bluuebunny/crossref_metadata_2025 huggingface dataset:
New insight on accretion shocks onto young stellar objects
Authors: de Sá L. , Chièze J.-P. , Stehlé C. , Hubeny I. , Lanz T. , Cayatte V.
Published: September 2019

Context.Material accreted onto classical T Tauri stars is expected to form a hot quasi-periodic plasma structure that radiates in X-rays. Simulations of this phenomenon only partly match observations. They all rely on a static model for the chromosphere and on the assumption that radiation and matter are decoupled.

Aims.We explore the effects of a shock-heated chromosphere and of the coupling between radiation and hydrodynamics on the structure and dynamics of the accretion flow.

Methods.We simulated accretion columns that fall onto a stellar chromosphere using the 1D ALE code AstroLabE. This code solves the hydrodynamics equations along with the first two moment equations for radiation transfer, with the help of a dedicated opacity table for the coupling between matter and radiation. We derive the total electron and ion densities from collisional-radiative model.

Results.The chromospheric acoustic heating affects the duration of the cycle and the structure of the heated slab. In addition, the coupling between radiation and hydrodynamics leads to a heating of the accretion flow and of the chromosphere: the whole column is pushed up by the inflating chromosphere over several times the steady chromosphere thickness. These last two conclusions are in agreement with the computed monochromatic intensity. Acoustic heating and radiation coupling affect the amplitude and temporal variations of the net X-ray luminosity, which varies between 30 and 94% of the incoming mechanical energy flux, depending on which model is considered.
A test for block circular symmetric covariance structure with divergent dimension
Authors: Xie Junshan , Sun Gaoming
Published: September 2019

The paper considers the likelihood ratio (LR) test on the block circular symmetric covariance structure of a multivariate Gaussian population with divergent dimension. When the sample sizen, the dimension of each blockpand the number of blocksusatisfypu<n− 1 andp=p(n) →∞asn→∞, the asymptotic distribution and the moderate deviation principle of the logarithmic LR test statistic under the null hypothesis are established. Some numerical simulations indicate that the proposed LR test method performs well in the divergent-dimensional block circular symmetric covariance structure test.
Narzissmus und Übertragungsfokussierte Psychotherapie (TFP)
Authors: Lohmer Mathias , Wernz Corinna
Published: September 2019

Die Übertragungsfokussierte Psychotherapie (TFP) 1 wurde als ein Verfahren zur Behandlung von schweren
Persönlichkeitsstörungen entwickelt. Der Artikel stellt diesen speziellen Ansatz nun für die Behandlung von narzisstischen Patienten vor.
Das schematherapeutische Vorgehen bei narzisstischen Patienten
Authors: Dieckmann Eva , Roediger Eckhard
Published: September 2019

In der Therapie mit interaktionell anspruchsvollen Patienten erweist sich die Schematherapie als geeignete Methode. Herablassendes oder verletzendes Verhalten
von narzisstischen Patienten wird als Bewältigungsversuch von Verletzungen in der Kindheit verstanden. Ziel ist es, Bedürfnisse zu identifizieren und in der
Therapiesituation zu befriedigen. Patienten reagieren sehr positiv auf dieses empathisch-konfrontierende Vorgehen.
Narzissmus
Authors: Senger Katharina
Published: September 2019

Definitionen, Fakten, Relevantes – die PiD-Essentials fassen die wichtigsten Informationen zum Thema des Heftes knapp und übersichtlich für Sie
zusammen.
Konservative Therapie der Arthrose unter Berücksichtigung der Physikalischen Therapie
Authors: Bork Hartmut , Gottfried Thomas
Published: September 2019

Die konservative Therapie der Arthrose gliedert sich in nicht medikamentöse und medikamentöse Behandlungsmaßnahmen. Neben Information, Beratung sowie Edukation fallen unter die nichtmedikamentösen Therapieoptionen diverse physikalische Therapieverfahren wie Bewegungstherapie, Krankengymnastik, Medizinische Trainings- und Sporttherapie sowie Aquatisches Training. Klinisch relevante Effekte wie Schmerzreduktion und Funktionsverbesserung konnten bei günstigem Nutzen-Risiko-Profil und Kosteneffektivität in den vergangenen Jahren in mehreren Studien u. a. für die Behandlung der Hüft- und Kniearthrose nachgewiesen werden. Im nachfolgenden Beitrag werden die wichtigsten konservativen Behandlungsmöglichkeiten und ihre Wertigkeit aufgezeigt.
Trends Associated with Stillbirth in a Maternity Hospital School in the North Zone of São Paulo: A Cross-Sectional Study
Authors: Fonseca Raissa Magalhães de Mendonça , Garcia Carolina Laila , Angimahtz Talita Sampaio , Battaglia Cindy Fazio , Chalem Elisa , Sass Nelson
Published: September 2019

Objective To evaluate conditions associated with stillbirth (SB), and possible trends related with it, in a maternity hospital school in the North zone of São Paulo.

