bluuebunny/crossref_metadata_2025 huggingface dataset:
Faecal immunochemical tests for occult blood testing should not be used outside of bowel screening: an audit of a large general practice
Authors: Byun Ui Ho , Anderson Neil , Upton Arlo , Frankish Paul
Published: September 2019

ABSTRACT

INTRODUCTION
The faecal immunochemical test (FIT) detects small quantities of human haemoglobin in faeces. This test has increasingly become the screening tool of choice in bowel cancer screening programmes worldwide, including New Zealand’s upcoming national screening programme.

AIM
This study audited the appropriate use of faecal immunochemical tests (FIT) in general practice as current recommendations discourage the use of FIT outside the National Bowel Screening Programme.

METHODS
Data on all FIT requested by a multiclinic general practice serving 16000 patients from May 2017 to May 2018 were extracted from clinical records. Patient characteristics, results of tests, clinical rationale for the test, number of referrals and results and the completeness of clinical evaluation were recorded.

RESULTS
In all, 184 patients received an FIT, with 13 (7.1%) positive and 145 (78.8%) negative tests, and 26 (14.1%) tests declined by the laboratory. Nine patients (69.2%) with a positive FIT, 12 patients (8.1%) with a negative FIT and one patient (3.8%) with a declined test were referred to gastroenterology services. Seven colorectal cancers were detected, all in patients with a positive FIT who were aged between 67 and 91 years. FIT was requested most for changes in bowel habit (53%) and blood in stool (15%); 10% of tests were ordered for reassurance and 9% did not record an indication for the test. Two general practitioners (of 17 in the practice) accounted for over half of all tests requested.

CONCLUSIONS
Because FIT is only a screening tool for colorectal cancer, direct referral is recommended for symptomatic patients. Although cancers were detected only in patients with positive FITs, these patients would have qualified for direct referral for definitive investigation, and a referral was made concurrently. Awaiting test results may also delay necessary referrals and a negative FIT may produce false reassurance.
Decreased humoral immunity to mumps in young adults immunized with MMR vaccine in childhood
Authors: Rasheed Mohammed Ata Ur , Hickman Carole J. , McGrew Marcia , Sowers Sun Bae , Mercader Sara , Hopkins Amy , Grimes Vickie , Yu Tianwei , Wrammert Jens , Mulligan Mark J. , Bellini William J. , Rota Paul A. , Orenstein Walter A. , Ahmed Rafi , Edupuganti Srilatha
Published: September 2019

Significance

The live-attenuated mumps-measles-rubella (MMR) vaccine has been highly successful in the United States since its introduction 47 years ago. However, for the past decade, mumps outbreaks have been occurring among young adults who were vaccinated as children. Waning immunity has been proposed as a key contributing factor to mumps resurgence. In our sample (n= 71) of 18- to 23-year-old college students, the majority had detectable mumps IgG antibodies by enzyme-linked immunosorbent assay (ELISA) but the magnitude was lower than rubella. Neutralizing antibody titers were 6-fold lower to a circulating genotype G mumps strain versus the vaccine strain. Ten percent of our participants had no detectable memory B cells to mumps. Strategies are needed to improve immunity to the mumps vaccine.
CCA1 and LHY contribute to nonhost resistance to Pyricularia oryzae (syn. Magnaporthe oryzae) in Arabidopsis thaliana
Authors: Yamaura Saaya , Yamauchi Yuri , Makihara Motoi , Yamashino Takafumi , Ishikawa Atsushi
Published: September 2019

The circadian clock enables plants to adapt to their environment and control numerous physiological processes, including plant–pathogen interactions. However, it is unknown if the circadian clock controls nonhost resistance (NHR) in plants. To find out, we analyzed microarray data with the web-based tool DIURNAL to reveal that NHR-related genes show rhythmic expression patterns in the absence of a pathogen challenge. Our clock mutant analyses found that cca1-1 lhy-11 double mutant showed compromised NHR to Pyricularia oryzae, suggesting that two components of the circadian clock, CCA1 and LHY, are involved in regulating penetration resistance in Arabidopsis thaliana. By analyzing pen2 double mutants, we revealed that CCA1 contributes to time-of-day-dependent penetration resistance as a positive regulator and that LHY regulates post-penetration resistance as a positive regulator. Taken together, our results suggest that the circadian clock regulates the time-of-day-dependent NHR to P. oryzae and thus enables A. thaliana to counteract pathogen attacks.

