast charge migration dynamics in iodoacetylene. TD-ACI is shown to be a valuable benchmark theory for electron dynamics, and it represents an important step toward accurate and affordable TD multireference methods.
On the time-evolution of resonant triads in rotational capillary-gravity water waves
Authors: Ivanov Rossen I. , Martin Calin I.
Published: November 2019

We investigate an effect of the resonant interaction in the case of one-directional propagation of capillary-gravity surface waves arising as the free surface of a rotational water flow. Specifically, we assume constant vorticity in the body of the fluid which physically corresponds to an underlying current with a linear horizontal velocity profile. We consider the interaction of three distinct modes, and we obtain the dynamic equations for a resonant triad. Setting the constant vorticity equal to zero, we recover the well known integrable three-wave system.
The necessity modalshave to,must,need to, andshould
Authors: Cappelle Bert , Depraetere Ilse , Lesuisse Mégane
Published: November 2019

When an ambiguous lexical item appears within a familiar string of words, it can instantly receive an appropriate interpretation from this context, thus being saturated by it. Such a context may also short-circuit illocutionary and other pragmatic aspects of interpretation. We here extract from the British National Corpus over 500 internally highly collocating and high-frequency lexical n-grams up to 5 words containinghave to,must,need to, and/orshould. These contexts-as-constructions go some way toward allowing us to group these four necessity modals into clusters with similar semantic and pragmatic properties and to determine which of them is semantico-pragmatically most unlike the others. It appears thathave toandneed tocluster most closely together thanks to their shared environments (e.g.,you may have/need to…, expressing contingent, mitigated necessity), whileshouldhas the largest share of unique n-grams (e.g., rhetoricalWhy shouldn’t I…?, used as a defiant self-exhortation).
Coercion
Authors: Leclercq Benoît
Published: November 2019

The goal of this paper is to investigate the possibility of a cross-theoretical understanding ofcoercion, a “kind of contextual enrichment/adjustment” (Lauwers & Willems 2011: 1220), by combining insights from Construction Grammar and Relevance Theory. In Construction Grammar, coercion has mostly been discussed in terms of the semantics of the linguistic items that occur in the sentence and how these interact with each other. Relevance Theory, on the other hand, does not distinguish cases of coercion from other instances of lexical adjustment, and discusses them in terms of the pragmatic principles involved during utterance interpretation. In order to highlight the complementarity of the two perspectives, this paper particularly consists in pinning down their respective explanatory limits. It will be shown that coercion is better described in terms of a linguistically required pragmatic process. Therefore, it will be suggested that coercion might actually instantiate a particular type ofsaturation.
Non-exhaustive lists in spoken language
Authors: Mauri Caterina , Goria Eugenio , Fiorentini Ilaria
Published: November 2019

The aim of this paper is to analyze how speakers refer to non-exhaustive sets in spoken discourse, by means of open lists. We will propose an analysis of non-exhaustivity in terms of indexicality and we will therefore consider open lists as having an inherently pragmatic component. Based on corpus data of spoken Italian, we will identify three main types of non-exhaustive lists, showing different structural properties and non-compositional semantics. In order to account for the observed variation, we will take a construction grammatical perspective, arguing that what may appear as a heterogeneous set of strategies is instead an inheritance-based network of constructions sharing a schematic core (cf.Goldberg 1995). We will elaborate on the most recent approaches to list constructions, along the lines proposed byMasini, Mauri, & Pietrandrea (2018), and will identify three types of non-exhaustive list constructions, which inherit the core properties from the upper-level list construction, but at the same time show more specific features and constraints.
Mandatory use of Prescription Drug Monitoring Program and Benzodiazepine Prescribing among U.S. Medicaid Enrollees
Authors: Liang Di , Guo Huiying , Shi Yuyan
Published: November 2019

Background

In the past two decades, the U.S. saw an alarmingly increasing trend of benzodiazepine prescribing. Mandatory use of Prescription Drug Monitoring Programs (PDMPs) was suggested to have the potential to reduce opioid prescribing, but little is known about its impacts on benzodiazepines. This study examined whether PDMP data use mandates were associated with changes in benzodiazepine prescribing in the U.S.