Methods An observational, cross-sectional study conducted at the Hospital Maternidade-escola de Vila Nova Cachoeirinha with 1,139 SBs in the period of 2003 to 2017. Cases of intermediate SB (ISB) (weight between 500 and 999 g) and late SB (LSB) (weight ≥ 1,000 g) were compared. We evaluated clinical data, laboratory tests, and fetal and placental studies. Data were stored in Windows Excel (Microsoft Corp., Redmond, WA, USA) worksheets, according to which graphs and tables were constructed. We used the statistical software SPSS for Windows version 18.0 (SPSS In., Chicago, IL, USA), estimating the prevalence ratio (PR) and odds ratio (OR), considering the 95% confidence interval (95% CI).

Results The general SB rate was 11.9%, and the in-hospital SB rate was 2.8%. Pregnant women younger than 16 years of age were more likely to have ISB (OR 0.32, 0.15–0.76), while patients older than 40 years old had a higher chance of LSB (PR 0.85, 0.72–0.99). A total of 25.7% of the general population did not have prenatal care, and 77.1% of the cases presented fetal death at admission. The cases of ISB had a statistically significant association with home birth (OR 0.61, 0.46–0.80). Cesarean section was performed in 16.1% of the subjects, and misoprostol was the most used method for induction. Necropsy and placental study of the fetuses were performed, respectively, in 94.2% and 97.3% of the cases. Associated causes were not identified in 22.1% of the cases, and the main causes identified were amniotic sac infections (27.9%), fetal malformations (12.5%), placental abruption (11.2%), hypertensive syndromes (8.5%), and maternal syphilis (3.9%), the latter with an increasing trend.

Conclusion Among the factors associated to SB were: hypertensive syndromes, amniotic sac infections, fetal malformations, placental abruption and syphilis. There was a growing trend in the number of cases of syphilis, which translates an alert. Diagnostic limitations justify indeterminate causes.
Mixed Phenotype of Langer–Giedion's and Cornelia de Lange's Syndromes in an 8q23.3-q24.1 Microdeletion without TRPS1 Deletion
Authors: Herrero-García Ana , Marín-Reina Purificación , Cabezuelo-Huerta Gloria , Ferrer-Lorente M. Belén , Rosello Mónica , Orellana Carmen , Martínez Francisco , Pérez-Aytés Antonio
Published: September 2019

Langer–Giedion's syndrome (LGS) or trichorhinophalangeal syndrome type II (TRPS II; MIM:150230) is a contiguous gene deletion syndrome caused by the haploinsufficiency of the TRPS1 and EXT1 genes. Cornelia de Lange's syndrome (CdLS) is a genetically heterogeneous dysmorphic syndrome where heterozygous mutations of RAD21 gene have been associated with a mild clinical presentation (CDLS type 4; MIM: 614701). We report a female patient with a 2.3-Mb interstitial deletion at 8q23.3-q24.1 encompassing EXT1 and RAD21 genes but not TRPS1. Clinical findings in this patient are correlated with a mixed phenotype of LGS and CdLS type 4.
Decoding signatures of structure, bulk thermodynamics, and solvation in three-body angle distributions of rigid water models
Authors: Monroe Jacob I. , Shell M. Scott
Published: September 2019

A tetrahedral structure resulting from hydrogen bonding is a hallmark of liquid water and plays a significant role in determining its unique thermophysical properties. This water feature has helped understand anomalous properties and physically interpret and model hydrophobic solvation thermodynamics. Tetrahedrality is well described by the geometric relationship of any central water molecule with two of its nearest neighbors in the first coordination shell, as defined by the corresponding “three-body” angle. While order parameters and even full water models have been developed using specific or average features of the three-body angle distribution, here we examine the distribution holistically, tracking its response to changes in temperature, density, and the presence of model solutes. Surprisingly, we find that the three-body distribution responds by varying primarily along a single degree of freedom, suggesting a remarkably simplified view of water structure. We characterize three-body angle distributions across temperature and density space and identify principal components of the variations with state conditions. We show that these principal components embed physical significance and trace out transitions between tetrahedral and simple-fluid-like behavior. Moreover, we find that the ways three-body angles vary within the hydration shells of model colloids of different types and sizes are nearly identical to the variations seen in bulk water across density and temperature. Importantly, through the principal directions of these variations, we find that perturbations to the hydration-water distributions well predict the thermodynamics associated with colloid solvation, in particular, the relative entropy of this process that captures indirect, solvent-mediated contributions to the hydration free energy.
Planar lens made from germanium metalens array focuses mid-infrared range radiation
Authors: Liebendorfer Adam
Published: September 2019

As thin as a sheet of paper, new array uses nanofins to achieve performance comparable to traditional spherical lenses when imaging flame from a lighter.
An Anionic Calcium Metal–Organic Framework Encapsulated with TbIII Ions as a Recyclable Luminescent Sensor for CrIII and FeIII Ions
Authors: Chen Min , Xu Wen-Ming
Published: September 2019

