bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Review of Audio-Visual Fusion with Machine Learning
Authors: Song Xiaoyu , Chen Hong , Wang Qing , Chen Yunqiang , Tian Mengxiao , Tang Hui 
Published: July 2019

Abstract
For the study of single-modal recognition, for example, the research on speech signals, ECG signals, facial expressions, body postures and other physiological signals have made some progress. However, the diversity of human brain information sources and the uncertainty of single-modal recognition determine that the accuracy of single-modal recognition is not high. Therefore, building a multimodal recognition framework in combination with multiple modalities has become an effective means of improving performance. With the rise of multi-modal machine learning, multi-modal information fusion has become a research hotspot, and audio-visual fusion is the most widely used direction. The audio-visual fusion method has been successfully applied to various problems, such as emotion recognition and multimedia event detection, biometric and speech recognition applications. This paper firstly introduces multimodal machine learning briefly, and then summarizes the development and current situation of audio-visual fusion technology in some major areas, and finally puts forward the prospect for the future.
Analysis of the Passion Criminal Tendencies Based on the Topic-Emotion Model
Authors: Sun Jiaqian , Wang Menghui , Huang Jiasheng , Wang Anqi , Jiang Linying 
Published: July 2019

Abstract
With the penetration of the Internet into daily life, more and more crime clues could be discovered on the Internet. Therefore, this paper uses a new method to predict whether the instant message on the internet has the tendency of emotional crime. It is because that the single theme-based analysis of criminal tendency, which ignores the impact of emotions, may confound the declarative sentences and normal expression with statements that contain criminal tendencies. The method used in this study introduces the analysis of emotional fluctuations while analysing themes. In order to solve the problems above, the study adopts seq2seq model to analyse emotional fluctuation. Themes and the values of emotional fluctuation are put into linear SVM as input vector to judge whether the instant message has an emotional crime tendency. The average predication accuracy of the model reached 86%, which basically accords with the assumption made before the experiment. This model can not only be used to analyse emotional crime, but also to screen the radical opinions on the internet.
Research on Digital Elevation Model Construction Method Based on TIN
Authors: Peng Quan , Zheng Xiaolong 
Published: July 2019

Abstract
Digital Elevation Model (DEM) is a very important part of geographic information system and a key part of various terrain surface studies. In this paper, we first introduce the classical DEM construction method based on irregular triangulation (TIN). In view of the shortcomings of the classical DEM construction method, this paper proposes a volume segmentation method based on variance criterion, in which the variance criterion can take full account of the elevation information of the original data and select the triangles with good shape in space to ensure that the constructed DEM is closer to the actual surface. Finally, an example is given to demonstrate the advantage of the variance criterion relative to the empty circumscribed circle criterion. The classical DEM construction method and the method of volume segmentation based on variance criterion are realized by MATLAB programming.
A High Accuracy and Low Power Temperature Sensor
Authors: Li Wenyuan , Wang Cheng 
Published: July 2019

Abstract
A resistor-based temperature sensor is designed intended for the temperature compensation of MEMS frequency references. High resolution is then required critically to minimize jitter. A Wien bridge RC filter is used to realize the temperature sensor. When the RC filter is driven at the resonant frequency of the filter, the change of temperature results in phase shift. This phase shift is then digitized by a phase-domain sigma-delta modulator whose phase-summing node is realized by a chopper demodulator. Implemented in a 65nm CMOS technology, the maximum power consumption is only 0.096mW under a supply voltage of 1.2V, and achieves 345 μKrms
resolution in a 5 ms conversion time. The sensor occupies an area of 1150μm × 650μm. It achieves ±0.035 inaccuracy over the industrial range (-40°C-85°C).
Numerical Simulation on Electroluminescent property of GaN Nanorod LED array
Authors: Ji Jincai , Feng Xiaolu , Sun Xiaohong , Zhang Xu 
Published: July 2019