Methods

Aggregate state quarterly prescription drug records of benzodiazepines for Medicaid enrollees during 2010–2017 were obtained from the U.S. Medicaid State Drug Utilization Data. Three population-adjusted outcome variables were evaluated, including quantity, dosage, and Medicaid spending of benzodiazepine prescriptions per quarter per 100 Medicaid enrollees. The primary policy variable was the state-wide implementation of PDMP data use mandates for benzodiazepines. To account for between-state variations in mandates, an additional policy variable was considered to indicate strong mandates on PDMP data use, which required all prescribers to query a patient's PDMP records for first prescribing and subsequent prescribing at least every 12 months. Linear regressions with difference-in-difference approach were used to assess the associations between PDMP data use mandates and benzodiazepine prescribing, controlling for state-level time-varying policy and socioeconomic covariates.

Results

The state-wide implementation of PDMP data use mandates for benzodiazepines was not associated with quantity, dosage, or Medicaid spending of benzodiazepine prescriptions. Strong mandates on PDMP data use were not associated with any benzodiazepine prescribing outcomes, either.

Conclusions

There was no evidence for the associations between PDMP data use mandates for benzodiazepines and changes in benzodiazepine prescribing among Medicaid enrollees. Future research is warranted to replicate the study in other populations using individual patient records and continuously monitor the trends in benzodiazepine prescribing in association with PDMPs.
Endothelial Cdk5 deficit leads to the development of spontaneous epilepsy through CXCL1/CXCR2-mediated reactive astrogliosis
Authors: Liu Xiu-xiu , Yang Lin , Shao Ling-xiao , He Yang , Wu Gang , Bao Yu-huan , Lu Nan-nan , Gong Dong-mei , Lu Ya-ping , Cui Tian-tian , Sun Ning-he , Chen Dan-yang , Shi Wei-xing , Fukunaga Kohji , Chen Hong-shan , Chen Zhong , Han Feng , Lu Ying-mei
Published: November 2019

Blood–brain barrier (BBB) dysfunction has been suggested to play an important role in epilepsy. However, the mechanism mediating the transition from cerebrovascular damage to epilepsy remains unknown. Here, we report that endothelial cyclin-dependent kinase 5 (CDK5) is a central regulator of neuronal excitability. Endothelial-specific Cdk5 knockout led to spontaneous seizures in mice. Knockout mice showed increased endothelial chemokine (C-X-C motif) ligand 1 (Cxcl1) expression, decreased astrocytic glutamate reuptake through the glutamate transporter 1 (GLT1), and increased glutamate synaptic function. Ceftriaxone restored astrocytic GLT1 function and inhibited seizures in endothelial Cdk5-deficient mice, and these effects were also reversed after silencing Cxcl1 in endothelial cells and its receptor chemokine (C-X-C motif) receptor 2 (Cxcr2) in astrocytes, respectively, in the CA1 by AAV transfection. These results reveal a previously unknown link between cerebrovascular factors and epileptogenesis and provide a rationale for targeting endothelial signaling as a potential treatment for epilepsy.
ILC2s are the predominant source of intestinal ILC-derived IL-10
Authors: Bando Jennifer K. , Gilfillan Susan , Di Luccia Blanda , Fachi José L. , Sécca Cristiane , Cella Marina , Colonna Marco
Published: November 2019

Although innate lymphoid cells (ILCs) functionally analogous to T helper type 1 (Th1), Th2, and Th17 cells are well characterized, an ILC subset strictly equivalent to IL-10–secreting regulatory T cells has only recently been proposed. Here, we report the absence of an intestinal regulatory ILC population distinct from group 1 ILCs (ILC1s), ILC2s, and ILC3s in (1) mice bred in our animal facility; (2) mice from The Jackson Laboratory, Taconic Biosciences, and Charles River Laboratories; and (3) mice subjected to intestinal inflammation. Instead, a low percentage of intestinal ILC2s produced IL-10 at steady state. A screen for putative IL-10 elicitors revealed that IL-2, IL-4, IL-27, IL-10, and neuromedin U (NMU) increased IL-10 production in activated intestinal ILC2s, while TL1A suppressed IL-10 production. Secreted IL-10 further induced IL-10 production in ILC2s through a positive feedback loop. In summary, ILC2s provide an inducible source of IL-10 in the gastrointestinal tract, whereas ILCregs are not a generalizable immune cell population in mice.
Lattice arrangement of myosin filaments correlates with fiber type in rat skeletal muscle
Authors: Ma Weikang , Lee Kyoung Hwan , Yang Shixin , Irving Thomas C. , Craig Roger
Published: November 2019

