bluuebunny/crossref_metadata_2025 huggingface dataset:
Single Crystal Growth and Magnetoresistivity of Topological Semimetal CoSi*
Authors: Wu D. S. , Mi Z. Y. , Li Y. J. , Wu W. , Li P. L. , Song Y. T. , Liu G. T. , Li G. , Luo J. L.
Published: July 2019

We report single crystal growth of CoSi, which has recently been recognized as a new type of topological semimetal hosting fourfold and sixfold degenerate nodes. The Shubnikov–de Haas quantum oscillation (QO) is observed on our crystals. There are two frequencies originating from almost isotropic bulk electron Fermi surfaces, in accordance with band structure calculations. The effective mass, scattering rate, and QO phase difference of the two frequencies are extracted and discussed.
Pressure-Induced Ionic-Electronic Transition in BiVO4*
Authors: Lyu Shu-Peng , Wang Jia , Zhang Guo-Zhao , Wang Yu-Fei , Wang Min , Liu Cai-Long , Gao Chun-Xiao , Han Yong-Hao
Published: July 2019

Electrical transport properties of bismuth vanadate (BiVO
4
) are studied under high pressures with electrochemical impedance spectroscopy. A pressure-induced ionic-electronic transition is found in BiVO
4. Below 3.0 GPa, BiVO
4
has ionic conduction behavior. The ionic resistance decreases under high pressures due to the increasing migration rate of O
2−
ions. Above 3.0 GPa the channels for ion migration are closed. Transport mechanism changes from the ionic to the electronic behavior. First-principles calculations show that bandgap width narrows under high pressures, causing the continuous decrease of electrical resistance of BiVO
4.
Van der Waals Epitaxy of Anatase TiO2 on mica and Its Application as Buffer Layer*
Authors: Xu Han , Luo Zhen-Lin , Zeng Chang-Gan , Gao Chen
Published: July 2019

MICAtronics, based on the functional oxide/mica heterostructures, has recently attracted much attention due to its potential applications in transparent, flexible electronics and devices. However, the weak van der Waals interaction decreases the tolerable lattice mismatch and thus limits the species of function oxides that are able to be epitaxially grown on mica. We successfully fabricate relatively high-quality epitaxial anatase TiO
2
thin films on mica substrates. Structural analyses reveal that the carefully chosen growth temperature (650°C) and suitable crystalline phase (anatase phase) of TiO
2
are the key issues for this van der Waals epitaxy. Moreover, as a buffer layer, the TiO
2
layer successfully suppresses the decomposition of BiFeO
3
and the difficulty of epitaxial growth of BiFeO
3
is decreased. Therefore, relatively high-quality anatase TiO2 is proved to be an effective buffer layer for fabricating more functional oxides on mica.
Parametric resonances and resonant delocalisation in quasi-phase matched photon-pair generation and quantum frequency conversion
Authors: Main Philip B , Mosley Peter J , Gorbach Andrey V
Published: July 2019

The existing widely-accepted theory of photon-pair generation via spontaneous down-conversion (SPDC) in nonlinear optical crystals and waveguides is incomplete, as it fails to account for the important physical phenomenon of parametric resonances. We demonstrate that exponential gain of classical fields in the regime of parametric resonance corresponds to resonant delocalisation in the Glauber–Fock model of quantum SPDC. We propose a quantitative measure of localisation of Floquet eigen-modes as an analogue of classical gain to identify regimes of resonant delocalisation. Using this method, we are able to reconstruct the classical ‘Arnold tongues’ map of domains of instabilities for SPDC. We also predict novel regimes of resonant delocalisation in the two-level model describing quantum frequency conversion processes.
Research on Sentiment Analysis of Chinese E-Commerce Comments Based on Deep Learning
Authors: Zhou Hanchen , Wu Gang
Published: July 2019

The analysis of emotional polarity of Chinese phrases in complex contexts has always been a problem in the field of machine learning. The emergence of Artificial Neural Networks has opened a window to solve this problem. Sentiment analysis has also become a hotspot in the research. This paper aims to solve this problem by introducing Attention mechanism to the Temporal Convolutional Nets (TCN) model. This paper chooses the Chinese commodity comment phrase on the E-commerce platform as the research object, to automatically analyse and judge the polarity (positive and negative) of each comment phrase. This paper optimized the design and constructed of the TCN Attention model, which made the TCN Attention model show superior performance in the area of sentiment analysis, and improved significantly compared with other models.
Improved Non-local Means Algorithm for Image Denosing
Authors: Li Menglong , Xu Yi
Published: July 2019

In order to solve the problem that Non-local means method (NLM) results in a weaker retention and noise immunity, an improved non-local means (INLM) is proposed. This paper introduces bilateral structure tensor combined with the spatial distance and the gradient distance to weight the structure tensor matrix. It has stronger noise immunity and can extract the texture structure more accurately. Mahalanobis distance is applied as a measure of the similarity of image pixels to suppress noise interference. Analyses and simulation results prove the validity of the proposed algorithm.
Two-person cooperative games on minimizing the makespan
Authors: Liu Lili , Gu Yanhong , Ren Han , Tang Guochun
Published: July 2019