Abbreviations: EE: evening element; ETI: effector-triggered immunity; NHR: nonhost resistance; PAMP: pathogen-associated molecular pattern; PTI: PAMP-triggered immunity; SAR: systemic acquired resistance.
Learning Concepts: A Learning-Theoretic Solution to the Complex-First Paradox
Authors: Poth Nina L. , Brössel Peter
Published: September 2019

Children acquire complex concepts like dog earlier than simple concepts like brown, even though our best neuroscientific theories suggest that learning the former is harder than learning the latter and, thus, should take more time (Markus Werning). This is the complex-first paradox. We present a novel solution to the complex-first paradox. Our solution builds on a generalization of Fei Xu and Joshua B. Tenenbaum’s Bayesian model of word learning. By focusing on a rational theory of concept learning, we show that it is easier to infer the meaning of complex concepts than that of simple concepts.
The very large electrode array for retinal stimulation (VLARS)—A concept study
Authors: Lohmann Tibor Karl , Haiss Florent , Schaffrath Kim , Schnitzler Anne-Christine , Waschkowski Florian , Barz Claudia , van der Meer Anna-Marina , Werner Claudia , Johnen Sandra , Laube Thomas , Bornfeld Norbert , Mazinani Babak Ebrahim , Rößler Gernot , Mokwa Wilfried , Walter Peter
Published: September 2019

Objective. The restoration of vision in blind patients suffering from degenerative retinal diseases like retinitis pigmentosa may be obtained by local electrical stimulation with retinal implants. In this study, a very large electrode array for retinal stimulation (VLARS) was introduced and tested regarding its safety in implantation and biocompatibility. Further, the array’s stimulation capabilities were tested in an acute setting. Approach. The polyimide-based implants have a diameter of 12 mm, cover approximately 110 mm2 of the retinal surface and carrying 250 iridium oxide coated gold electrodes. The implantation surgery was established in cadaveric porcine eyes. To analyze biocompatibility, ten rabbits were implanted with the VLARS device, and observed for 12 weeks using slit lamp examination, fundus photography, optical coherence tomography (OCT) as well as ultrasound imaging. After enucleation, histological examinations were performed. In acute stimulation experiments, electrodes recorded cortical field potentials upon retinal stimulation in the visual cortex in rabbits. Main results. Implantation studies in rabbits showed that the implantation surgery is safe but difficult. Retinal detachment induced by retinal tears was observed in five animals in varying severity. In five cases, corneal edema reduced the quality of the follow-up examinations. Findings in OCT-imaging and funduscopy suggested that peripheral fixation was insufficient in various animals. Results of the acute stimulation demonstrated the array’s ability to elicit cortical responses. Significance. Overall, it was possible to implant very large epiretinal arrays. On retinal stimulation with the VLARS responses in the visual cortex were recorded. The VLARS device offers the opportunity to restore a much larger field of visual perception when compared to current available retinal implants.
Shape effects on the diamagnetic susceptibility in Inhomogeneous Quantum Dots
Authors: M’zerd S. , Janati. Edrissi S. , Chrafih Y. , Rahmani K. , Khenfouch M. , Zorkani I. , Jorio A.
Published: September 2019

We have studied the magnetic field effects on the diamagnetic susceptibility and binding energy of a hydrogenic impurity in a Inhomogeneous Quantum Dots by taking into account various geometries of these systems like spherical, cylindrical and cubic inhomogeneous Quantum Dots. The calculation was performed with the use of a variational method in the effective mass approximation. We describe the effect of the quantum confinement by an infinite deep potential. In order to make a comparison of the geometry, the results obtained for these tree structures show that the diamagnetic susceptibility and the binding energy is proportional with the magnetic field and strongly depend on the position of the donor. There is more pronounced for the large spherical layer.
ELABORATION AND CHARACTERIZATION OF ZNO THIN FILMS STRUCTURAL AND OPTICAL STUDY
Authors: Adar M , Najih Y , Charafih Y , Rahmani K , Khaouch Z , Mabrouki M
Published: September 2019

The Nano-topographical structure of the solid surface is known as a necessary parameter in the physicochemical characterization and wetting properties. In this study, the physicochemical properties are evaluated by calculating the surface energy and by measuring the contact angle. The structural proprieties were determined using XDR. The optical proprieties were studied using the UV-visible technics. Substrates used in this study are the zinc oxide thin films deposited on the glass by sputtering under different powers (150, 200 and 250 watt). The Nano -topographic properties were examined using the atomic force microscopy (AFM) in order to calculate the roughness of different substrates. As results, the images obtained by atomic force microscopy showed that the growth of the power causes the growth of the roughness. XRD diagram assessment revealed that the deposited films have a preferential crystallographic direction according to the (002) plane while maintaining the initial orientation. The optical characterization showed that the bandwidth of these films is in the order of 3.28 eV. It is interesting to mention that the increase in RF power has slightly increased the energy of gap.
Synthesis, characterization and evaluation of bioactivity of glasses in the CaO-SiO2-P2O5-MgO system with different CaO/MgO ratios
Authors: Chajri S , Bouhazma S , Adouar I , Herradi S , Khaldi M , El Bali B , Lachkar M
Published: September 2019