Abstract
The quantum well thickness and In concentration of the multi-section nanorod array structure increase with the sidewall height. At present, a two-section nanorod structure with a 466 nm spectrum at the bottom of the nanorod and a 488 nm spectrum at the top has been prepared. In this paper, according to the theory of yellow-blue light mixing to achieve white light, we have proposed a structure of the two-section nanorod, with a white LED model emitting a blue light spectrum at the bottom of the nanorod and a yellow light spectrum at the top. By varying the In concentration in the structure, the spectra of different In concentrations in different positions of the sidewall quantum wells were simulated to obtain the blue light in the sidewall quantum wells. We observed that the blue light of In cooperation range are 0.15∼0.22, the yellow light of In cooperation are 0.27∼0.34, and the corresponding wavelengths varied from 443nm to 498nm and 542nm to 610 nm. Therefore, the proposed structure will be a potential candidate in phosphor-free white-color emission LED.
Improved direct fitting algorithm for elliptic parameters
Authors: Liu Yaru , Chen Xiaorong , Shi Gaohui , Chi Xiaoyu , Li Heng , Dai Xingyu 
Published: July 2019

Abstract
For the ellipse fitting problem often encounter in visual measurement, this paper proposes a method combining algebraic distance fitting method based on least squares and characteristic root method for ellipse fitting. Firstly, the improved hierarchical agglomerative clustering is used to denoise the data. Then the algebraic distance fitting method based on least squares is used to calculate the initial iteration value of the parameters in the characteristic root method. Finally, the Gauss-Newton iteration method is used to solve the elliptic parameters. This algorithm is used to detect the out-of-roundness of the optical fiber and compare the test results with the high-precision optical fiber tester FGM-502. The results prove the validity and accuracy of the ellipse fitting algorithm studied in this paper and show that the algorithm studied in this paper meets the requirements of actual ellipse fitting measurement.
A novel approach for Sparse Imaging of Through-wall Radar
Authors: Xia Chaoyu , Gao Yuxiang , Yu Jie 
Published: July 2019

Abstract
Aiming at the problem of segmented weak orthogonal matching pursuit (SWOMP) imaging blur in the process of through-wall radar (TWR) imaging, a dynamic threshold weak orthogonal matching pursuit algorithm (DWOMP) is introduced in this paper, which can significantly improve the imaging performance. Firstly, the TWR compressed sensing simulation model and over-complete dictionary are established by using Chirp signal radar echo data. Then the specific process of DWOMP algorithm is proposed and used to reconstruct multi-sparse target scenes. Finally, the DWOMP algorithm is compared with Basis Pursuit (BP) algorithm and SWOMP algorithm through simulation experiments. The simulation results show that in the same experimental conditions, the imaging time of DWOMP algorithm is about 3/5 of that of BP algorithm, and the imaging resolution of DWOMP algorithm is better than that of SWOMP algorithm.
An effective underwater image enhancement method based on CLAHE-HF
Authors: Luo Mingshi , Fang Yang , Ge Yimeng 
Published: July 2019

Abstract
In order to solve the problem that the unbalanced illumination and noise induce poor contrast, uneven brightness and poor clarity to underwater image, an effective underwater image enhancement method based on spatial and frequency domain is proposed in this paper. Firstly, Contrast Limited Adaptive Histogram Equalization technology is used in spatial domain to enhance the local contrast of different regions of the image according to the distribution of pixel values of each region, which can not only ensure the image texture clarity and detail characteristics, but also suppress the noise. Secondly, Homomorphic Filtering technology is used to enhance the image details and reduce the noise in the image in frequency domain. Two sets of image data that verify the effectiveness of the proposed method were provided. The experimental results show that, compared with the traditional underwater image enhancement methods, the peak signal-to-noise ratio (PSNR), mean square error (MSE) and information entropy of the proposed method are combined optimal.
Prediction of Drug-Target Interaction with Graph Regularized Non-Negative Matrix Factorization
Authors: Yan Xiao-Ying , Li Run-Zhou , Kang Lei 
Published: July 2019