This paper considers the problem of two-person cooperative games on minimizing the makespan. A special case of this problem is proved NP-hard, and a dynamic programming algorithm is presented for the general case, which runs in pseudo-polynomial time and indicates that this problem is binary NP-hard. This paper also adopts List Scheduling (LS) and Longest Processing Time (LPT) algorithms for classical parallel machines scheduling problem to cooperative game problem and develops the performance ratios of the algorithms.
Research on Path Planning Method of Humanoid Robot Based on Improved Genetic Algorithm
Authors: Zhan Xiaoyi
Published: July 2019

Mobile robot is a comprehensive subject developed rapidly in recent years. In the near future, robots may appear in many aspects of your life. Robots can replace humans to complete many tasks, among which it is very critical to accurately reach the destination, so the path planning of mobile robots has become a research hotspot in related fields.In this paper, the path planning technology of mobile robot based on genetic algorithm is studied. Based on the traditional method, some improved algorithms and new solutions are proposed to improve the computational efficiency of the algorithm and search an optimal path between the starting point and the target point.
Auto-Scaling Cloud Resources using LSTM and Reinforcement Learning to Guarantee Service-Level Agreements and Reduce Resource Costs
Authors: Zhong Jiang , Duan Saisai , Li Qing
Published: July 2019

Auto-Scaling cloud resources aim at responding to application demands by automatically scaling the compute resources at runtime to guarantee service-level agreements (SLAs) and reduce resource costs. Existing approaches often resort to predefined sets of rules to add/remove resources depending on the application usage. However, optimal adaptation rules are difficult to devise and generalize. A proactive approach is proposed to perform auto-scaling cloud resources in response to dynamic traffic changes. This paper applies Long Short-Term Memory (LSTM) to predicting the accurate number of requests in the next time and applies Reinforcement Learning (RL) to obtaining the optimal action to scale in or scale out virtual machines. To validate the proposal, experiments under two real-world workload traces are conducted, and the results show that the approach can ensure virtual machines to work steadily and can reduce SLA violations by up to 10%-30% compared with other approaches.
Application of Data Encryption Technology in Computer Network Security
Authors: Chu Wei
Published: July 2019

With the development of information technology, the problem of computer network security is becoming more and more prominent. Illegal invasion, information disclosure, virus impact and network vulnerability are all factors that affect computer network security. In order to effectively guarantee the integrity and confidentiality of data and information, people must pay attention to computer network security.
Research and Countermeasure of Network Education Teaching Based on Online Learning Behavior Data Analysis
Authors: Yang Shi
Published: July 2019

Compared with traditional teaching mode, network teaching has advantages, but its defects are obvious. This paper selected some samples and analyzed them according to certain standards, pointed out the shortcomings of modern network education teaching, and finally put forward some suggestions and measures from the aspects of education mode, resource integration, system norms and so on.
Research on the Application of Microservice Architecture in Administrative Law Enforcement Supervision System
Authors: Wei Fan , Zhang Qian
Published: July 2019

Traditional WEB system costs a lot of resources but takes less effect. Thus, this problem should be solved. In this paper, we take administrative law enforcement supervision system as an example to show why microservice architecture is a good solution to the above problem. And evaluation proved that microservice architecture is more effective.
Arrhythmia classification based on multi-domain feature extraction
Authors: Yin Li , Chen Fumin , Zhang Qi , Ma Xu
Published: July 2019

Arrhythmia is a common abnormality of cardiac electrical activity. Arrhythmia classification has enormous significance for the clinical diagnosis of cardiovascular diseases. In this study, a method of multi-domain electrocardiogram feature extraction was put forward as to classify arrhythmia precisely. The RR intervals were extracted as time domain feature. The fifth level approximation coefficients of wavelet decomposition were adopted to represent frequency domain feature. Besides, the sample entropy values of six wavelet coefficients were employed as nonlinear feature. These three features were fed to classifier for automated diagnosis. Furthermore, ten-fold cross-validation scheme was adopted to train and test classifier whose parameters were optimized by genetic algorithm. In this study, eight classes of the most frequently occurring arrhythmia from MIT-BIH arrhythmia database were validated. The result turned out that the SVM classifier yields an average accuracy of 99.70%. Compared with the existing methods, the proposed method shows better results.
An Azimuth Frequency Domain NCS Algorithm for Missile-borne SAR
Authors: Zhang Yan , Shi Xiaomin , Zhang Mingxuan
Published: July 2019