Bioactive glasses (80Mg, 70Mg, 60Mg and 50Mg) in the system SiO2-CaO-P2O5-MgO were prepared by sol-gel method and then characterized. The structure of the synthesized samples has been studied by X-ray diffraction (XRD), Fourrier Transform Infrared Spectroscopy (FTIR) and Environmental Scanning Electron Microscopy (ESEM).In vitrobioactivity tests were performed in Simulated Body Fluid (SBF). The apparent density and wettability variation with time were measured. Thein vitrostudies showed the formation of an apatite-like layer covering areas of the material surface. The variation in the CaO/MgO ratio has an influence on the chemical durability and bioactivity. The XRD and FTIR analysis revealed that the samples with larger CaO/MgO ratio exhibited better bioactivity. The results showed that the 50Mg glass which has the higher content of CaO/MgO is hydrophilic sample for the two used fluids (water and SBF). The porosity and hydrophilicity increase with increased the rate of CaO/MgO. The surface of bioglasses became rougher with the increased CaO/MgO ratio, which may lead to a decrease in water contact angle.
Nanostructured Tungsten Trioxide (WO3): synthesis, structural and morphological investigations
Authors: Hatel R , Baitoul M
Published: September 2019

Tungsten trioxide (WO3) has attracted considerable attention due to its promising and remarkable properties. In this study, we have prepared WO3 nanostructures via a simple chemical method using tungsten carbide (WC) as a precursor. This novel approach has many advantages such as high yields, simple methodology and easy work up. The X-ray diffraction (XRD) pattern initially revealed the formation of the intermediate phase of tungsten trioxide hydrate (WO3.H2O), then the complete transformation to pure WO3 after annealing at 600°C for 5h in air atmosphere. The prepared WO3 nanostructures crystallized into a monoclinic phase. The Scanning electron microscopy (SEM) image indicates the exceptional porous morphology, which consisted of hollow sphere-like shape with a uniform distribution. Fourier transform infrared (FTIR) spectroscopy confirmed the structural composition and the purity of the formed WO3. The experimental results proved that our simple approach offers a promising route to prepare WO3 nanostructures as high performance material for advanced applications.
Study on the effect of Er dopant on the structural properties of ZnO nanorods synthesized via hydrothermal method
Authors: Achehboune M , Khenfouch M , Boukhoubza I , Mothudi B , Zorkani I , Jorio A
Published: September 2019

Rare earth Er-doped ZnO nanorods were prepared by a simple hydrothermal method in order to search for a new functional for potential application in optoelectronic. Nanorods were deposited onto glass substrates with zinc nitrate, hexamethylenetetramine and Erbium chloride as starting materials. The results obtained by XRD spectrum indicated the presence of hexagonal (wurtzite) structure for both pure and Er doped ZnO NRs with additional cubic phase of Er2O3 that appeared in the doped sample. The size and lattice strain of particles have increased after doping with Erbium. The increase of their sizes can be explained by the incorporation of Er atoms into ZnO lattice.
The Research on the Simulation of VHF Radio Broadcasting Loss
Authors: Li Ting , Zhang Han , Gao Xiufeng , Wang Shuai
Published: September 2019

VHF radio is a crucial part of the communication system in the battlefield. Since that the paper has adopted the idea of hierarchical modelling, and aiming at building up a simulation model of VHF radio accordingly, which is described with CCIR model, and the elevation data is presented with topographical map. Finally, the battlefield simulation of VHF radio is implemented with OMNET++. The result of simulation validated the credibility of the method, which would provide the simulation of battlefield in complicated electro-magnetic environment with new practical approaches.
SINR and MI-based Robust Transmitted Waveform Design in the Situation of Multiple Targets
Authors: Chen Xu , Wang Bin , Wu Rihan , Fan Ruiyi , Wang Lulu
Published: September 2019

Most of the previous researches on transmitted waveform design assume that the radar can perceive the real target spectrum, as the environments of the radar target become more and more complex, the real target spectrum cannot be accurately captured by radar in many cases and the required performances of detection and estimation for radar cannot be met. Therefore, under the complex electromagnetic environment and the scenario of multiple targets, to ensure the detection and estimation performance of radar system, the SINR and MI-based robust transmitted waveform design methods are proposed in this paper. Simulation results indicate that the designed robust transmitted waveforms ensure the detection and parameter estimation performance effectively and provide useful guidance for energy allocation of transmitted waveform.
Estimation of Intrinsic Dimension using Supervised Parameter Selection Method
Authors: Qiu Haiquan , Yang Shulun
Published: September 2019

In this paper, we propose a new method for estimating the intrinsic dimension of datasets. The new method uses the local information of different scales of the sample points (by adjacency matrix) to estimate the intrinsic dimension. The only parameter used in the new method is the scaling ratio k, which determines the adjacency matrix of different scales. We propose a parameter selection method based on the difference of estimated dimension and the classification accuracy of projection data. Experiments on real datasets demonstrate the effectiveness of the proposed method.
A Real-time Algorithm for Regional Crowd Counting Based on Survival Analysis Theory
Authors: Wang Jun , Fu Yongfeng , Ma Lianhang , Yang Xiaokang , Dong Jiangping
Published: September 2019