A 3D CaII-based anionic metal–organic framework (Ca-MOF) with microporous pores containing [(CH3)2NH2]I cations has been synthesised. The Ca-MOF possesses a formula of [(CH3)2NH2]2[Ca5(TATB)4(DMA)4](DMA)10(H2O)2 (H3TATB=4,4′,4″-s-triazine-2,4,6-triyltribenzoic acid, DMA=N,N-dimethylacetamide) with 12-connected [Ca5(O2CR)10] secondary building units and 3-connected TATB ligands to give a rare 12,3-connected net of llj topology. Furthermore, the postsynthetic material (TbIII@Ca-MOF), which is obtained by ion-exchange processes, exhibits an excellent ability for the recyclable detection of CrIII and FeIII ions.
Medullary thymic epithelial NF–kB-inducing kinase (NIK)/IKKα pathway shapes autoimmunity and liver and lung homeostasis in mice
Authors: Shen Hong , Ji Yewei , Xiong Yi , Kim Hana , Zhong Xiao , Jin Michelle G. , Shah Yatrik M. , Omary M. Bishr , Liu Yong , Qi Ling , Rui Liangyou
Published: September 2019

Significance

Aberrant T cell activation augments liver and lung diseases, increasing mortality and morbidity. T cell development is controlled by thymic epithelial cells (TECs); therefore, elucidation of TEC growth and differentiation holds a key for understanding of adaptive immunity and autoimmune disease. We found that NIK/IKKα pathways are highly activated in medullary TECs. TEC-specific ablation of NIK or IKKα blocked medullary TEC development, resulting in production of pathogenic autoreactive T cells due to breakdown of T cell central tolerance. Consequently, NIK- or IKKα-deficient mice displayed fatal autoimmune destruction of livers and lungs. This work unravels a regulation of autoimmunity by TEC-intrinsic NIK/IKKα pathways and identifies the NIK/IKKα pathway as a potential target for the treatment of chronic liver and/or lung disease.
Weak warning signals can persist in the absence of gene flow
Authors: Lawrence J. P. , Rojas Bibiana , Fouquet Antoine , Mappes Johanna , Blanchette Annelise , Saporito Ralph A. , Bosque Renan Janke , Courtois Elodie A. , Noonan Brice P.
Published: September 2019

Aposematic organisms couple conspicuous warning signals with a secondary defense to deter predators from attacking. Novel signals of aposematic prey are expected to be selected against due to positive frequency-dependent selection. How, then, can novel phenotypes persist after they arise, and why do so many aposematic species exhibit intrapopulation signal variability? Using a polytypic poison frog (
Dendrobates tinctorius
), we explored the forces of selection on variable aposematic signals using 2 phenotypically distinct (white, yellow) populations. Contrary to expectations, local phenotype was not always better protected compared to novel phenotypes in either population; in the white population, the novel phenotype evoked greater avoidance in natural predators. Despite having a lower quantity of alkaloids, the skin extracts from yellow frogs provoked higher aversive reactions by birds than white frogs in the laboratory, although both populations differed from controls. Similarly, predators learned to avoid the yellow signal faster than the white signal, and generalized their learned avoidance of yellow but not white. We propose that signals that are easily learned and broadly generalized can protect rare, novel signals, and weak warning signals (i.e., signals with poor efficacy and/or poor defense) can persist when gene flow among populations, as in this case, is limited. This provides a mechanism for the persistence of intrapopulation aposematic variation, a likely precursor to polytypism and driver of speciation.
P2Y2 purinergic receptor modulates virus yield, calcium homeostasis, and cell motility in human cytomegalovirus-infected cells
Authors: Chen Saisai , Shenk Thomas , Nogalski Maciej T.
Published: September 2019

Human cytomegalovirus (HCMV) manipulates many aspects of host cell biology to create an intracellular milieu optimally supportive of its replication and spread. Our study reveals that levels of several components of the purinergic signaling system, including the P2Y2 and P2X5 receptors, are elevated in HCMV-infected fibroblasts. Knockdown and drug treatment experiments demonstrated that P2Y2 enhances the yield of virus, whereas P2X5 reduces HCMV production. The HCMV IE1 protein induces P2Y2 expression; and P2Y2-mediated signaling is important for efficient HCMV gene expression, DNA synthesis, and the production of infectious HCMV progeny. P2Y2 cooperates with the viral UL37x1 protein to regulate cystolic Ca
2+
levels. P2Y2 also regulates PI3K/Akt signaling and infected cell motility. Thus, P2Y2 functions at multiple points within the viral replication cycle to support the efficient production of HCMV progeny, and it may facilitate in vivo viral spread through its role in cell migration.
Extracellular RNA in a single droplet of human serum reflects physiologic and disease states
Authors: Zhou Zixu , Wu Qiuyang , Yan Zhangming , Zheng Haizi , Chen Chien-Ju , Liu Yuan , Qi Zhijie , Calandrelli Riccardo , Chen Zhen , Chien Shu , Su H. Irene , Zhong Sheng
Published: September 2019