Abstract
Identification of drug-target Interactions (DTIs) is very important for drug discovery, which can help to find the new uses for an old drug or to discover the off-targets for a given drug. Currently, algorithms have difficulty in finding interactions for new drugs and new targets. We proposed a novel method that uses graph regularized nonnegative matrix factorization framework to predict potential targets/drugs for new drugs/targets by using clustering approaches to construct interaction profiles for new drugs/targets. Compared with other methods, our method obtained the best performance in terms of AUPR.
Research on Somatosensory Interaction Based on Convolutional Neural Network
Authors: Tang Hui , Wang Qing , Chen Hong , Guo Hao 
Published: July 2019

Abstract
Somatosensory interaction is an important part of human-computer interaction. The core of somatosensory interaction must accurately obtain the 3D spatial information of the human body. This article uses an RGBD camera to acquire both color and depth images. We Perform 2D human pose estimation in color images using hourglass network with residual structure. At the same time, we use the sparse feature points to align the color map with the depth map in order to accurately map the detection results in color images to the corresponding depth images. The experimental results show that this method can accurately and quickly obtain the 3-dimensional posture information of the human body, which is an important guarantee for somatosensory interaction.
Voiceprint recognition based on BP Neural Network and CNN
Authors: Zhipeng Dong , Jingcheng Wei , Yumin Xu , Qingmin Meng , Xiaoming Wang 
Published: July 2019

Abstract
At present, speech recognition has become a key technology of human-computer interaction, which can be used in semantic recognition and speaker identification and other related applications. This paper focuses on a speaker identification scene and the technical design. Because artificial neural network has the ability to distinguish complex classification boundaries, plenty of work on speech recognition had studied multi-layer perceptual networks to improve the accuracy of classification where back propagation method (BP algorithm) has been used. In the studied scheme, when the test objects (speakers) speak the same isolated word, the identification system can judge who the speaker is by identifying the voice voiceprint of different people. Our design not only considers the recognition based on BP as well as its variant, but also explores the voiceprint recognition based on convolution neural network (CNN). Secondly, the network structure and performance are also compared in detail.
Multi-color Space Features Analysis from Rock Shin-section Image for Rock-type classification
Authors: Liu Ye , Guo Chao , Li Fan , Lv Lintao , Gao Dongchen 
Published: July 2019

Abstract
To fully analyze the features extracting from multi-color space for rock classification, each feature will be evaluated in this paper. And, to enhance correlation between features and reduce dimensionality of feature space, a PCA approach will be used and the result of PCA will also be fully analyzed. A C-SVM model is chosen to test analysis result. Data set consist of 500 images from Ordos basin. The classification result between single color space and multi-color space will be compared, and the cooperation result shows that features from multi-color space can support classifier like C-SVM to take higher accuracy and higher reliability.
A DDoS Attack Detection Method Based on Machine Learning
Authors: Pei Jiangtao , Chen Yunli , Ji Wei 
Published: July 2019

Abstract
Distributed denial-of-service attack, also known as DDoS attack, is one of the most common network attacks at present. With the rapid development of computer and communication technology, the harm of DDoS attack is becoming more and more serious. Therefore, the research on DDoS attack detection becomes more important. Nowadays, some related research work has been done and some progress has been made. However, due to the diversity of DDoS attack modes and the variable size of attack traffic, there has not yet been a detection method with satisfactory detection accuracy at present. In view of this, this paper proposes a DDoS attack detection method based on machine learning, which includes two steps: feature extraction and model detection. In the feature extraction stage, the DDoS attack traffic characteristics with a large proportion are extracted by comparing the data packages classified according to rules. In the model detection stage, the extracted features are used as input features of machine learning, and the random forest algorithm is used to train the attack detection model. The experimental results show that the proposed DDoS attack detection method based on machine learning has a good detection rate for the current popular DDoS attack.
Analysis of Human Height Based on Footprint Multivariate Regression Analysis
Authors: Yang Wenbo 
Published: July 2019