This paper proposes a real-time algorithm for regional crowd counting based on survival analysis theory. Our algorithm can accurately calculate regional people flow in real time over a long period, which considers tourists’ stopping time as a type of survival time, and discretizes its continuous distribution probability as a proportion of stopping people in a monitoring region. Compared to other crowd counting methods, our algorithm calculates regional population in real time using a mathematical statistical model, which greatly reduces the dependence on hardware devices. Experiments in two real scenarios verify that the average accuracy of our algorithm is over 91% in comparison with 60% of sensing devices.
Research on a Novel Improved KMP Fuzzy Query Algorithm
Authors: Ni Bowen , Hu Xiaomei , Chai Jianfei , Qu Hewei , Yu Tao
Published: September 2019

The rapid development of computer technology has led to an increasing demand for database management information systems. Most of the data queries existing in the current system use accurate query methods, which leads to inefficient query and cannot solve the fuzzy matching problem between strings. Based on the Knuth-Morris-Pratt algorithm, this paper introduces the concept of ambiguity and proposes an improved KMP fuzzy query algorithm, which is applied to the disease query system to verify the feasibility of the algorithm. The improved KMP fuzzy query algorithm not only has a high matching speed between strings, but also satisfies the fuzzy matching between strings. Compared with the traditional BF algorithm, KMP algorithm and other algorithms, the improved KMP fuzzy query algorithm has superiority in terms of fuzzy matching.
Research on Feature Recognition of UAV Acoustic Signal Based on SVM
Authors: Jin Hengkang , Zhang Yiwen
Published: September 2019

At present, the analysis of UAV flight acoustic signals is mainly based on traditional speech signal processing methods, and has not been analyzed in depth. According to the flight signal of UAV, combined with the aerodynamic characteristics of UAV, the characteristics of UAV’s acoustic signal are analyzed. The three feature extraction algorithms of pitch period, FFT and Mel Cepstral Coefficient (MFCC) are analyzed and compared. Feature extraction is performed, and a support vector machine (SVM) classification algorithm is applied to perform multi-classification model recognition. The measured and experimental results show that based on SVM classification and recognition, the three feature recognition methods all realize the classification of the model. The comprehensive FFT is the best, and the MFCC is the second. The pitch period is not suitable as the feature extraction method alone.
RETRACTED: Application Analysis Based on Computer Software Technology in Big Data Era
Authors: Zhang Peijun
Published: September 2019

With the rapid development of the economy and the continuous development of Internet technology, China has entered the era of big data. In the era of big data, the application of computer software technology is more extensive and in-depth. This article is mainly from the following four aspects. First, a brief introduction to computer software technology and the era of big data; Second, the application of computer software technology in the next generation of big data; Third, the challenges that computer software technology needs to face in the era of big data; Fourth, in the era of big data considerations for using computer software technology. This paper briefly introduces and analyzes the application of computer software technology in the era of big data.
Incentive Mechanism of Medical Data Sharing Based On Information Entropy in Blockchain Environment
Authors: Liang Xiaoyan , Chen Wanghu , Li Jing , Mu Yuxiang , Tian Zhen
Published: September 2019

With the rapid development and wide applications of information technology in the medical field, the data sharing of medical information has become one of the focus that has been received much attention by most researchers in recent years. At present, the scheme of data sharing based on blockchain has been becoming more and more mature, and it has the features of decentralized, secure and tamper-resistant to address the problem of data security in the process of data sharing, thereby improving the quality of medical service of citizens, reducing medical cost and cutting down medical risk. Obviously, information technology is not the main factor to hinder data sharing between medical institutions. Actually, the low enthusiasm of medical institutions was due to lack a comprehensive incentive mechanism of medical data sharing. Combining the medical field, this paper proposes an incentive mechanism of data sharing based on information entropy, which can effectively promote more medical institutions to participate in data sharing and enhance the enthusiasm of medical data sharing. Analyses show that the approach is efficient and effective.
Multi-layer Network Attack and Defense Model Based on Entropy Method to Measure Network Security
Authors: Zhao Xiaolin , Guo Jiong , Jiang Xiaoyi , Zhao Jingjing , Xu Hao
Published: September 2019

Network security is also facing major challenges. On the one hand, network security assessment can dynamically reflect the state of network security and reveal potential threats. On the other hand, network security assessment is the basis of network security management, it is also the prerequisite for building a reliable and secure network. This paper proposes a multi-level network security measurement model, which divides the network into a network environment layer and a network attack and defense layer. The network environment layer considers the network hardware security and the reliability of a network topology. The network attack and defense layer mainly examines the security status of the network from the perspective of network attack and defense. The model comprehensively evaluates and measures the network from different layers of the network, and conducts experiments. The result of experiment proves that the model can effectively assess the security of the network.
Channel Pruning for Efficient Convolution Neural Networks
Authors: Wang Wei , Zhu Liqiang
Published: September 2019