The thick (myosin-containing) filaments of vertebrate skeletal muscle are arranged in a hexagonal lattice, interleaved with an array of thin (actin-containing) filaments with which they interact to produce contraction. X-ray diffraction and EM have shown that there are two types of thick filament lattice. In the simple lattice, all filaments have the same orientation about their long axis, while in the superlattice, nearest neighbors have rotations differing by 0° or 60°. Tetrapods (amphibians, reptiles, birds, and mammals) typically have only a superlattice, while the simple lattice is confined to fish. We have performed x-ray diffraction and electron microscopy of the soleus (SOL) and extensor digitorum longus (EDL) muscles of the rat and found that while the EDL has a superlattice as expected, the SOL has a simple lattice. The EDL and SOL of the rat are unusual in being essentially pure fast and slow muscles, respectively. The mixed fiber content of most tetrapod muscles and/or lattice disorder may explain why the simple lattice has not been apparent in these vertebrates before. This is supported by only weak simple lattice diffraction in the x-ray pattern of mouse SOL, which has a greater mix of fiber types than rat SOL. We conclude that the simple lattice might be common in tetrapods. The correlation between fiber type and filament lattice arrangement suggests that the lattice arrangement may contribute to the functional properties of a muscle.
In situ monitoring of artificial aging and solution heat treatment of a commercial Al–Mg–Si alloy with a high intensity positron beam
Authors: Resch L , Gigl T , Klinser G , Hugenschmidt C , Sprengel W , Würschum R
Published: November 2019

Microstructural changes of a commercial Al–Mg–Si alloy were studied during artificial aging by in situ Doppler broadening spectroscopy using a high-intensity positron beam. The in situ positron annihilation characteristics at high temperatures differ considerably from the conventionally applied ex situ measurements at low temperatures. Therefore, a more comprehensive view of precipitation processes in Al–Mg–Si alloys is obtained. Further, in situ positron–electron annihilation techniques allow for an investigation of aging processes with increased sensitivity. For the artificial aging temperatures of 180 °C and 210 °C pronounced variations of the Doppler broadening S-parameter reveal (i) the evolution of clusters into larger precipitates and (ii) the time of the formation of

” precipitates and the role of vacancies in connection to this. Towards higher aging times, the transformation from coherent

” to semi-coherent

’ precipitates could be verified. Additional insights are gained by in situ measurements of the S-parameter during the solution heat treatment of the previously overaged sample. Here, the S-parameter reveals both the dissolution of precipitates starting from temperatures of 364 °C and the thermal generation of vacancies.
A Trust Assessment Model Based on Recommendation and Dynamic Self-adaptive in Cloud Service
Authors: Lu Yiqin , Fang Yahui , Qin Jiancheng
Published: November 2019

Cloud computing is a completely distributed sharing platform, through which users can request personalized cloud services. With the continually expansion of cloud services scale, more and more service providers are offering services with similar functions but different qualities. Users are faced with the problem of selecting the most suitable cloud services from numerous cloud services. In this paper, a trust assessment model based on recommendation and dynamic self-adaptive in cloud service is proposed. In the trust assessment model, trust is divided into direct trust and indirect trust, which can make the evaluation of trust value more accurate. In addition, a recommendation mechanism is proposed in this paper, the third-party nodes make recommendations according to the actual situation, and accept reward or punishment automatically after interacting. Experiment shows that the model can accurately evaluate the trust between nodes, and effectively resist the attack of malicious nodes.
Research on Zero-Sequence Insulator Detection Technology Based on Deep Learning
Authors: Tao Guo , Lianggang Xu , Hongyun Shi , Fengxiang Chen , Shichun Wang , Xiaowei Liu
Published: November 2019

With the expansion of power grid scale, the method of manual inspection is more and more difficult to implement. Nowadays, it is possible to use infrared imaging equipment and target detection method based on deep learning technology to intelligently detect zero-sequence insulator. In this paper, zero-sequence insulator detection technology based on in-depth learning is proposed to detect zero-sequence insulators with different contamination, air humidity and different locations. This technology has the characteristics of low investment cost, high accuracy and strong adaptability in complex environment, which can reduce the labor intensity and workload of power grid patrol personnel. The high accuracy of the system can effectively reduce the outage accidents caused by the deterioration of insulators in the power grid, thus ensuring the safe and stable operation of the power grid.
An Improved Algorithm of UKF-SLAM Based on RBF Neural Network Adaptive Robot
Authors: Lee X Y , Gao G W
Published: November 2019