Significance

The SILVER-seq technology enables sequencing extracellular RNAs (exRNAs) from a single droplet of liquid biopsy. This study revealed strong associations between serum exRNA expression levels and the donor’s sex and age. SILVER-seq detected serum exRNAs from the genes that are only expressed in brain, suggesting the possibility of monitoring brain gene expression from a blood test. Classifiers based on exRNA expression levels were able to separate breast cancer patients from control donors. The exRNA-based classifiers could also distinguish the patients with recurrent cancer from other breast cancer patients. The SILVER-seq technology can therefore lead the way to future in vitro diagnostics trials based on finger prick blood, which is more accessible for screening and frequent monitoring of human diseases.
Revealing misfit dislocations in InAsxP1−x-InP core–shell nanowires by x-ray diffraction
Authors: Lazarev Sergey , Göransson David J O , Borgström Magnus , Messing Maria E , Xu H Q , Dzhigaev Dmitry , Yefanov Oleksandr M , Bauer Sondes , Baumbach Tilo , Feidenhans’l Robert , Samuelson Lars , Vartanyants Ivan A
Published: September 2019

InAs
x
P1−x
nanowires are promising building blocks for future optoelectronic devices and nanoelectronics. Their structure may vary from nanowire to nanowire, which may influence their average optoelectronic properties. Therefore, it is highly important for their applications to know the average properties of an ensemble of the nanowires. Structural properties of the InAs
x
P1−x
-InP core–shell nanowires were investigated using the coplanar x-ray diffraction performed at a synchrotron facility. Studies of series of symmetric and asymmetric x-ray Bragg reflections allowed us to determine the 26% ± 3% of As chemical composition in the InAs
x
P1−x
core, core–shell relaxation, and the average tilt of the nanowires with respect to the substrate normal. Based on the x-ray diffraction, scanning, and transmission electron microscopy measurements, a model of the core–shell relaxation was proposed. Partial relaxation of the core was attributed to misfit dislocations formed at the core–shell interface and their linear density was estimated to be 3.3 ± 0.3 × 104 cm−1.
Temperature effect on the binding energy and the diamagnetic susceptibility of a magneto-donor in Cylindrical Quantum Dot (GaAs/GaAlAs)
Authors: Janati Edrissi S , Zorkani I , Rahmani K , Chrafih Y , Jorio A , Khenfouch M
Published: September 2019

In this work, the effect of the temperature on the binding energy and the diamagnetic susceptibility of a shallow magneto-donor confined to move in a cylindrical quantum dot made out of GaAs/GaAlAs is studied within the effective mass approximation. We describe the quantum confinement by an infinite deep potential. The results show that when the temperature or the magnetic field increases, (i) the binding energy increases and (ii) the absolute value of the diamagnetic susceptibility decreases. Its effects depend strongly on the size of the CQD.
The phase topology and bifurcation tori of the Hydrogen atom subjected to external fields
Authors: Chatar W , Benkhali M , El Fakkousy I , Kharbach J , Rezzouk A , Ouazzani-Jamil M
Published: September 2019

In this paper, we performed an adapted canonical transformation, and we analysed the phase space topology and the bifurcation of Liouville tori of the Hydrogen atom subjected to three static external fields: Van der Waals potential, electric and magnetic fields. In particular, for all values of the parameters of the system under consideration, the bifurcation diagrams of the momentum mapping are constructed, bifurcations of the common level sets of the first integrals are described and the all-generic bifurcations are computed for all singular points of the bifurcation diagrams. However no author has combined these three fields and studied their behavior. Numerical investigations are performed for the integrable case by means of Poincaré surfaces of section and the phase space trajectories method, and we observed the chaos-order-chaos transition
Novel approach to enhance light absorption of porphyrin/graphene oxide composites by PH and concentration modification for energy applications
Authors: Bajjou O. , Bakour A. , Khenfouch M. , Baitoul M. , Mothudi B. , Maaza M. , Faulques E.
Published: September 2019

Graphene oxide (GO) decorated with nanostructured porphyrin (PN) was synthesized and the interfacial interaction between these two components were investigated by using Scanning electron microscopy (SEM), X-ray, and UV-Visible absorption spectroscopy. These techniques showed good dispersion of GO and PN as well as a π-π intermolecular interaction confirming the energy/charge transfer. Moreover, X-ray diffraction study confirmed the intercalation of PN in GO and their disaggregation. The UV-Visible results showed an important interaction between GO and porphyrin nanorods under different pH values and concentrations resulting in the enhancement of optical absorption of GO-PN. The findings presented here are important to achieve the functionalization of graphene oxide with PN for various optoelectronic applications.
Research on the Development of Jinan Smart Tourism in the Age of Big Data
Authors: Wang Ainiao
Published: September 2019

The development of smart tourism in Jinan lags behind Yantai and Qingdao, which is inferior to other well-known tourist cities in China, such as Beijing and Hangzhou. Under the innovation of network technology and the construction of big data, the development of smart tourism in Jinan presents problems such as low level of construction, difficulty in coordination, insufficient technical support, and lack of professional talents. This paper proposes a development strategy, namely, strengthening big data construction, government leaders lead, promote technological innovation and cultivate professional talents.
Research on Author Name Disambiguation Based on Fusion Features and Semantic Fingerprints
Authors: Zhai Xiaorui , Han Hongqi , Li Zhong , Ran Yaxin
Published: September 2019