In this paper, we propose a novel pruning method using feature maps Ln-norm to compress deep convolutional neural networks (CNNs) with large model size and long inference time. Different from existing pruning methods, which are mainly based on convolution kernels L1-norm, the proposed method uses output feature maps Ln-norm, where n is a variable. This method can accurately identify the redundant convolution filters and has good compression performance. The experiments on ImageNet show that the proposed method outperforms previous methods and is generally suitable for any CNNs models.
A Novel Strategy for Short-Term Prediction of Fading Channel
Authors: Sui Yongbo , He Yigang , Shi Luqiang , Huang Yuang , Cheng Tongtong , Wu Yuting
Published: September 2019

In this paper, a novel prediction strategy based on firefly algorithm, echo state network and Savitzky-Golay filter for short-term prediction of fading channel is proposed and introduced in detail. This hybrid strategy includes two parts, that is, optimization part and repairing part. In the former, parameters of echo state network are optimized by firefly algorithm so that an optimal prediction echo state network is obtained, then in order to improve performances in prediction strategy, prediction sample of echo state network is repaired by repairing strategy based on Savitzky-Golay filter. Finally, the classic Rayleigh channel is tested and performances are analysed and compared with classic AR, which verified the validity and correctness of our proposed prediction strategy.
Moving Vehicle Detection with Shadow Elimination Based on Improved ViBe Algorithm
Authors: Xia Xue , Lu Xiaobo , Cao Yichao , Xia Shudong , Fu Changqing
Published: September 2019

Moving vehicle detection based on video processing has been widely used in intelligent transportation system recently. However there are also many problems, such as dynamic background, ghost region, and shadow of moving objects. This paper proposes an improved ViBe object detection algorithm. First, an accurate background image is obtained by using the multi-frame averaging method, and then the background model is initialized by this accurate background image, thus effectively reducing the generation of ghost region. Whenever there is no moving object for a fixed number of consecutive frames in the video, this frame is updated to the background image. Conservative update strategy and foreground point counting method are adopted to update the background and reduce the impact of dynamic background on the foreground detection. Next, the foreground image detected by improved ViBe algorithm is input into the shadow elimination method proposed in this paper. Shadows in foreground pixels are detected in RGB color space, and then the pixels determined as shadows are eliminated. Finally, accurate moving vehicles are obtained. Our algorithm can effectively eliminate the shadows of moving vehicles, quickly adapt to background changes and illumination mutation, and get accurate moving objects, which is helpful for vehicle contour extraction and subsequent image processing.
Analytic Model and Assessment Framework for Data Quality Evaluation in State Grid
Authors: Li Zhe , Wu Sai , Zhou Hongwei , Zou Sheng , Dong Tingting
Published: September 2019

With the construction of information infrastructure and the smart grid development, big data platform become an important component to support the construction of smart grid, as a consequence, the quality of the data contain in the big data platform become an important part of the process of big data platform construction. In this paper, a discussion about the current data quality analysis model was presented and also the typical data quality problem appears in the process of information infrastructure construction of the State Grid Corporation is analyzed. On the basis of this, the data quality assessment for the information system in the whole life cycle of the State Grid Corporation is designed. As a result, data quality can be checked by the analysis model in order to ensure the data quality level in the whole life cycle of the information system, finally a case was carried out for the preliminary analysis by using the mentioned framework, the result show that this framework can be effective in embedded into the workflow and can be helpful for the data quality assessment.
A Fraudulent Transactions Simulation Method Based on Genetic Algorithm
Authors: Wang Xiaoguo , Li Yuanxiu , Zhao Ran
Published: September 2019

When using machine learning or other methods to construct the fraud detection models, the banking industry faces such problems: the number of fraud transactions data is too small, which affect the training of anti-fraud model and the detection effect of fraud transaction. This paper proposed a data simulation algorithm based on genetic algorithm (GA-DS). By studying the feature of real fraudulent transactions, we designed the crossover mutation and other genetic operators, explored the suitable fitness function that can evaluate the quality of simulated data, and generated simulated data satisfying the characteristics of the original transaction. The experiment result shows that mixing the simulated data and the original data into the training can improve the detection ability of anti-fraud model.
A Refined Classification Method for Transformer Fault Diagnosis
Authors: Luan Le , Mo Wenxiong , Wang Hongbin , Kong Lingming , Zhou Kai
Published: September 2019

Dissolved gas analysis is one of the most effective methods for diagnosing transformer faults. The traditional method for oil-immersed transformer fault diagnosis can only recognize several types of defects and has a low accuracy rate. In order to improve the classification effectiveness, a refined classification method for transformer fault diagnosis is proposed. It can detect more types of faults with a higher accuracy rate. The proposed method is based on the probability-output relevance vector machine, and a three-layer four-classifier model is constructed to analyse the different diagnostic results of different kinds of input data. In this model, a binomial tree is used to transfer the multi-classification problem to four binary classification problems; each classifier is a binary classifier used to distinguish the transformer type between two types of error. The proposed method is employed for analysis of 100 DGA samples consisting of characteristic gas content. The experimental result shows that this method has a high diagnostic rate and can diagnose 11 kinds of operation state.
Application Research of Public Participation Platform for Urban Planning Management Based on Panoramic Technology
Authors: Dan Chenglong , Gong Junhong , Chen Xiyue , Jiang Daiyin
Published: September 2019