For Extend Kalman Filtering (EKF) algorithm in the robot simultaneous localization and mapping (SLAM), the linearization of the nonlinear system leads to the error of the system state equation, the large amount of computation caused by the Jacob matrix calculation, and the noise model uncertainty. Filtering instability and other issues. A modified UKF-SLAM algorithm based on RBF network adaptive is proposed. In the absence of prior noise information, the algorithm identifies the process noise and observation noise through RBF network adaptive identification unit, and iteratively corrects the noise covariance and the mean filter new covariance, so that the real-time positioning accuracy of the robot is improved. The experimental results show that this method has higher positioning precision and adaptive ability compared with the EKF algorithm and UKF algorithm.
Research on Training System Optimization of College Teachers Based on Internet+ and Big Data
Authors: Wang Lei
Published: November 2019

With the advent of the era of Internet+ and cloud computing, more and more mature key technologies of big data, including date acquisition, data processing, data storage and visualization analysis, have provided new thoughts and opportunities for the reform of college teachers training system. In this context, this study analyzed the shortcomings of the traditional training mode and conducted SWOT analysis on application of big data technologies in training of teachers. Under the guidance of the idea of Internet + and big data, this study optimized the training steps including training preparation, training practice, training evaluation as well as training feedback. It also proposed some important issues worth attention from the training departments in universities – to enrich the repository of online open courses, to ensure security of the training data, to strengthen cooperation with the big data management department on campus and to improve the training management system.
Dynamic load identification of stochastic structures based on unscented transformation
Authors: Wang Chongwen , Du Chengbin
Published: November 2019

In view of some unknown parameters in structures, which are described by the Gaussian distribution model in this paper, a new dynamic load identification method for stochastic structures is proposed based on unscented transformation (UT). Firstly, the convolution equation for solving structural response is discretized, and the sampling points, namely, sigma points of stochastic parameters are calculated according to the unscented transformation. Then, when the stochastic parameters take each sigma point, the dynamic load is calculated by the direct inversion method combined with the regularization method. Finally, the mean value and standard deviation of the identified load are obtained, and the coefficient of variation and the upper and low bounds of the identified load are defined. The result of an example shows that compared with the Monte Carlo simulation (MCS) and perturbation method (PM), the proposed method has higher computational efficiency and accuracy.
Visualization of Casing Stress Characteristics under Non-Uniform In-situ Stress and Non-uniform Cement Sheath
Authors: Tao Chen , Wei Zhu , Feng Chen , Qinfeng Di , Guangxu Qin , Qing Li
Published: November 2019

Casing damage is a common problem in oil and gas fields due to the complicated stress state of casing. Especially in the horizontal well section, the casing is difficult to be centered, and the eccentric casing is prone to failure under non-uniform in-situ stress. In order to study the influence of non-uniform in-situ stress and non-uniform cement sheath on the stress state of casing, a numerical model of the coupling of casing, cement sheath and formation is established. The stress distribution characteristics of casing under four conditions of uniform in-situ stress uniform cement sheath, uniform in-situ stress non-uniform cement sheath, non-uniform in-situ stress uniform cement sheath and non-uniform in-situ stress non-uniform cement sheath are compared. The results show that the non-uniform cement sheath will cause the stress distribution of casing to be uneven, and the maximum stress on the casing is proportional to the amount of eccentricity. The non-uniform in-situ stress will cause the casing stress to be uneven and affect the extreme value of the stress on the casing inner wall. The results will provide the theoretical basis for the casing damage prediction effectively.
An Analysis of tourist satisfaction of Scenic spots in Pingliang based on Fuzzy Comprehensive Evaluation method
Authors: Guo Weiwei , Li Chunfang
Published: November 2019

There are some basic steps of the fuzzy comprehensive evaluation method of constructing the satisfaction index system, establishing the weight set, determining the evaluation set, establishing the evaluation matrix,the paper comprehensively evaluates the tourists satisfaction in Pingliang scenic spots.
The Application of Support Vector Machine with Elastic Net Regularizationin in Classification of Wheat Seeds
Authors: Jiaoyang Zhao , Xingshi He
Published: November 2019