Author name disambiguation has been a challenging problem in many applications. In order to promote researches to solve name disambiguation, Aminer launched the Open Academic Data Challenge 2018 jointly with Chinese Association for Artificial Intelligence and China Knowledge Centre for Engineering and Technology. Aminer is a scholar-cantered academic search and mining platform covering more than 200 million papers and more than 100 million scholars in various academic fields. Our team proposed a name disambiguation method based on fusion features and semantic fingerprint technique to participate in the competition. The method identified authors with same names through organization feature and co-author feature at first, and then it solves ambiguity names by way of semantic fingerprints which are 128-bit binary vector generated from textual features of papers by Simhash algorithm. Our method scored 0.609 on the verification set and 0.879 on the test set ranking first in the final submission.
Choice of the Number of Hidden Layers for Back Propagation Neural Network Driven by Stock Price Data and Application to Price Prediction
Authors: Zhang Peipei , Shen Chuanhe
Published: September 2019

Since the stock market is dynamic and nonlinear, we adopt the neural network to forecast the stock price. We construct the single hidden layer prediction model firstly, and analyse the effect of prediction accuracy on neurons amount and epochs. To improve the prediction accuracy and operating rate, we then construct the multiple hidden layers prediction model, and provide some theory guide on setting the number of each hidden layer for neural network with multiple hidden layers. Finally, we make a choice of the number of hidden layers by analysing the effect of stock price prediction, and the empirical results obtained demonstrate that the prediction performance of two hidden layers prediction model is better than that of the single hidden layer prediction model. Additionally, the empirical results obtained also demonstrate that the more epochs of training network, the better the results obtained with using the same number of neurons.
Design and Implementation of UAV Engine Test System
Authors: Bao Chuanmei , Zheng Xing , Cheng Jian , Xu Luoting
Published: September 2019

To meet the testing requirements of small UAV piston engine, a virtual instrument-based UAV engine test system is designed. This system has the capability of on-line testing on engine even without the UAV system-wide support. With virtual instruments, serial analog-to-digital conversion and other new technologies, it can replace the ground control station to control the engine; realize rapid and portable testing as well as integrated display of the engine performance parameters; and based on the testing data, perform fault analysis and provide maintenance instructions when appropriate. The system covers the shortage of existing test methods, and improves the efficiency of UAV engine system test in field operations.
Research on Eliminating Uncertain Abnormal Large Data in Network Based on Ant Colony-Genetic Algorithms
Authors: Cui Haiting
Published: September 2019

In order to solve problem of high miss detection rate and false detection rate in current methods of eliminating abnormal big data in network, a method under asynchronous transmission based on ant colony-genetic algorithm is proposed. Data is validated by measuring proximity and initialization parameters of ant colony are set and real-time evolution rate of offspring is counted in which pheromones are used to train ants, then chromosomes are coded to obtain fitness function of particles. The fitness function is used to calculate individual optimal value and global optimal value of particles. Then concentration of global pheromone is used to judge whether the Ant Optimization meets end condition. If it is, large uncertain data are detected and eliminated. Experiments show that the average missed detection rate is about 2.5%, and the false detection rate is low.
An Advanced Pyramid Network Technology for Optical Character Recognition
Authors: Zhang Haoruo , Cheng Zhongquan
Published: September 2019

Optical Character Recognition (OCR) technology can quickly convert text and digital information in pictures into text information, which has been widely used in actual scenes. However, in some complex scenes, such as different light, angle or occlusion, the existing OCR systems still cannot reach the required accuracy. This paper proposes an advanced pyramid network structure, which uses multiple different scales and parallel pyramid network structures to deal with the problem of different character scales and misalignment. Each pyramid network structure has at least four convolutional layers. At the same time, in each pyramid network connection, the proportionally discarding parameters is introduced to increase the calculation speed further. The advantage of this network structure is very robust to more differently sized characters and increases the number of valid parameters obtained through training. Experiments on open source data sets show that the method has better recognition accuracy and speed.
A Secure Authentication and Key Agreement Scheme in Smart Home
Authors: Song Dawei , Wen Fengtong
Published: September 2019

Secure communication between users and devices is an important aspect of IoT applications. Smart home is an emerging key element in IoT applications. Due to limited computing resources, it is difficult to deal with these communication problems. Therefore, a safe and efficient solution is needed to complete the communication between users and devices. Sometimes secure communication of information is required between devices and devices. In view of this point, this paper proposes a secure and efficient authentication and session key negotiation scheme between smart home devices.
Creating and Optimizing Learning Maps by Negative and Positive Convolutional Neural Networks
Authors: Cui Xu
Published: September 2019

In this paper, a model of creating and optimizing learning maps by negative and positive convolutional neural networks is established. Firstly, the ontology knowledge base, learning maps and convolutional neural network are introduced. Then after three steps of reverse link, reverse pooling, reverse convolution of negative convolutional neural networks (RCNN), three kinds of learning maps: linear learning map, tree learning map, and graphic learning map are created. Learners learn knowledge by the direction of learning maps, different learning matrices are obtained in the process of learning, and notes and ideas are recorded in electric note books. At last, positive convolutional neural networks (CNN) are used to optimize leaning maps. After three operations of convolution learning map matrix, pooling learning nodes, link knowledge domains, the results of weakening what learners are not interested in, weakening the task that learners think is difficult, reducing the repetition of public knowledge and randomly selecting according to the complexity of the learning map can reach. Improving learners’ confidence and Enriching the content of the learning maps have been done also.
Situation-based Real-time Assignment of Air Combat Command and Guidance Targets
Authors: You Hanghang , Han Qisong , Yang Haiyan
Published: September 2019