SAR data in highly squint mode features large range walk and small range curvature. Therefore, an advanced Frequency Phase Filtering Algorithm based on line range walk correction is proposed in this paper. Firstly, range walk is removed in range frequency and azimuth time domain. Then, a chirp scaling (CS) algorithm is adopted to correct range curvature. Based on the above operation, a novel high-order of phase filtering factor is introduced into the azimuth time and frequency domain in order to correct the azimuth-dependence. Finally, the signal is focused in the Doppler domain by SPECtral Analysis technique. Simulation results illustrate that it satisfies the imaging quality of SAR in high squint mode and large scene swath.
Application Research of Evolutionary Algorithm in Synthesis of Reversible Logic Circuits
Authors: Han Jiaxin , Zhang Xin , Wang Xiaoxiao
Published: July 2019

In order to avoid the algorithm falling into premature convergence, a big mutation adaptive evolutionary algorithm for reversible logic circuit synthesis with tabu list is proposed. Big Mutation Variable Length Evolutionary Algorithm Reversible Logic Circuit (BM_VLEA_RLC) is based on Variable-Length Evolutionary Algorithm Reversible Logic Circuit (VLEA_RLC) algorithm that designs the corresponding fitness function, adaptive evolution probability, etc., and tests using benchmark functions. The experimental results show that the proposed algorithm can obtain several feasible solutions, and some solutions are better than the existing algorithms, which verifies the feasibility of the algorithm.
Propagation characteristics of underwater plasma pulse sound source
Authors: Chen Zhenyang , Yan Bing , Zhang Xiaobing
Published: July 2019

The signal propagation characteristics of underwater plasma pulsed sound source have important guiding significance for the research and application of underwater directional radiation technology. In this thesis, the model propagation method is used to analyze the signal propagation law of underwater plasma pulse source with different depths. Firstly, the propagation model of underwater plasma sound source is established. Secondly, the distribution characteristics of underwater sound field at different depths are simulated. The research results show that the simulation results can express the underwater sound field characteristics at different depths, which proves the effectiveness of the simulation model, and lays a foundation for further research on underwater pulse directional radiation technology and improving the underwater application of pulse sound source.
RETRACTED: Research on Network Information Security and Privacy Protection in the Age of Big Data
Authors: Gao Zhang
Published: July 2019

The rapid development of big data has brought more problems in network information security and privacy protection. This paper will study the specific network information security and privacy protection technologies in this context. The first part of this paper firstly discusses the connotation and characteristics of big data, and then puts forward the information security and privacy protection problems in the era of big data. The last part deeply studies how to solve the solutions corresponding to these problems.
Predict Consumers’ Automobile Purchase Behavior Based on the Preferences of Explicit Features and Implicit Topic
Authors: Yanan Du , Yang Qian
Published: July 2019

In order to know the role of online reviews in predicting consumers’ automobile purchasing behavior in the social network environment, this paper uses online reviews to construct a consumer interest preference model to obtain consumers’ explicit feature preferences and implicit topic preferences. Based on the preferences of consumers, introduce the tree structure to construct hierarchical prediction model to predict consumers’ purchase behavior. Meanwhile, consider the effect of online interactive comment on consumers’ automobile purchases. Several results are obtained in this paper:(1) The proposed hierarchical model introducing more information could achieve multi-granularity purchase-decision predicting;(2) the predictive effect based on implicit topic preference is better than that based on explicit feature preference; (3) integrating online interactive comments have positive effect in analyzing and predicting consumers’ purchasing behavior.
A Blind Recognition Algorithm for Digital Amplitude Modulated Signals
Authors: Li Zhengdong , Yang Cui , Wu Bin
Published: July 2019

A blind recognition Algorithm of amplitude modulation-ASK and QAM signals is proposed in this paper. Without requiring any prior information of the accepted signal, this algorithm employs the instantaneous amplitude of the incoming signal to recognize the modulation order, i.e., M, using the clustering centers of the amplitude. It is found that this new algorithm is practical and steady in recognition amplitude modulation signal in the presence of AWGN.
Process Security Sequence Improvement Algorithm Based on Banker Algorithm
Authors: Jiang Li
Published: July 2019

By analyzing the core idea of the banker’s algorithm and the essential meaning of the security state, an algorithm for applying multiple resources at a certain time in the system is proposed. The algorithm is implemented using object-oriented programming language C++. By analyzing security sequences and processes, the number of application resources can provide support for system resource allocation and process scheduling optimization. This algorithm can also be used as an improved implementation algorithm for deadlock detection algorithms or banker algorithms.
Exploration and Application of SPOC-based Belended Teaching Mode in Comprehensive English Course
Authors: Chen Jun
Published: July 2019

As the new direction of education reform in the “Internet +” epoch, the SPOC-based blended teaching mode can enrich the teaching content and meet the learning needs. Based on the SPOC teaching concept, this paper studies the advantages of the blended teaching mode and explores its application in the teaching of Comprehensive English course for English majors. Through teaching practice, it is found that the SPOC-based blended teaching mode can improve students’ enthusiasm for learning English, enhance their learning interaction, and finally optimize the effectiveness of English teaching. In view of this, through the study of the blended teaching mode in English teaching practice, this paper aims to provide useful enlightenment for the future implementation of SPOC-based blended teaching mode.
Application of Improved Decision Tree C4.5 Algorithms in the Judgment of Diabetes Diagnostic Effectiveness
Authors: Liu Jiawei , Ning Bo , Shi Daosheng
Published: July 2019