Machine Learning is playing a big part in data analysis. Aiming at the problem of wheat seed classification, Seed data from UCI Machine Learning Repository is used as experimental data in this paper, firstly Support Vector Machine (SVM) algorithm is used to classify and predict the test set samples of wheat seed data sets. Secondly, Support Vector Machine (SVM) algorithm with Lasso and elastic net regularization is used to classify and predict wheat seed. The experimental results show that the SVM-Elastic Net model has high classification accuracy.
Chinese Reviews Generation Based on HM-BiLSTM Model
Authors: Yuan Jianglin , Guo Zhigang , Chen Gang , Sun Yihe , Yang RuiPeng
Published: November 2019

HM-BiLSTM model is proposed to solve the problem of inaccurate expression of Chinese comment with specific topic, which is generated by existing deep learning models. Firstly, HM-BiLSTM model was created to generate comments with subject attention mechanism algorithm whose purpose was to assist to constrain the theme. Then, imitation writing sentence was retrieved to extract syntactic structure information from Chinese reviews corpus. The subject as well as structure information was both encoded. Lastly, Chinese comments were generated recurrently by HM-BiLSTM model under the constraints of the subject attention mechanism algorithm and syntactic structure information. The experiment results showed that the model generated comments with better quality and accurate theme.
A Mega-Trend-Diffusion and Monte Carlo based virtual sample generation method for small sample size problem
Authors: Yu Xiaoru , He Yanlin , Xu Yuan , Zhu Qunxiong
Published: November 2019

Data-driven modeling has attracted wide attention in academia because of its effectiveness. However, Due to the lack of data, some traditional modeling methods, such as extreme learning machine (ELM), can’t achieve high learning accuracy. A novel approach based on Mega-Trend-Diffusion (MTD) and Monte Carlo is presented in this paper to deal with the problem, named Monte Carlo Mega-Trend-Diffusion (MCMTD). The proposed approach utilizes MTD to estimate the acceptable range of the attributions and Latin hypercube sampling method to sample. ELM is employed to establish the prediction model. In this paper, two real data sets, the multi-layer ceramic capacitors (MLCC) and the purified terephthalic acid (PTA), are used to verify the effectiveness and reasonability of MCMTD. The experimental results show that MCMTD can significantly enhance the accuracy and ability of the forecasting model.
Dynamic regularization discriminant local preserving projection method for fault diagnosis
Authors: He Yanlin , Yan Xiaona , Zhu Qunxiong , Xu Yuan
Published: November 2019

Due to the high dimensionality, serial correlation, and nonlinearity of industrial process data, the primary task for diagnosing fault is to extract key fault features from fault datasets. In this paper, to obtain much more inherent fault information, a dynamic regularization discriminant local preserving projection approach (DRDLPP) based on feature reduction is put forward to diagnose fault, which addresses the small sample size problem of discriminant local preserving projection (DLPP) by incorporating the regularization term into the objective function of DLPP. The enhanced performance of DRDLPP for fault diagnosis over conventional diagnostic approaches mostly benefits from two aspects: One aspect is that DRDLPP can discover local manifold fault information hidden in original sample space by preserving the local neigborhood structure of data; The other aspect is that DRDLPP has the remarkable capacity to capture dynamic information by extending the observation vector with previous observation vectors. What is more, the information criterion function is utilized to capture the optimal dimensionality reduction order and time lag of DRDLPP method. The experimental results of the Tennessee Eastman process demonstrate that the proposed DRDLPP approach provides a better visual performance and achieve lower misclassification rates in fault diagnosis.
Excavating effective information in different stage of backbone to improve semantic segmentation results
Authors: Han Huihui , Fan Lei
Published: November 2019

To overcome the two difficulties in semantic segmentation task:1) the presence of multi-scale objects, 2) the loss of spatial resolution, a new semantic segmentation model is designed in this paper, which can explore effective information from different stage of backbone. Extensive experiments conducted on two public benchmark datasets have proved the effectiveness of each module, and the new model achieves 73.62% mIoU on Cityscapes test set and 79.88% mIoU on PASCAL VOC 2012 test set.
Research on Extraction of Address from Criminal Case Records via Lattice LSTM
Authors: Ding Xiangwu , Yang Ziqi
Published: November 2019

As an important carrier for recording the details of case in the field of criminal investigation, case records contain address of the crime scene which is crucial to the case analysis. This paper uses Lattice LSTM to extract address from case records which combines the advantages of the character-based and word-based models. It can exploit word and word sequence information without performing Chinese word segmentation. For the purpose of optimizing the model, this paper proposes a word vector template which is generated by address repository of Shanghai to pretrain more accurate and more controllable word embeddings. The experiment shows that the model achieves the performance with the precision rate of 96.49%, recall rate of 94.83% and F1 score of 95.65%.
Air Traffic Controller’s Behavior Recognition from Still Images by Transfer Learning
Authors: Xiang Zheng , He Deyang , Deng Weinan
Published: November 2019