The creation of a public participation platform for urban planning based on panoramic technology by combining CSS and THREEJS on the Web end provides an effective channel for the public to participate in urban planning. Panoramic technology is a new type of streaming media, it highly restores the planned content through the virtual simulation, panoramic technology could deepen the public understanding of the content of the plan, it also reduced the learning cost of public participation in planning due to the professionalism of planning. Through the mongo DB-based database, the planning and comment data can be effectively managed for the auxiliary decision-making of urban planning, and the democratic and scientific nature of urban planning can be improved.
Improved TF-IDF for We Media Article Keywords Extraction
Authors: Guan Xinxin , Li Yeli , Gong Hechen
Published: September 2019

Keyword extraction is one of the work of computer text topic mining, and it is also the basis of text analysis and public opinion analysis. The keywords extracted by the traditional TF-IDF algorithm are mainly calculated based on the word frequency. The importance of other feature words with fewer occurrences and the comments of readers below the article are not considered. Aiming at the above problems, this paper improves the traditional TF-IDF algorithm, adds the part of speech and the reader’s comment as the impact factor, and recalculates the weight of TF-IDF, so that the accuracy of the algorithm is improved. This paper uses the Python language programming to crawl from the media article and implement the improvement of the algorithm. Experiments show that the improved TF-IDF algorithm has significantly improved compared with the traditional TF-IDF, in terms of accuracy, recall rate, F1, MacAvg_P, MacAvg_R and MacAvg_F1.
Robot Indoor Text Contents Recognition Based on Visual SLAM
Authors: Hu Lan
Published: September 2019

Although research on indoor mobile robots has evolved over the years and has made good progress in many areas, few researchers have noticed their combination in text/character recognition and navigation applications. In fact, robots navigate and move with obstacles and various visual disturbances without prior knowledge, and people and objects are possible obstacles. Indoor robots may need to recognize text/characters to help them determine their location and move on. In order to enable mobile robots to navigate and locate more accurately, we improved the visual text content recognition system based on Faster RCNN. This method uses the attention mechanism to replace the original re-sharp method in Faster RCNN, and the visual attention mechanism can be adopted for the anchor. Reduce the number and error, and reduce the interference of non-target information on the identification system. This method can reduce the problem of large system consumption time. Experiments on the indoor robot dataset show that the method has certain advantages in accuracy and cost time.
A Method of Cylindrical Panoramic Video Stitching
Authors: Chen Jie , Li Xiaoyu , Yu Xiao
Published: September 2019

In this paper, we propose a cylindrical stitching method which based on template frame image to realize 360° panoramic video stitching. Firstly, we selected template frame to determine the camera parameters and distortion coefficient. And, removing the image distortion. After that, extracting the template frame feature and making rough matching with the feature points, and then eliminating the feature points of mismatching by the improved feature point screening method. Then, feature points are projected on the cylinder in advance for subsequent video image fusion. Finally, image fusion is carried out after the cylinder projection of the video image. The speed was greatly improved in the subsequent video splicing by using camera parameters, distortion coefficient and transformation matrix of template frame image. In addition, the CUDA platform was adopted for GPU parallel processing in order to further improve the speed to meet the real-time requirements. Finally, we realize real-time video stitching with 30 frames per second.
A Deep Clustering Algorithm based on Gaussian Mixture Model
Authors: Lin Xianghong , Yang Xiaofei , Li Ying
Published: September 2019

Clustering autonomously learns the implicit cluster structure in the original data without prior knowledge. The effect of ordinary clustering algorithms is not good to cluster high-dimensional data. In this paper, we propose a deep clustering algorithm based on Gaussian mixture model, which combines two models of stacked auto-encoder and Gaussian mixture model. This algorithm uses the expectation maximization algorithm of reducing dimension data feature to train Gaussian mixture and updates the data cluster so that the data is clustered in the feature space. The experimental results demonstrate that the proposed algorithm improves the clustering accuracy, and verifies the effectiveness of the algorithm.
Multi-scale Feature Mapping Method Based on Clustering Convolution
Authors: Jiang Yun , Ren Junping , Xiao Jize , Zhang Hai , Chen Xiufang
Published: September 2019

In recent years, convolutional neural networks have attracted much attention due to their powerful image feature learning capabilities. However, in many cases, the network exhibits shortcomings such as difficulty in training and adjustment, and time-consuming training. This paper combines clustering and constrained Boltzmann machines to propose a multi-scale feature mapping method based on clustering convolution. Experiments were carried out on Cifar-10 and lung X-ray images. This method not only shortened the time but also improved the accuracy. Therefore, the proposed method is superior to the traditional feature extraction method.
Algebraic Fundamentals in Artificial Intelligence for the Purpose of Undergraduate Education and Training
Authors: Zhang Silan , Xia Jingbo
Published: September 2019