C4.5 algorithm needs to scan data sets repeatedly when constructing decision tree, which leads to its inefficient operation. A C4.5 decision tree optimization algorithm based on data dimension reduction is proposed, which generates a more concise decision tree and improves the efficiency of the algorithm. At the same time, it eliminates the interference of redundant attributes and improves the accuracy of the algorithm prediction.The simulation results of the improved C4.5 algorithm and the traditional algorithm show that the accuracy and efficiency of the improved C4.5 algorithm are greatly improved.This algorithm is applied to the prediction system of diabetes diagnosis effect, which provides decision support for the diagnosis of diabetes in hospitals, realizes better precise medical treatment, and makes better use of social medical resources.Successful application of diabetes diagnosis and treatment results also provides technical ideas for large data decision-making in other fields.
The Research on Internet Financial Flow Risk Measurement and Statistical Based on Analytic Hierarchy Process
Authors: Li Shuyi
Published: July 2019

As a decision-making method of qualitative and quantitative analysis, AHP has the advantages of flexibility, system, simplicity and convenience. It has important instrumental significance in the assessment of Internet financial liquidity risk. This paper adopts the concept and formation of Internet financial liquidity risk. The reason is discussed. On this basis, the analytic hierarchy process tool is introduced. The model is used as the carrier to measure and count the Internet financial risk, so as to provide a useful reference for the prevention and control of Internet financial flow risk.
Short-term and Fast Tracking Algorithm of Vehicle with Distance Information
Authors: Qi Shuaihui , Li Zhiyuan , Ma Jianfeng , Zhang Qinghui , Xu Shasha
Published: July 2019

To solve the contradiction between speed and performance in vehicle tracking, this paper designs a short-term and fast tracking algorithm which integrates vehicle distance information. Millimeter wave radar is used to get accurate range information to optimize the scale of vehicle, so as to achieve fast and accurate vehicle tracking. The effectiveness of the algorithm in scale optimization is proved by experiments.
Research and analysis of threshold segmentation algorithms in image processing
Authors: Niu Zuodong , Li Handong
Published: July 2019

Image segmentation is one of the most difficult and important tasks in digital image processing. The accuracy and effect of segmentation determine the final success or failure of the calculation and analysis process. Therefore, analysis and improvement on basic image segmentation algorithms should be paid attention to in a quite wide range of applications. This paper mainly introduces and studies the principle and characteristics of threshold segmentation algorithm, and analyses the application scenarios of global threshold segmentation and adaptive local threshold segmentation related algorithms, which has certain reference significance for digital image processing related research.
Business Anomaly Detection Based on QoS Benchmark of Resource-service Chain in Collaborative Task
Authors: Li Haibo , Tong Juncheng , Zhang Zheng , Yu Yongbo
Published: July 2019

To detect business anomaly caused by resource services in collaborative task, an approach based on QoS benchmark of resource-service chain is presented. Firstly, taking QoS of resource services as feature, the QoS benchmarks are obtained by clustering the relationships between the features of resource services in resource service chains. Then, thresholds of QoS benchmarks are calculated. A dynamic calibration strategy to the threshold is used to perform online detection of business anomaly in global and local views of a collaborative task. Finally, experiments and case studies show that the proposed approach has a high accuracy rate for business anomaly detection.
Research on network load balancing method based on simulated annealing algorithm and genetic algorithm
Authors: Yingzhuo Xu , Qing Yang Geng
Published: July 2019

Due to the high burstiness and high real-time performance of modern network load, it is difficult to meet the requirements of purely simulated annealing algorithm or genetic algorithm for network resource utilization and flow control problems, resulting in low network resource utilization. Congestion is serious. In order to make network load balancing more reasonable, a method of combining simulated annealing algorithm with genetic algorithm is proposed. Firstly, the genetic algorithm is used to globally search the network load balancing problem, so that the solution of the problem is quickly in the vicinity of the global optimal region. Then the simulated annealing algorithm is used to further local optimization near the global optimal region, and the optimal solution for the network load balancing problem is found. The simulation results show that the method improves the global search speed, improves the network resource utilization, and significantly improves the network load imbalance.
Computer Aided Evaluation of Fir Tree Blade Root Profile Based on Particle Swarm Algorithm
Authors: Li Jianhua , Chen Fumin , He Shuai
Published: July 2019