The fatigue problem of air traffic controllers is one of the important reasons that en-danger the safety of civil aviation. In this paper, we introduce behavior recognition into the field of fatigue monitoring of civil air traffic controllers. We investigate the method of automatic recognition of controller’s behaviors by using transfer learning and model fusion. The task of behavior recognition for controllers can be regarded as a multi-classification problem (Such as putting heads on desks for a nap, lay with face upward, nod to sleep, talking with others, directing planes normally, Hold up chin on left hand, writing notes on strips radio check, Resting hand on forehead ). Our study solve this problem from three aspects: (1) Set up a data set of controller behavior, including 18247 pictures of 10 actions; (2) Employ two-stream CNN(Resnet50,Inception V3) to extract different features; (3) We investigate different fusion strategy to improve the accuracy of action recognition of controllers. Experiments demonstrate that our method achieves a recognition accuracy of 94.2% and 93.7% on the CAFUC-Controller and Kaggle-Driving datasets.
Privacy Protection for Recommendation System: A Survey
Authors: Huang Weiming , Liu Baisong , Tang Hao
Published: November 2019

Recommendation system have become one of the most well-liked and accepted way to solve overload of information or merchandise. By collecting user’s personal data for processing, suitable lists of information or merchandise are provided to the potential consumers. For online business, recommendation systems have become an extremely effective revenue driver and developed rapidly. Although recommendation systems are great beneficial, directly exposing privacy data to the recommender may lead to leakage of privacy and cause risks. Therefore, quality of recommendation and privacy protection are both important metrics in recommendation. In this paper we present a review investigating development in recommendation systems with privacy protection, including the definition of privacy, classification of privacy leakage, taxonomy of privacy, measuring of privacy risk, policies for privacy protection, approaches of privacy protection and models of privacy protection. We also speculate on the future direction.
Decoding of Visual Information Based on EEG Data
Authors: Zhou Qing , Liu Yadong , Zhong Saisai , Tang Jingsheng , Liu Yaru
Published: November 2019

We perhaps fantasized about reading the mind of the human brain directly and use it in other ways. In this paper, we tried to analyze human brain activity to identify the feature space employed by humans for visual classification. The brain signals driven by visual information were classified, and the visual information classifier were trained, and the visual information were decoded by the brain signals. The 64 channels EEG data induced by 40 types of images were collected, and recurrent neural network(RNN) was used for extracting features of electroencephalogram(EEG) signals and predicting the types of EEG signals. The accuracy of decoding could reach 80.90%. The technology of decoding of visual information based on brain EEG data could be applied to the brain-computer interface(BCI).
Deep Fusion and Ensemble Neural Networks for Point Sets Data
Authors: Kang Yi , Na Zhang , Yuting Zhao , Yiyan Lei
Published: November 2019

The typical character of point cloud is that its format is irregular, so most researchers transform such data into regular format. However, this conversion may induce some noise. In this paper, we design the dedicated neural network unit for point sets data processing. Firstly, to guarantee the permutation invariance of point sets, we infer the neural unit design principles. Secondly, we propose the deep fusion and ensemble neural networks for point sets data, in which we can not only combine different scales of feature representation, but also input the point data into the network directly. Finally, we evaluate the proposed networks on object classification and object part segmentation, which can verify the efficiency of our network.
Intelligent Development of Building Fire Monitoring System
Authors: QiuBo
Published: November 2019

This paper analyzes the characteristics of the intelligent development of building fire monitoring system, combined with the advantages of intelligent development of building fire monitoring system. Through the research of intelligent monitoring equipment, intelligent monitoring process, intelligent fire detection, intelligent fire alarm, the development strategy aims to raise people’s awareness of intelligent monitoring systems and improve the safety level of residents’ lives.
Fuzzy Control for Off-center Steering System of a Novel Electric Chassis Based on PWM Technology
Authors: Qu Jiwei , Li Yining , Song Shujie , Guo Kangquan
Published: November 2019