College algebra syllabus setting is of utmost importance for the higher education developments and students education and training, especially for the newly opened bachelor majors. In this research, we investigated the traditional syllabus of college algebra in a nationwide manner, and discussed the algebraic fundamentals in artificial intelligence. Three case studies were proposed to address the fundamentals of abstract logic thinking, data observation and knowledge inference, and data-driven thinking. Eventually, a suggested syllabus for “Advanced Algebra and its Applications in AI” was introduced subsequently for the new major of “artificial intelligence”.
An Improved K2 Algorithm for Symptom-Disease Bayesian Network
Authors: Yu Ying , Yang Wenwen , Huang Hao , Yang Zheng , Lu Jianye
Published: September 2019

This paper establishes a Bayesian network for pre-diagnosis based on symptom-disease knowledge from clinical data. In order to solve the local optimal problem, the idea of simulated annealing algorithm is introduced into an improved K2 algorithm, and the BDE scoring function is used to determine the optimal network structure of the Bayesian network. performance evaluation indexes are used to evaluate the performance of the network. The experimental results show that the improved K2 algorithm outperformed the K2 algorithm.
Research on Performance Evaluation of Wargame System Based on Deep Reinforcement Learning
Authors: Shang Tongfei , Wu Jinyun , Ma Jianfeng
Published: September 2019

Deep reinforcement learning combines the advantages of deep learning and reinforcement learning to make end-to-end perception decisions in complex high-dimensional state action spaces. The paper proposes a deep reinforcement learning method for the deduction of the wargame system, which can better assist the combat commander in wartime decision-making. The simulation proves the effectiveness of the method.
A Fingerprint Image Enhancement Algorithm Based on Stretching Transfer Function
Authors: Zhou Jun , Wang Chao , Wang Fang , Ma Jinhai , Yang Mei
Published: September 2019

In this paper, a fingerprint image enhancement algorithm based on the stretching transfer function is proposed. The Otsu method is used to generate the threshold, which divides the fingerprint image grayscale roughly into two parts. The width of grayscale distribution can be controlled by adjusting the parameter of the stretching transfer function. The contrast of the input fingerprint image will be enhanced by the function, highlight image layers and make the ridge and valley lines clearer. The function could also retain the fingerprint image information in transformation process, thus lays a solid foundation for the accuracy and reliability of the fingerprint characteristic value extraction in the next step. The treatment effect of the algorithm is obviously better than that of histogram equalization algorithm; the superiority also lies in the smaller amount of calculation and the faster processing speed which effectively avoids manual intervention. It can be used for the occasion with limited computing resources and high real-time requirements. Besides, the ability to control the contrast of output image by only changing a single parameter provides users with regulations and choices in biometric.
Study on Influences of Artificial Intelligence Era on Early Childhood Family Education in China
Authors: Jin Ling
Published: September 2019

The artificial intelligence era will have important influences on the future society. This paper studies the influences of the artificial intelligence era on the early childhood family education in China. First, the problems in the early childhood family education in China are investigated, then the influences and opportunities of the artificial intelligence era on early childhood family education are analyzed, and finally some applications of artificial intelligence in the early childhood family education are discussed. The study shows that the artificial intelligence era has the potential to provide better family education for children.
Transferring Colorization with Smaller Samples
Authors: zhao Yangze , You Fucheng
Published: September 2019

This paper applies transferring learning to colorization and the main body is based on DNN. However, considering the less complexity of the data set and the equipment constraints. We extract global features from vgg pre-trained model instead of Inception-resnet-v2. And we initialize the other parts weights by fix vgg weights. The result shows that we use less training time and sample and get comparable performance.
Modeling and Optimization of Heterogeneous Vehicle Routing Problem in Emergency Resource Distribution after Disaster
Authors: Zhou Haibin
Published: September 2019

In recent years, natural disasters have occurred frequently around the world. There are many kinds of disasters in China, which are diversified in form and widely distributed. How to reduce the damage caused by disasters to humans is one of our urgent tasks. Firstly, a heterogeneous vehicle routing optimization model is constructed, and a genetic algorithm is designed to solve the model. Secondly, the optimal path of heterogeneous vehicle resource transportation is solved by using examples to verify the effectiveness of the model and algorithm. Finally, this paper puts forward the prospect of research on the problem of heterogeneous vehicle routing for emergency resource transportation after disasters.
Monthly Runoff Prediction Using Wavelet Transform and Fast Resource Optimization Network (Fron) Algorithm
Authors: Lu Jiahai , Li Aiyun
Published: September 2019

In this study, Discrete Wavelet Transform (DWT) and a Fast Resource Optimization Network (FRON) Algorithm were integrated to forecast monthly runoff and improve the accuracy of forecasting models. The original monthly runoff sequence is reconstructed by DWT processing into two components of deterministic and stochastic components, then these two components were inputted into two different FRON model respectively, and finally the prediction results of the two models were summed up as the final forecasts of monthly runoff. The WFRON1 model is compared with the WFRON2 model and the FRON model without discrete wavelet transform. The mean absolute error (MAE), the deterministic coefficient (DC) and the correlation coefficient (R) are used as the model evaluation index. The model is applied to the prediction of monthly runoff at the Pantuo hydrological station in Shangyuan River. The results show that: The evaluation index of WFRON2 model is better than WFRON1, and both models predict the effect better than FRON model.
A Study on the Development of Small Tourist Towns under the “Internet Plus” Background
Authors: Lin Qian , Wei Wei
Published: September 2019