Aiming at the problems of inefficiency and inaccuracy in the evaluation of fir tree blade root profile, a method of computer aided profile evaluation based on particle swarm algorithm is proposed. The evaluation process is automated by this method, the theoretical profile and the coordinates of the measured points are read automatically, the measured points and the theoretical profile are aligned automatically, the profile deviation is calculated, and whether the blade root is qualified is determined. The mathematical model and calculation method of profile evaluation are given, the measured points and theoretical profile are aligned precisely according to the principle of least square by making use of the particle swarm optimization algorithm with adaptive inertia weight(APSO) after coarse alignment, and the influence of position error between design reference and measurement reference on the results of profile evaluation is eliminated. Simulation experiment and practical application manifest better accuracy and stability can be obtained by using APSO algorithm compared with other algorithms when evaluating the fir tree blade root profile. The computer aided profile evaluation method proposed in this paper could totally replace the traditional manual method.
Research on the incremental learning SVM algorithm based on the improved generalized KKT condition
Authors: Xie Wenhao , Liang Gongqian , Yuan Pengcheng
Published: July 2019

In order to adapt to the classification of the large-scale data and the dynamic data, this paper proposes an incremental learning strategy of SVM called GGKKT–ISVM algorithm based on the generalized KKT condition. The algorithm sets the generalized extension factors by the samples distribution density in order to make the useful samples become new support vectors, and it trains a new classifier. Then this algorithm modifies the classifier secondly, and it can not only keep the historical classification information, also can make full use of the new samples’ information, and structure the classifier that has stronger generalization ability. The experimental results show that the algorithm has a good classification effect.
A Second-order 16.5 Bit Analog-to-Digital Converter with a 145 dB Operational Amplifier Gain
Authors: Wei Rongshan , Lin Jiacheng
Published: July 2019

In this study, an IDC applicable for use with temperature and humidity sensors was designed. To this end, we take into account the factors of area and accuracy, a simple second-order chain of integrators with forward (CIFF) architecture was used to achieve an effective number of bits (ENOB) of 16.5. A gain-boosted operational amplifier (op amp) with a gain of up to 145 dB was adopted for the first-order integrator, and the digital filter adopted a sincL structure. In Section 2.1, the working principle of the proposed second-order. IDC is introduced. In Section 2.2, the design specifications are presented, the system timing and circuit structure are introduced, the circuit structure of the first-order integrator is described in detail, and the overall simulation results are provided. In Section 3, the test results obtained for a physical chip implemented using the SMIC 0.18 μm process are presented and compared with those obtained by similar previously proposed designs. Measurements show that the incremental converter achieves 99.1 dB SNR higher than the other second-orders or even some three-order IDC. And the chip occupied an effective area of 0.211 mm2 smaller than the other IDC. Finally, the conclusions of this study are given in Section 5.
Research on Information Retrieval Behavior Based on College Readers’ Cognition
Authors: Hu Jianfei , Meng Luyang , Zhang Bin
Published: July 2019

This paper takes the information retrieval behaviors of college students and researchers as the research object, and discusses the reading cognition and reading behavior habits of college readers through empirical analysis. The study found that there are large differences in reading cognition and reading needs among people of different backgrounds and levels. In the reading and retrieval behavior pattern, they are different in their grasp and use of information retrieval. The main innovation of this paper is to take the central colleges and universities as an example, and analyze the information retrieval behaviors of different levels of students in colleges and universities, and summarize the regular characteristics, which has certain demonstration significance.
Pulsed laser diode dual-end pumped double-end bonded Tm:YAG transient thermal effect analysis
Authors: Wen Ya , Liu Ning , Fan Zhen , Wu Chunting , Jin Guang-yong
Published: July 2019

Based on heat transfer differential equations, by using integral transform we got the internal temperature distribution of pulse LD dual-end pumped Tm: YAG crystal and the thermal focal length of crystal was obtained by the temperature field. At pumped power of 11W and repetition rate of 100Hz, the maximum temperature inside crystal is 27.68°C. As repetition frequency varying from 100Hz to 200Hz, the maximum temperature inside crystal is increasing to 28.24°C. In the case of duty cycle unchanged, as the repetition rate increases, the thermal focal length is distributed in a saw-tooth pattern and gradually becomes shorter, eventually it tends to be periodical distribution. The theoretical analysis of the thermal focal of pulse LD dual-end pumped Tm: YAG crystal is demonstrated by the comparison between the theoretical results and the experimental results.
Social Sentiment Analysis Using Classifiers and Ensemble Learning
Authors: Zhang Linxiang
Published: July 2019

Instant message applications has become the trend of present social network for connecting people nowadays. With such massive information transmitted between end users, it may provide valuable hidden knowledge for data analysing and help us to obtain some insights of how the messages can influence current social trends. During the analyses, the author uses several different classifiers to conduct sentiment collection and use Ensemble Learning to aggregate the results. As a result, the author maps data about users’ sentiment graph in database of given category.
Case Reasoning Based Design System for Product Packaging
Authors: Wang Zheng
Published: July 2019