Abstract
In the process of investigating criminal cases, it is usually necessary to infer the suspect’s height according to the footprints on the scene. The current common method is to multiply the length of barefoot footprints by a coefficient of 7, and the accuracy of the results obtained by this method is less than 70% in the positive and negative 3cm of the interval. In this paper, the average foot length of 1200 people is collected by using mathematical statistics analysis method, and the average stride length of each person is added to the multiple regression analysis. The better results are obtained, which makes the accuracy rate of body height in positive and negative 3cm increase to about 87%.
Power Network System Identification and Recovery Based on the Matrix Completion
Authors: Liu Qi 
Published: July 2019

Abstract
Health detection of power networks is a complex and important issue. In order to reduce the detection complexity of the power network, a matrix recovery method based on the matrix completion has been proposed in this paper. Matrix completion has attracted considerable attentions in computer vision, system identification, and machine learning in recent years. This paper presents a survey on algorithms about matrix completion including Singular Value Threshold (SVT), Alternating Direction Method (ADM) and so on. In order to reproduce algorithms about matrix completion and evaluate the performance of these algorithms, this paper is present. The numerical experiments are conducted to evaluate the performance of algorithms. An application about image denoising is also carried out.
Design of a minimum resolution contrast detection system
Authors: Li Ji-Quan , Li Hai-Ping , Li Jing-Wen , Zhou Hou-You 
Published: July 2019

Abstract
Aiming at TV camera equipment, a minimum resolution contrast detection system is designed. The system quantitatively controls the brightness of the target background in a closed loop mode, simulates a test pattern with adjustable contrast for the system to be tested under an infinite uniform background, and uses image processing technology and Johnson criteria to replace the traditional human eye observation to complete the objective judgment of the minimum resolution contrast, thus realizing the quantitative measurement of the minimum resolution contrast evaluation parameter of the television camera system. This detection system can realize continuous reading and adjustable brightness ranging from 0.02 to 4.45× 104 cd/m2, and the control precision can reach less than 0.003 cd/m2, thus satisfying the measurement of the minimum resolution contrast of the television camera system within 180mm of the light-transmitting aperture.
Magnetic field analysis of solenoid driven by alternating current
Authors: Wei Cai , Youan Xu , Zhiyong Yang , Zhonghao Zhao 
Published: July 2019

Abstract
In order to elaborate the magneto-optical modulation process in detail and clarify the distribution of the magnetic field in the solenoid. In this paper, the magnetic field model of the solenoid with sine wave is established by Maxwell’s equation. The boundary condition is determined by the law of Ampere loop and the law of electromagnetic induction. The exact function expression of the magnetic field is obtained and the relevant factors are analyzed in detail. The simulation results show that the magnetic field in the solenoid with sine wave has good sine characteristic and the magnetic field is closely related to the factors such as the frequency of the driving signal and the axial magnetic field at any point inside the solenoid is much larger than the circumference magnetic field at that point. So, when the magnetic field at a point inside the solenoid is analyzed, it is possible to ignore the circumferential magnetic field at that point. But outside the solenoid, both directions of magnetic field need to be considered. In this paper, the method of studying the magnetic field of the solenoid provides a reference for the detailed analysis of the magnetic field inside and outside the solenoid under different signal driving.
Multi-joint Cooperative Control of Athletes under High Intensity Training
Authors: Tao Wu 
Published: July 2019