In order to solve the problem of lack of real-time allocation in traditional air combat command and guidance target allocation, this paper proposes a new model. Firstly, this paper establishes a target allocation model, discusses the situation under different situation conditions, and quantifies the situation of over-the-horizon air combat reasonably. On this basis, inspired by the algorithm of Traveling Salesman Problem (TSP), the 0-1 programming model is established to calculate the target allocation. Finally, a simulation scenario calculation is used to verify the correctness of the model and the algorithm, and the rationality of the results is analyzed from the qualitative analysis.
A Density-based Under-sampling Algorithm for Imbalance Classification
Authors: Hou Yun , Li Bailin , Li Li , Liu Jiajia
Published: September 2019

Imbalance classification is an interesting issue in machine learning and data mining. In recent years, many related algorithms have been proposed to solve such an issue. Among them, under-sampling is an effective and timesaving data pre-processing method, which balances the dataset by removing some examples from the majority class. However, these proposed under-sampling methods often lose some useful information or ignore noise in the datasets, which will result in the performance degradation. This paper proposes a density-based under-sampling algorithm (DBU) to solve these two problems. In feature space, similar examples are close to each other and noisy example is far from other examples belonging to the same class. Thus the similar examples have a high density while the noisy example has a low density. DBU uses the local density peaks to represent the whole majority class, so that it can retain the useful information and eliminate the noisy examples automatically. To evaluate our algorithm, experiments are conducted on 15 two-class imbalanced datasets. Experimental results show that DBU achieves the better results than other under-sampling methods.
A Novel Environment Perception Algorithm Combining Biologically Inspired Features and Brain-like Memory Pattern
Authors: Yan Xingwei , Yang Afeng , Du Chun , Liu Zhangmeng
Published: September 2019

In this paper, we proposed a novel framework for environment perception. The proposed model combines the physio-logical mechanisms of human vision and psychological principle of cognitive development. Our algorithm consists of two major components, a gist module and a memory module. The gist module simulates the cognition process associated with visual attention. The memory module is simulated by the Incremental Hierarchical Discriminant Regression (IHDR) tree, which ensures the memories of the world are distributed over the hierarchy. Experimental results on the USC dataset demonstrated that the proposed algorithm is robust and efficient.
The Nonlinear Filtering Feature Identification Methods for the Duffing Chaotic Fractal State
Authors: Zhang Yang , Rui Guosheng , Dong Daoguang , Zhang Haibo
Published: September 2019

With the wide applications of weak signal detection techniques for Duffing system, it is important to study the fast and accurate identification methods for fractal state, such as in the field of all kinds of signals detections, by fractal state discriminant of Duffing, the target detections can be achieved under the conditions of extremely low signal-to-noise ratios. Aiming at the existing problems of the algorithm, a new idea is put forward, the Duffing oscillator is converted to the state equation of nonlinear filters, respectively by CKF(Cubature Kalman Filter,) PF(Particle Filter) and EKF(Extended Kalman Filter) three nonlinear filtering algorithm for recursive estimations, the inherent rules whose filtering parameters change with the system state changing will be researched, and a discrimination method of oscillator Fractal state is proposed; the experimental results show that the three kinds of filtering methods can all achieve the Duffing oscillator state tracking, and with the help of the filtering characteristics, the identification of the oscillator state can be distinguished and feature extraction can both realize the qualitative and quantitative detections.
An Overview on Quality Evaluation Constitution in Context of Big Data Application
Authors: Li Zhe , Wu Sai , Zhou Hongwei , Zou Sheng , Dong Tingting
Published: September 2019

Big data applications have been applied in various domain. With the development of big data in electric power, the application of smart grid is developing towards a more intelligent and coordinated direction. However, except for the functionality realization, the non-functional requirement such as data quality become an more and more important issues. For the reason that it relate to the validation of final results of function in big data area. The criterial of quality evaluation is crucial to the evaluation for data quality, and also the standardizations the scientific management of big data in enterprises can be used as the guide the construction of new information infrastructure. This paper investigates the existing data quality evaluation dimensions, model and application domain, and combines with the practice in industry, analyses the standards, dimensions and methods and then gives suggestions on promoting the data quality evaluation in specific domain.
The Pseudo-DF Approach for Learning Huge-scale Data
Authors: Deng Chang Hui , Wang Xiao Jun , Gu Jun , Wang Wei
Published: September 2019

As the advent of the big data era, huge-scale data continuously appears in various fields of science, commerce, industry and society. More algorithms/methods/approaches are urgently required to learn huge-scale data collected from different applications/backgrounds. Therefore, the Pseudo Data Flow (Pseudo-DF) approach with ensemble ReOS-ELMs is proposed in this paper. The Pseudo-DF approach randomly divides a huge-scale data set into K (K>1) non-overlapping data chucks, and a Pseudo-DF is constructed by these data chucks. The computation of a huge-scale data is changed into that of a Pseudo-DF with smaller-scale chucks, the computational burden will be much reduced. Then, the ensemble Regularized OS-ELMs (ReOS-ELMs) based on Different random Hidden-node Parameters (DiffHPs) is presented to learn a Pseudo-DF, which is a recursive leaning algorithm possessing the advantages of low computational burden, high accuracy, well generalization and stability, and strong robustness. Lastly, experiments are performed to validate the effectiveness of the proposed approach.
A Fraudulent Data Simulation Method Based on Generative Adversarial Networks
Authors: Wang Xiaoguo , Zhao Ran , Li Yuanxiu
Published: September 2019