A flexible chassis (FC) for an electric vehicle with four in-wheel drive motors is described in this paper. It has a unique structure with an off-center steering shaft. The driving system and steering system are combined, which has considerable advantages in terms of actuation flexibility for the confined spaces. However, there is no rigid constraint for steering, and it is difficult to coordinate the motions of electromagnetic steering lock (ESL) and the wheel. To solve this problem, a pulse-width modulation (PWM) technology is introduced to control the ESL. With the control of PWM signal, the chassis can achieve stepwise driving and steering. Then, fuzzy logic is introduced to control PWM signal of the ESL so that it can match the steering motion of the wheels. To validate the system, steering signal step input tests and sinusoidal input tests were conducted on a self-made test bench. The results demonstrate that, the off-center steering system is with good steering performance, and the linkage and tracking errors of the left and right wheel are all in an acceptable range. The proposed control strategies are feasible and effective, and can ensure smooth turning for the chassis.
A novel hierarchical clustering approach based on data gravitation model
Authors: Zhang Peng , Xiang Xiwang , She Kun
Published: November 2019

The purpose of clustering is to group the data points into several clusters according to their similarity. Inspired by law of gravitation, each data point considered as an object is exposed in data space and influenced by the data gravitation of the other data points. In this paper, a novel hierarchical clustering approach (NGHC) is proposed based on local data gravitation. Firstly, the dataset is partitioned into some clusters as intermediate result by a simple gravity-based clustering approach. Then the intermediate clusters are merged by a new linkage measure which combines the data gravitations between two clusters until the satisfactory result is obtained. Two real-world datasets are used to validate the clustering performance of NGHC compared with five representative clustering algorithms. The results exhibit that the NGHC algorithm obtains the best performance on low- and high-dimensional datasets.
Multi-Variant Execution Research of Software Diversity
Authors: Liu Zhenwu , Zhang Zheng , Zhang Jiexin , Liu Hao
Published: November 2019

As more and more software products are threatened by malicious reverse analysis, along with software products are pirated, tampered with and so on, it is of great significance to study software security protection technology in depth. As a software security protection technology, software diversification introduces uncertainty into the target program and provides probabilistic protection for the target program. Multi-Variant Execution (MVE) are fine-grained implementation of software diversification that produces functionally identical variants at the system call level. This article first introduces the related concepts of multi-variant execution. Secondly, it expounds the key technologies of multi-variant execution implementation-variant generation, variant monitor, input/output and synchronization, monitor-variant communication. Security of monitors and different variant communication technologies are analyzed and compared, their advantages and defects are pointed out respectively. Several implementation methods of multi-variant execution design are summarized. Finally, the reasons for the false negatives and false positives of attack events are analyzed, and the prospects and challenges of using multi-variant execution techniques to implement security systems are summarized.
An improved algorithm for radar adaptive beamforming based on machine learning
Authors: Bai Moyu , Liu Hao , Chen Haochuan , Gu Shengming , Zhang Zhenhua
Published: November 2019

In the field of radar digital signal processing, adaptive beamforming is a widely used technique for suppressing interference and noise. The Least Mean Square Algorithm (LMS) is a simple and easy algorithm for adaptive digital beamforming. However, it has the disadvantage of not achieving a balance between convergence speed and stability. In order to improve the performance of adaptive beamforming, this paper firstly reviews the classical LMS algorithm and then the machine learning optimization algorithm. Improvement effects of the three machine learning methods on the LMS algorithm are analyzed. The results show that the improved LMS algorithm based on AdaGrad exhibits the best performance. The algorithm can independently adjust the adaptive learning rate of different parameter components, making the iterative process of the adaptive beamforming more stable, efficient, and suitable for both theoretical research and engineering practice.
A Precautionary Loss-based Bayesian Algorithm for Zero Failure Analysis of Exponential Model
Authors: Zhang Fan , Liu Yongjing , Luo Laijun
Published: November 2019

The aim of this paper is to study the Bayesian reliability analysis of the exponential distribution under a precautionary loss function based on zero failure data. In the Bayesian algorithm, the prior distribution of the parameter is assumed to be Gamma distribution. Bayesian and E-Bayesian estimators of the failure rate and reliability of exponential model are obtained under the precautionary loss function. Finally, a practical example is utilized to validate the effectiveness and robustness of the proposed estimators.
The application of the improved Talbot’s inverse Laplace transformation method in solving the flow problem of porous materials
Authors: Wang Fang , Han Xueli
Published: November 2019