With the development of the times, people’s pursuit of personalized and unique tourism has also spawned the product of a small tourist town. The combination of the Internet and the small tourist towns is a collision of the new era, the tourism industry is actively seeking market positioning, and reshaping the competitive advantage and realizing the transformation and development in the Internet era. The article mainly studies the current situation and countermeasures of the development of small tourist towns under the background of “Internet Plus”. Firstly, it introduces the status quo of the construction of small tourist towns, and then analyses the main problems in the development of the small tourist towns, and from the “Internet Plus” perspective of these problems, the corresponding solutions are proposed. It found that the Internet would become the main driving force and support for the innovation and development of tourism.
The Smart Elderly Care Service in China in the Age of Big Data
Authors: Zhou Guangtao
Published: September 2019

China is just experiencing a period of population aging accelerating. With the approaching of an aging society, elderly care becomes increasingly challenging; however, a variety of disadvantages has been exposed gradually, including unsatisfactory health care, unstable daily care, insufficient emotional comfort, no timely emergency aids, and so forth. The traditional elderly care model has already impeded, to a large extent, the sustainable development of elderly care. As the rapid development and wide application of big data, smart elderly care comes into being, which shows various advantages while comparing with traditional elderly care services. The Chinese government has the responsibility to implement the smart elderly service in its initial stage of development.
High-speed Railway Fastener Detection Using Minima Significant Region and Local Binary Patterns
Authors: Fan Hong , Xiong Ying , Fei Yaodong
Published: September 2019

Railway fastener is an important part of the railway system. Keeping the fasteners effective is essential to ensure the safe operation of the railway, so abnormal railway fastener detection is a main task of railway maintenance. With the development of railway system, the traditional manual fastener detection method has been unable to meet the application requirements, because it is very slow, costly, and dangerous. In this paper, we propose a novel method for abnormal fastener detection based on computer vision, which can detect missing fasteners automatically. In this method, Minima Significant Region is extracted in order to improve the fastener localization accuracy. Then, fastener recognition is operated using local binary features and Support Vector Machine classifier based on the fastener sub-images which are obtained by fastener localization. The proposed method is evaluated in our own database which is obtained by railway inspection system in different environments. The experimental results have shown improved performance against the state-of-the-art algorithm.
The Research on Application of Virtual Reality Technology in Museums
Authors: Wang Bo , Liu Yue
Published: September 2019

The advantages of virtual reality technology become more and more obvious since the VR comes out. Il is also widely used in different social fields, such as the field of architectural design, aerospace, entertainment and so on. As well, in the field of museums, the VR technology also shows it specific advantages and is widely applied. The article mainly analyzes and studies the application of VR virtual reality technology in museums, and proposes the strategies to improve the application of VR technology used in museums.
Short-term Power Prediction of Wind Farm Power Based on BP Neural Network
Authors: Ren Yaming
Published: September 2019

With the proportion of wind power in the power systems increasing, consideration must be given to the fact that the randomness and volatility of wind power output will inevitably affect the stable operation of power grid. One of the effective ways to solve this problem is to forecast the output of wind power. In this paper, we employ the method of BP neural network to predict the wind power output in a period of time. To discuss the predictive performance of BP neural networks, we set different number of input variables to observe the prediction effect of BP neural network. We find that it’s not that the more input information, the better the prediction effect. The data with strong correlation can be used as input to achieve better results.
Extension Method in Case-Based Reasoning and its Application in Artillery System Design
Authors: Zhao Shubin , Xu Cheng
Published: September 2019

Case-Based Reasoning (CBR) is a method of designing new product configurations based on previous design case matching. Over the last couple of decades, CBR has grown from a relatively isolated research area to a comprehensive and multi-field one. Extenics is one of fields combined with it. The representation model and extension distance theory in Extenics are surveyed. Based on dependence function, an improved comprehensive evaluation of configuration schemes of a complex modularized product is proposed, considering the interface matching problem that may occur between different components. To illustrate the validity and feasibility of our method, a design case of artillery system is described. The result shows that our evaluation method can better adapt to engineering practice and provide effective and reasonable matching results.
Research on WeChat Attendance Technology Based on Geographical Location and iBeacon
Authors: Zhao Jiangsheng , Huang Xi , Wang Beizhan , Xu Zhibin
Published: September 2019

In the society today there are many occasions that involve attendance or sign-ins, and applications and technologies are emerging one after another. This paper focuses on the geographical location-based and iBeacon-based micro-attendance technology, and gives a concrete implementation of the two. This paper also improves the micro-attendance technology based on iBeacon. While using iBeacon positioning service, we overcome the shortcomings of “Shake” attendance and greatly optimize the user experience.