The overall design procedure of product packaging is divided into 2 phases: generation of packaging design scheme and detailed design scheme, based on which, overall structure of design system for product packaging PACKAGING is designed. By using CBR technology, generation of product design scheme in PACKAGING is achieved, Meanwhile, each key technological step in generating procedure of design scheme for product packaging is discussed in detail.
Performance Analysis of UAV Uplink Communication Technology
Authors: Feng Yongxin , Song Bixue , Liu Fang , Jiang Qiang
Published: July 2019

In order to meet the reliability and anti-jamming requirements of UAV communication system, the anti-jamming performance of the up-link communication technology is analyzed by comparing the influence of different jamming on the signal and the influence of Rice channel on the signal on the basis of in-depth study of the communication mechanism of the low-altitude UAV communication link. The simulation results show that the anti-jamming performance of the signal is better when the interference is added in the upstream communication technology, and the anti-broadband interference of the signal is better than other interference types when the channel is added.
Manual and Semi-manual Comparison in the Annotation of CAPT Speech Corpus
Authors: Xie Yanlu , Wei Xing , Wang Wei
Published: July 2019

Annotation plays an important role in a robust computer aided pronunciation training (CAPT) system. Manual annotation is time and annotator consuming. Automatic annotation is more efficient, but the accuracy is lower. This paper proposes to provide phoneme level labelling candidates with ASR models, and compare this method with manual annotation. In the previous work, phoneme level mispronunciation patterns were modelled and detected to provide readable pronunciation erroneous tendencies (PETs) feedback. In this paper, it is proposed to use this model to provide phoneme level labelling candidates, then annotators could choose the appropriate labels and make final decision. Experimental results show that compared with manual annotation, the consistency rate of semi-manual annotation increased from 88.52% to 92.88%. In addition, the false positive rate (FPR) reduced by 3%, the posterior F1 score declined more than 3%. The reported results demonstrated the efficiency of the proposed method.
HRRP Target Recognition Based on Sparse Representation
Authors: Peipei Duan , Yan Zhang
Published: July 2019

When high resolution range profiles(HRRP) are used to recognize radar target, a few traditional recognition methods analyze the sparseness of HRRP samples. In order to overcome the large sample size problem and simplify the recognition procedure, sparse representation is an effective way to compress HRRP samples and extract the features. Thus, a structure redundant dictionary and a fast sparse representation algorithm are introduced to implement radar target recognition here. The simulation results show that this algorithm has higher recognition rate and better denoising performance. It is easy and practical for radar target recognition.
Spatially Constrained Fuzzy c-Means Clustering Algorithm for Image Segmentation
Authors: Li Xiaohe , Qu Zhan , Yang Xiaojing
Published: July 2019

The fuzzy c-means (FCM) clustering is an unsupervised clustering method, which has been widely used in image segmentation. In this paper, a spatially constrained fuzzy c-means clustering algorithm for image segmentation is proposed to overcome the sensitivity of the FCM clustering algorithm to noises and other imaging artifacts. Firstly, the local prior probabilities of pixel classification are defined according to the fuzzy membership function values of neighbouring pixels, and then those local prior probabilities are incorporated into the objective function of the standard FCM. Thus, the local spatial information embedded in the image is incorporated into the FCM algorithm. Experimental results on the synthetic and real images are given to demonstrate the robustness and validity of the proposed algorithm.
An Improved Mapping and Optimization Method for Implication-based Memristive Circuits Using And-Inverter Graph
Authors: Wang Xiaoxiao , Han Jiaxin , Yang Yifang , Li Yu
Published: July 2019

Any logic operation can be realized in implication-based memristive circuits characterized by low energy consumption and nanometre level size. The synthesis method transforms And-Inverter Graphs (AIGs) representation for logic functions into implication-based networks. It includes a mapping process which converts a cascade of nodes in AIGs to a series of implication gates and detects fan-out nodes simultaneously. An optimized copy process is employed to reduce the delay and area of memristive circuits in occurrence of a fanout node. Experiments are carried out over a benchmark set including 33 functions with input variables from 3 to 41. Experimental results are compared with that from the original algorithm and another Majority-Inverter Graphs (MIG) based mapping method. It shows that the improved algorithm can obtain better performance in latency and area on most of the functions in the test set.
Image Super Resolution Based on Gradient Constrained POCS Method
Authors: Jin Jie , Dong Lili , Jiang Yuhang , Zhang Meng , Xu Wenhai
Published: July 2019

A gradient constraint set based on edge detection is proposed to solve the problem of edge blurring in traditional projections onto convex sets (POCS) images reconstruction. Using the complete edge information of the original low-resolution(LR) image, the edge gradient constraint is applied to the reconstructed image, and the high-frequency information of the reconstructed image is retained to solve the problem of blurring the reconstructed image edge. And this paper uses the resolution test card to carry out simulation experiments, quantitative evaluation of the reconstructed image with respect to the LR images to improve the actual resolution of multiple. Simulation results show that the proposed method can reduce the edge blur of the reconstructed image, and the actual resolution is greatly improved compared with the traditional convex set projection method.
Parallax-Tolerant Image Stitching with Optimal Homography
Authors: Xu Peng , Chen Kuo , Xie Airong , Xiong Shiyong
Published: July 2019