In view of the positive and negative sample imbalance in the process of establishing anti-fraud rules and models of the third-party payment, this paper simulated the fraudulent transactions by means of generative adversarial networks. In the design of the model, the output of the discriminator is optimized where classification results are set to three categories; the loss function of the GAN model is changed to include two parts: the source loss function and the category loss function. The generated data and the real business data are mixed according to a certain ratio, to train a fraud detection model. Verify the generation effect of this simulation method by comparing the detection effects of different detection models.
Security-aware Localized Topology Control Algorithm for Mobile Ad-hoc Networks
Authors: Wang Wenjian , Feng Renjian , Zhu Min , Wu Yinfeng , Liu Yadong , Ye Yafeng
Published: September 2019

In this paper, we extend the topology control schemes by making security the primary concern. For this purpose, we propose a security-aware localized topology control algorithm that can maximally avoid making malicious nodes into intermediate nodes. In order to marginalize malicious nodes, we introduce a new metric for characterizing the security status of each link in the network. Each node independently builds a local spanning tree for finding a reduced neighbor set. In the meantime, we maximally avoid using insecurity links in their neighborhood for the local spanning tree construction. Simulation results show that our algorithm significantly improves the security of a network.
System-on-a-chip (SoC) FPGA-based Hardware Acceleration for Green Crop Segmentation
Authors: Li Shan , Zhang Zhibin , Zheng Yuan
Published: September 2019

With the development of artificial intelligence technology, agricultural robot plays a significantly important role for agricultural intelligence. Crop segmentation is a critical and fundamental pre-processing step for agricultural robot navigation and crop growth monitoring. Although there are several crop segmentation methods, they cannot meet the online real-time requirement for agricultural robot. In view of this, a real-time crop segmentation system implemented on a SoC-FPGA is first proposed in this paper, which contains image loading module, segmentation algorithm module and image display module. As a core module, segmentation algorithm module consists of greenness identification, thresholding operation and morphological operation. Its hardware implementation adopted a parallel pipeline architecture to enhance real-time performance. The proposed system is implemented on a Xilinx Zynq-7000 FPGA and the experimental results showed that it can achieve a frame rate of 60 frames per second for the image resolution of 1920×1080 with the average accuracy of 91%.
A Load Balancing Vertical Handoff Algorithm Considering QoS of Users for Heterogeneous Networks in Power Communication
Authors: Shao Gang , Wu Wenqin , Yin Lei , Ding Chenyang
Published: September 2019

With the increase of the number of users and the complexity of services, only single communication network cannot meet all its requests in power communication networks. Therefore, heterogeneous networks are an effective solution. Vertical handoff (VHO) is an important step in the process of heterogeneous network convergence. An appropriate handoff algorithm can improve the quality of service (QoS) of users. In order to solve the problem of network congestion caused by a large number of users connecting to partial networks in heterogeneous networks, a vertical handoff algorithm based on load balance is proposed. Among the networks that can meet users’ requirements, the most load-balancing network is obtained using optimal algorithm. In addition, to consider users’ requirements and ensure the QoS of network service, we use analytic hierarchy process (AHP) to weight different networks. This algorithm can take full use of the various network resources, so that the distribution of the load among networks is average. The simulation results show that the proposed algorithm can select the network according to the user’s demands and each network has close network utilization under the algorithm.
Research on Evaluation Method of LDA Topic Model in Mail Classification
Authors: Gong Hechen , You Fucheng , Lai Shuren
Published: September 2019

The LDA topic model is a document generation model, which is an unsupervised machine learning technique that can be used in keyword extraction, topic classification, and so on. The main purpose of this paper is to effectively evaluate the number of optimal topics of the LDA topic model in the message subject classification and extract the importance of the topic, so that the LDA topic model can be highly readable after subject classification. Therefore, this paper proposes a keyword matching and subjective statistical value word comparison (KM-SSVW) for the subject classification of emails, the keyword matching technique in this method uses the TF-IDF technique. This method can accurately evaluate the extracted keywords and optimize the number of topics. The data in this article is mainly from the mail gate, a total of 7,000 messages that Hillary communicates with others. The empirical results show that the proposed method of keyword matching and subjective statistical value word comparison has a good effect on subject quantity optimization and subject word readable evaluation. However, there are still some limitations in this paper. In the experiment, new methods are not validated for other types of data sets, such as microblog short text, XML documents, and WeChat public platform articles.
Intrusion Detection Method Based on Improved K-Means Algorithm
Authors: Meng Chun , Lv Yuanyuan , You Long , Yue Yuchen
Published: September 2019