In this paper, we extend the fixed Talbot’s method to the complex-valued function in order to get the general solutions for the Biot’s consolidation in the physical domain. We derive a solution for the unsteady flow field of layered porous media with anisotropic permeability under a point fluid source. By a Laplace and two-dimensional(2D) Fourier transform, the continuity equation of the fluid can be solved, and the flow field can be expressed in an analytical form in the transformed domain. Using the boundary and interface condition, the flow field for general layered porous media can be solved in the transform domain. The actual solutions in the physical domain can be obtained by inverting the Laplace-2D Fourier transform. Numerical examples are given to demonstrate the validity of the extended fixed Talbot’s inverse Laplace transformation method, and its application in solving dymanic problems of porous materials.
A Review of the Development and Applications of Number Theory
Authors: Yan Keyu
Published: November 2019

This paper mainly studies the development and applications of number theory, aiming to review the history of this discipline, and explore its influence on production and our life and its applications. Number theory is devoted originally to the study of the integers. With the contributions made by mathematicians in different ages to advancing the study of the integers, the basic system of number theory has been gradually improved, and thereby a complete and unified discipline has been formed. As a basic discipline, number theory spreads profound impacts on other disciplines, and is the foundation of many disciplines.
A (n +1)-dimensional KP Equation with Variable Coefficients with Self-consistent Source
Authors: Wen Danhua , chen Li , Zhang Xiaofei
Published: November 2019

In this paper, the variable coefficient (n+1)-dimensional KP equation is studied with the Hirota bilinear method. Firstly, the logarithmic transformation is used to transform the nonlinear variable coefficient (n+1)-dimensional KP equation into a bilinear form, and then variable coefficient (n+1)-dimensional KP equation with self-consistent source was constructed with the source generation procedure.
Noise calculation of noncompact bodies with impedance boundary in limited enclosed space
Authors: Wang Fang , Zheng Wensi
Published: November 2019

The present paper mainly investigates noise propagation in limited enclosed space with non-compact bodies. In view of the scattering effect induced by solid boundaries, tailored green’s function methods are used to study the noise propagation. Frequency-domain integral equation is presented to calculate scattered noise under impedance boundary condition in enclosed space. At first, the scattered noise of circular cylinder with solid boundary condition is calculated to verify the validity of the present algorithm. Secondly, the impedance boundary condition is executed on the cylinder wall. Furthermore, the impedance boundary conditions are carried out on the boundaries of enclosed space. Numerical results demonstrate that the total noise drastically reduces in some areas with impedance boundary, but increases in other areas. The phenomenon is related with the cylinder radius, the location of sound sources and the scattering effect. In practical engineering problems, the impedance boundary should be applied locally according to the configuration.
A General Analysis Method for Data Link Log Files
Authors: Xu Zhengqin , Chen Juan , Peng Laixian , Zhao Wendong , Chu Xiaojing
Published: November 2019

Aiming at the problem that the data analysis software of data link experiment has strong dependence on the log data format and the analysis function is difficult to expand dynamically, this paper puts forward the design idea of the log file general parsing software and the general description language of the log file of the bit-oriented data link system, and designs and implements the log file data analysis system of the general data link system.The file format description language is used to separate the analysis software from the specific data format. The log file data of various formats can be imported into Excel software on demand. By using the powerful data analysis function of Excel software, the business departments can achieve targeted data analysis.
The Optimization of Food Distribution Path of Cold Chain in the Beijing-Zhangjiakou Olympic Winter Game
Authors: Yu Fei
Published: November 2019

In this paper, we take the winter Olympic Games held in Beijing in 2022 as the research background, mainly in the Olympic Games held during the cold chain food distribution path scheduling optimization problem. The Beijing Olympic Games in 2022 is the first time in the history of China to host the Winter Olympics, and in Beijing, Zhangjiakou two cities together, Beijing will soon become the history of the Olympic Games held the first summer Olympic Games and the Winter Olympic Games city. Therefore, during the Olympic Games held its research on the food supply problems of athletes, but also the issue of cold chain food distribution has become the focus of the study.

Based on the model of vehicle routing problem with time window, we construct a cold chain food distribution model with the aim of minimizing the total cost of distribution. At the same time, in order to respond to the call of green Olympics and green supply chain, we consider energy saving and emission reduction. In the cost calculation, the environmental cost of carbon emission is added. Secondly, the routing of the distribution is optimized by the saving algorithm, and the distribution route is improved by the actual example given. The detailed distribution scheme is given Finally, according to the optimization results, some suggestions are put forward for the cold chain food distribution in the winter Olympic Games.