Artificial ghosting is a serious problem in image stitching with wide-baseline, which makes it difficult to achieve seamless stitching. A robust parallax-tolerant image stitching method combined with optimal homography matrix selection and seam-cutting processing is proposed. Optimal homography matrix is obtained by analysing feature point distribution and minimizing the registration error. And then, the aligned area is enhanced by the optimal homography matrix, which can facilitate the seam searching. To realize seamless stitching, the luminance difference and the geometrical structure are used in seam-cutting processing. At last, the experimental results show that the artificial ghosting is solved quickly and effectively in the images stitching with large parallax.
Image Stitching Based on Binocular Vision
Authors: Zhang Bo , Ma Yue , Xu Min
Published: July 2019

In the field of photography, image stitching is used to generate panorama. In most cases, the position and intrinsic parameters of the camera can not be obtained, so the transform matrix can only be calculated using the information of the image. In industrial machine vision detection system, the information of the camera can be obtained by calibration. It is feasible to improve the speed of image stitching algorithm by using the information of camera calibration. An image stitching algorithm based on binocular vision was proposed. Compared with the traditional stitching algorithm based on image information only, it greatly improved the running speed of the algorithm. The effectiveness of the algorithm was verified by experiments.
A Method of Network Coding to Ensure Public Network Security
Authors: Liang Zihe
Published: July 2019

When connecting to the wireless network in public places, users’ personal information will be exposed in the public network, which is easy to be stolen and tampered with by third parties. In this paper, the author proposes a method to protect user information when connecting to public wireless networks in order to protect users’ privacy. Random linear network coding is used to encrypt the accessed user serial number, which is then forwarded to the upper layer. When transmitted to a specific node, the encrypted information is transmitted to the server for information decryption. After decoding the user serial number, the node is accessed. In this way, we can encrypt user information, reduce the delay time of forwarding, and improve the transmission efficiency. The time complexity is O(n2).
Detection and Recognition of Flower Image Based on SSD network in Video Stream
Authors: Tian Mengxiao , Chen Hong , Wang Qing
Published: July 2019

At present, most flower images could only be recognized but not detected. They can only be used in the scenes with a single target instead of the scenes with two or more targets. Some application scenarios require the human-computer interaction mode with the current location information of flowers; moreover, due to the complexity of the environment and the similarity and difference between flowers, the traditional computer visual methods are inefficient and inaccurate. Therefore, this study introduced SSD deep learning technology into the field of flower detection and identification. The flower data set published by Oxford University was used as the research object, and it was used as the input of the neural network model for training and testing. The experimental results show that the average accuracy is 83.64% based on the evaluation standard of Pascal VOC2007, and 87.4% based on the evaluation standard of Pascal VOC2012. The processing time of an image on PC is 0.13s, which indicates that high-quality automatic detection and recognition can be performed, which can facilitate the retrieval of agricultural plant information database and help people to popularize related information of flowers.
Research and Improvement of Single Image Super-Resolution Based on Generative Adversarial Network
Authors: Zhang Tianji
Published: July 2019

This paper proposes a new image super-resolution method based on Generative Adversarial Network (GAN). Firstly, the algorithm model includes generating model and discriminant model, generating model to generate high-resolution image, discriminating model to distinguish the image true or false, the original image is true, and the generated image is false. Using alternate training method, the generated model and discriminant model achieve Nash equilibrium, and finally generate high-quality image. Compared with previous super-resolution method based on generative adversarial network (SRGAN), the following changes have been made: modifying the network structure, removing the unnecessary batch normalization layer in the standard residual block, deepening the network layer number and improving the loss function. The experimental results show that compared with the traditional bicubic interpolation method and compared with SRGAN, the proposed algorithm improves the actual image effect, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) in varying degrees.
Facial Landmark Detection based on Cascade Neural Network
Authors: Chen Xuzhan , Huang Luxi , Chen Yingming
Published: July 2019

A three-stage cascade convolutional neural network is proposed in this paper to learn the facial features. In order to improve the robustness and accuracy of facial landmark detection, several parallel sub-networks are involved in each stage of network. The convolutional neural network performs global advanced feature extraction for the entire facial region in the preceding stage, so as to obtain more accurate positioning. Since the first-stage input covers the whole image, the global facial information can be effectively used to weaken or even avoid the detection errors caused by occlusion, large posture changes, extreme illumination and etc. During facial detection, the first-stage network will make initial prediction of facial landmarks, and then get the facial area narrowed down and accurate according to the prediction results; the second-stage network will take the narrowed-down facial area above as the input, the three parallel convolution networks are used to classify the facial landmarks for quadratic estimates, so as to improve the accuracy of prediction results based on the results of first-stage network. The input of the third-stage network is limited to the small neighborhood of the second-stage prediction results, which can make the prediction results more accurate. The experimental data shows that our method has certain advantages in detection accuracy and reliability.
A Wake-up Radio protocol based on adaptive backoff and dynamic CCA in Wireless Sensor Networks
Authors: Xie Haofei , Ke Junyi , Wang Ping , Yuan Xingwei
Published: July 2019