Data mining technology has a good application in the field of intrusion detection. For the problem that K-Means algorithm is difficult to process high-dimensional data, local optimal solution, and cannot determine K value, this paper proposes an improved K-Means algorithm. Firstly, the PCA algorithm is used to reduce the dimension of the data set, and then the Outlier detection is used to eliminate the Outliers that have a great influence on the final clustering result. Then, the initial clustering center point is selected based on the distance to avoid the local optimal solution. Finally, K- is used. The Means algorithm performs clustering to obtain an intrusion cluster. The experimental results show that compared with the common data mining-based intrusion detection algorithm, the proposed method has a good performance in detection rate and false positive rate, and its performance has also improved.
Deep Belief Network based Coal Mine Methane Sensor Data Classification
Authors: Wu Xuefeng , Zhao Zhikai , Wang Li
Published: September 2019

Gas explosion is the main hazard which affects the safety of coal mine production. One way to solve the problem is to predict the dangerous levels of methane concentration using the sensor time series from hazard monitoring systems. In this paper we proposed our method based on DBN to classify the dangerous levels of methane concentration. A multilayer RBM network is built to reconstruct the methane sensor data and depth characteristics of methane disaster classification are extracted. Then a BP network is used for classification learning. We test our method on a real coal mine methane sensor data, and contrast with two classical methods, SVM and KNN. The experiments results showed that our proposed deep feature learning methods could achieve better performance than the two classical shallow methods.
Research on the Falling Detection System for Elderly-assistant and Walking-assistant Robot
Authors: Wei Xiaojuan
Published: September 2019

With the development of living standards, the problems caused by aging population have become more and more serious. At the same time, with the growth of age, the body function of the elderly is degenerating and the body flexibility is reducing, which leads to frequently occurring of falling in elderly. The research data shows that falls are the most common and unpredictable problem for old people, which are even life-threatening. Therefore, a falling detection system used in the walking-assistant robot is proposed to make the walking-assistant robot better service during the outdoor walking. Firstly, a general design plan of the falling detection for old people, which is used in walking-assistant robot, is introduced through the analysis of falling mechanism of elderly. Then, a acceleration sensor ADXL345 and the detection module of touch pressure sensors are used to collect data of acceleration and hand touch force of users. Next, the feature extraction is carried out on the above data, and the falling detection is delivered by time domain feature representation and recognition algorithm. At last, the test system is built for test verification, and the results show that the falling detection system has a stable and reliable performance and high detection accuracy rate, reaching 99.05%. It can detect the falling of elderly effectively.
Multi-Person Pose Estimation via Learning Feature Integration
Authors: Hu Xiaomei , Tian Jiahao , Qiao Huixiang , Wang Minjie
Published: September 2019

Human pose estimation is a fundamental but challenging task in computer vision. Although batch normalization is widely used for deep learning, feature extraction in deep convolutional neural networks (DCNN) is still not well explored. In this work, we propose a pose encoding module (PEM) to enhance the learning ability and generalization ability of feature extraction. Given input images, PEM integration instance normalization and batch normalization, combining them in an appropriate way to learn to capture and eliminate appearance changes while maintaining the distinction between learning features can be seen as an integration and adjustment of global information. In addition, we use a simple and efficient up-sampling strategy to recover high-resolution representations for predicting more accurate human keypoint heatmaps, which has achieved better performance than the average network in recent missions. We studied our approach on a standard benchmark for human pose estimation.
Research on Visual Algorithm of Road Garbage Based on Intelligent Control of Road Sweeper
Authors: Min Haitao , Zhu Xuhan , Yan Beizhan , Yu Yuanbin
Published: September 2019

In order to reduce drivers’ labor intensity during the operation of road sweeper, the development of road operation feature recognition algorithm based on visual perception technology is of great significance, especially in the intelligent control of the cleaning devices and the improvement of energy saving effect. This paper summarizes the development status and trends of road sweepers in domestic and foreign, proposes intelligent road sweeper operation power control system, and designs key technologies. Taking the type of garbage and the coverage of garbage as the target of feature recognition, the corresponding visual perception and recognition algorithm was developed, and the algorithm was tested and analysed. The recognition rate and recognition effect reached the expected.
Research on Bearing Stress Characteristics of Stacking Robot Base under New Engineering Background
Authors: Duan Peng , Li Yulong , Zhou Wenyu , Liu Jun
Published: September 2019

Under the background of the continuous development of new engineering, more and more researches are aiming at new cutting-edge technologies, among which industrial robots have attracted much attention. Based on typical trajectory and a given load condition of the palletizing robot, the dynamics analysis and calculation was accomplished by using the dynamics analysis and calculation module of Adams software under the condition that the palletizing robot was working normally, then obtained the dynamic load variation when rolling bearing was running, furthermore, the contact stress change rule was obtained by using the finite element analysis calculation method, and the rule was regard as the basis of rolling bearing fatigue life analysis and evaluation. Judging from the results of analysis and calculation, the contact stress distribution of rolling bearing under the combined effect of both external load and dynamic load, which was similar to the phenomenon of commonly used rolling bearing stress fatigue destruction, this method can effectively overcome the deficiencies of traditional rolling bearing fatigue life calculation method that only consider static load, and reference for relevant engineering and technical personnel.