Abstract
The control structure and performance of high intensity sports training on the trunk joint of athletes is analyzed to improve the quality of sports training, a multi-joint cooperative control model for athletes under high intensity sports training is proposed based on attitude sensor fusion tracking compensation. The multi-joint kinematics model of the athletes’ torso under high intensity sports training is constructed, and the constraint parameters of the multi-joint linkage and cooperative control of the athletes are analyzed combined with the identification method of the parameters of the state space model. The mechanical control model of the action of high intensity sports training on athletes’ torso is established by using the coupling method of driving branch chain inertia, and the characteristics of multi-joint linkage coupling of athletes are analyzed. The collaborative filtering Kalman fusion method is used to realize the parameter fusion and adaptive parameter identification of athletes’ multi-joint linkage. According to the results of parameter recognition, athletes’ multi-joint linkage collaborative control is realized. The simulation results show that the high intensity sports training method has good stability and low error of attitude parameter adjustment for athletes’ multi-joint linkage and coordination control, which improves the effect of sports training.
The Investigation of Photonic Band Gap of 2D Multi-fold Photonic Quasicrystals
Authors: Yu Xi Xiao , Hong Sun Xiao 
Published: July 2019

Abstract
Two dimensional photonic quasicrystals (PQCs) with different rotational symmetries are constructed based on holographic interference patterns. The photonic band gap (PBG) are obtained by calculating the transmission and reflection spectra based on the finite element method (FEM). The maximum PBG of different PQCs is obtained and investigated by considering the same dielectric constant and different fill factors. All the simulation settings are the same except for the structure itself in this process. The results show that 10-fold quasicrystal exhibit better band gap richness than the others. This may provide a reference to select appropriate quasicrystal structures for photonic devices.
Research on the Theory of Multiple Intelligences in Training Mode of College Students’ Innovative Ability
Authors: Zhang Maohong , Liu Hui , Sun Lihua 
Published: July 2019

Abstract
In today’s pluralistic era, the cultivation of comprehensive quality education is being paid more and more attention. With the development of computer technology and artificial intelligence, especially with the advent of the theory of multiple intelligences, the meaning of intelligence is being understood again, provide the feasible thought guidance for the implementation of comprehensive quality education. Applying the theory of multiple intelligences to the subject teaching, the teaching design is aimed at the students’ individual intelligence advantage. In the course of teaching, we should pay attention to the use of weak intelligence such as interpersonal communication intelligence and introspection intelligence, in order to promote the all-round, active and individual development of students’ multiple intelligences. Through the preliminary experiment exploration on the improvement of teaching quality, we obtain the cultivation mode of college students’ innovative ability based on the theory of multiple intelligences after theoretical analysis and experimental verification. It is conducive to create suitable environment to develop multiple intelligence for students, so that students’ comprehensive intelligence, innovation consciousness and personality potential can be fully developed, so as to promote the cultivation of College Students’ creativity, stimulation of creativity and speed of scientific literacy.
Human action recognition based on a single acceleration sensor
Authors: Wu Yi 
Published: July 2019

Abstract
In this paper, based on the acceleration signal acquired by a single triaxial acceleration sensor worn at the bottom of the finger, an algorithm for recognizing the daily movement behavior of the human body is proposed. The algorithm converts the three-axis acceleration into the resultant acceleration, horizontal acceleration and vertical acceleration, and extracts the features from them. The 37 features are sorted by the F-score as a measure of the importance of the feature, and a subset of features that meet the application requirements is found. Model training is performed on the feature subset using multi-class SVM to obtain the final classification model. Experiments show that the model has high recognition accuracy and the accuracy can reach 93.17%.
DBM Optimization with Additional Category Information
Authors: Liu Kai , Zhang Jie , Wang Xinghai 
Published: July 2019

Abstract
For unconstrained training of restricted Boltzmann machine (RBM), it is easy to appear that feature homogenization leads to poor generalization ability. This paper introduces category conditions into DBM training, proposes label conditional RBM which is used in the construction of DBM. According to unsupervised training characteristics of RBM, this paper adds the category information as the model hidden unit training condition to the implicit unit posterior activation probability calculation. This paper applies the model as the underlying structure of the deep Boltzmann machine (DBM) to the deep network construction. Through handwritten digit recognition set test, compared with the shallow model, the new model after adding the category condition has a great improvement in the model training speed and feature extraction effectiveness, and can effectively enhance the feature learning ability of the deep model.