Asynchronous wake-up communication based on wake-up radio (WuR) is an important energy-saving method in wireless sensor networks (WSNs). The Clear Channel Assessment (CCA) is a major section of energy consumption in asynchronous WuR-based nodes. The traditional static backoff will lead to longer waiting delay which increase the energy consumption of successful sending packets when the network traffic becomes heavier. So a protocol DNAP-WuR is proposed to solve these problems in the terminal initiated (TI) communication mode. It first implements the dynamic CCA optimization (DCO) mechanism which allows nodes to interrupt the CCA process as soon as the channel is busy. Nodes can go to sleep faster during busy periods, instead of performing CCA detection until the counter is reduced to zero. And then, the WuR based adaptive logarithm backoff (WALB) algorithm is used to predict the backoff time sequences which can reduce the WuC’s collision probability and decrease the energy consumption according to the nodes number and the packet arrival rate. In order to avoid the phenomenon that the backoff window size is reduced too fast or increased slowly after packets transmitted. Compared with CSMA-WuR protocol, the DNAP-WuR has 4.6% reduction in energy consumption and 12.1% promotion in throughput.
An Animal Spinal Cord Neural Signal Regeneration Circuit
Authors: Li Wenyuan , Yang Zhaoyuan
Published: July 2019

An animal spinal cord neural signal regeneration circuit is presented in this paper. The circuit consists of an on-chip amplifier which was designed in TSMC 0.18μm 1P6M standard CMOS process and an output circuit using an off-chip high voltage transistor. A low temperature drift bias circuit and a constant-gm rail-to-rail input circuit employing a novel structure called Max Current Selection are designed in the amplifier. The amplifier occupies a die area of 300μm×300μm. The simulation results show that the consumption of the amplifier is about 1.45mW and gain of the whole circuit is 40dB. The output voltage reaches a wide swing of -1V∼4V under +5V and -1.5V off-chip power supplies. The circuit is expected to be used in vitro test for regenerating neural signals of animal’s damaged spinal cord nerve, as a new method for trying to cure nervous injury.
Discussion on coding rules and labelling specifications of defect image samples of substation equipment
Authors: Yang Sha , Xiong Wei , Zhang Zhao , Chen Liang , Huang Shuxin
Published: July 2019

In the image recognition of substation equipment defects based on deep learning, image sample coding rules and labeling specifications are one of the key factors to ensure the quality of sample data. At present, the image of the defect of the substation equipment is scarce, and the sample management specification is not uniform. There is a low level of image recognition for equipment defects in the substation intelligent inspection. Therefore, the paper carries out analysis and thinking on the coding rules and labeling specifications of the substation key equipment defect image samples. The sample making process is standardized through the standardization of the coding of typical substation equipment type, component type, defect type and annotation format, to some extent, which improves the accuracy of sample data quality and substation equipment defect recognition, and improves the level of defect management in intelligent inspections of substation. Finally, the results show that the standardized labeling samples help to improve the recognition accuracy and have wide application value.
Functions, Values & Inadequacies ------An Evaluative Discussion of Pigai Intelligent Online English Writing Correction System in View of Second Language Acquisition
Authors: Yan Wang
Published: July 2019

The recent development of AI technology leads to either the belief that the possibility for it to replace human being has been increasing, or the disbelief that AI technology is still of that potency. This research is to evaluate an intelligent online English writing correction system known as Pigai from the perspective of the second language acquisition. The research is made available (1) in light with learning theories such the Input Hypothesis, as well as investigation of the learners in China; (2) by discussion of functions found in the system, and (3) with analysis of values and inadequacies as demonstrated with the system. The thesis finally argues that writing practice with Pigai system may improve learners’ second language writing skills and language competence, but partially; and will not be of much help in promoting learners’ humanities quality.
The Design of RA Code Pseudo-random Interleaver
Authors: Shen Yanchun , Zhao Guozhong , Zhang Shengbo , Li Yashang , Li Shuai
Published: July 2019

In the process of digital signal transmission, due to noise and other interference signals, the code element waveform will produce some changes. RA code can encode and decode in linear time, which is one of the key techniques of channel coding. Interleaver is a very important part of RA code encoder, so its performance affects the performance of RA code. A S-pseudo-random interleaver and deinterleaver is designed in this paper, and the model is verified by simulation. The experimental results show that the bit error rate (BER) of interleavers with different interleaver lengths is significantly different. The longer the interleaver length is, the better the performance of the interleaver is, and the closer to the Shannon limit is.