bluuebunny/crossref_metadata_2025 huggingface dataset:
KLF7 overexpression in bone marrow stromal stem cells graft transplantation promotes sciatic nerve regeneration
Authors: Li Wen-Yuan , Zhu Guan-Yu , Yue Wen-Jiang , Sun Guang-Da , Zhu Xiao-Feng , Wang Ying
Published: July 2019

Objective. Our previous study demonstrated that the transcription factor, Krüppel-like Factor 7 (KLF7), stimulates axon regeneration following peripheral nerve injury. In the present study, we used a gene therapy approach to overexpress KLF7 in bone marrow-derived stem/stromal cells (BMSCs) as support cells, combined with acellular nerve allografts (ANAs) and determined the potential therapeutic efficacy of a KLF7-transfected BMSC nerve graft transplantation in a rodent model for sciatic nerve injury and repair. Approach. We efficiently transfected BMSCs with adeno-associated virus (AAV)-KLF7, which were then seeded in ANAs for bridging sciatic nerve defects. Main results. KLF7 overexpression promotes proliferation, survival, and Schwann-like cell differentiation of BMSCs in vitro. In vivo, KLF7 overexpression promotes transplanted BMSCs survival and myelinated fiber regeneration in regenerating ANAs; however, KLF7 did not improve Schwann-like cell differentiation of BMSCs within in the nerve grafts. KLF7-BMSCs significantly upregulated expression and secretion of neurotrophic factors by BMSCs, including nerve growth factor, ciliary neurotrophic factor, brain-derived neurotrophic factor, and glial cell line-derived neurotrophic factor in regenerating ANA. KLF7-BMSCs also improved motor axon regeneration, and subsequent neuromuscular innervation and prevention of muscle atrophy. These benefits were associated with increased motor functional recovery of regenerating ANAs. Significance. Our findings suggest that KLF7-BMSCs promoted peripheral nerve axon regeneration and myelination, and ultimately, motor functional recovery. The mechanism of KLF7 action may be related to its ability to enhance transplanted BMSCs survival and secrete neurotrophic factors rather than Schwann-like cell differentiation. This study provides novel foundational data connecting the benefits of KLF7 in neural injury and repair to BMSC biology and function, and demonstrates a potential combination approach for the treatment of injured peripheral nerve via nerve graft transplant.
Research on the Development Path of Competitive Intelligence System of Commercial Banks Based on SCP
Authors: Xu Rongzhen , Han Jiashan , Wang Meiqi
Published: July 2019

Competition is the essence of market economy, and how commercial Banks reform and develop and occupy a dominant position in competition is a common concern in the fields of finance and information science. By sorting out relevant results, it can provide reference for the development and research of commercial Banks. Starting from financial intelligence, this paper discusses the development evolution of commercial Banks from the perspective of competitive intelligence. Based on SCP analysis of the development dilemma of commercial Banks, this paper proposes the development path of competitive intelligence system of commercial Banks. Putting forward new ideas for the development of commercial Banks, combined with the needs of the industry, in-depth competition intelligence collection and processing work for the innovation and development of commercial Banks is of great significance.
A New Variable Structure Multi-Model Target Tracking Algorithm
Authors: Wang Linxi , Hu Xiaoxi , Han Xun , Kuang Yin , Yang Xinquan
Published: July 2019

Maneuvering targets tracking technology has important research value in the fields of space target detection, civil aviation, ground traffic control and so on. To solve this problem, a new variable structure multi-model tracking algorithm based on fuzzy logic and strong tracking filter is proposed. The basic ideas of fuzzy logic inference and strong tracking filter are analyzed. An adaptive grid multi-model algorithm based on strong tracking filter and fuzzy interaction is established. The algorithm is also compared with generalized pseudo-Bayesian algorithm, interactive multi-model algorithm and traditional variable structure multi-model algorithm. The simulation results further prove the correctness and effectiveness of the proposed algorithm. Under the same simulation conditions, tracking performance of the proposed algorithm is significantly improved, and it has a higher cost-effectiveness ratio. Therefore, the proposed algorithm has a good application prospect in maneuvering target tracking.
Dual Channel Audio Watermarking Algorithm Based on Embedded Strength Optimization
Authors: Luo Yifan , Peng Dezhong
Published: July 2019

This work proposes a new dual-channel audio watermarking algorithm to improve the imperceptibility of watermark while maintaining the robustness. First, we use the correlation method to detect the pitch of the origin audio, by which the audio will be segmented adaptively. Then, we implement the discrete wavelet transform (DWT) on the audio signal to get the approximate decomposition coefficients. On this basis, the coefficients are divided into odd-order and even-order channels by the coefficient serial number. According to the high similarity between the two channels, the coefficients of the even-order channel are embedded into the odd-order channel in the form of an echo. To improve the imperceptibility of the watermark, we apply the cross-correlation method to optimize the embedding strength to minimize the signal-to-noise ratio (SNR). By this way, the robustness of the watermark will be improved without significantly affecting the original audio quality. In the watermark extraction stage, the coefficients cross-correlation between the embedding channel and the embedded channel is used to detect the signal peaks. According to the peak positions, the watermark can be extracted, so the blind detection is realized. We test the proposed method by simulation experiments, and the experimental data show that the robustness of the proposed method is higher than the method proposed in [1].
Enhanced Noncontact Vital Signs Detection Method for Slightly Moving Subjects Using Tracks Catching and Parameters Estimation (TCPE)
Authors: Chen Deken , Hong Weijun , Dong Yaru
Published: July 2019

A novel signal processing method based on track catching and Parameters estimation(TCPE) is proposed to detect the vital signs of slightly moving subjects, which can extract the vital signs of subjects with the influence of Gaussian noise or harmonic interference when the subject is stationary or slightly moving without much body shake. The experiment results show that the proposed method can detect the vital signs of slightly moving subjects with higher accuracy comparing with traditional methods.
A New Privacy Protection Security Scheme for Blockchain
Authors: Qiao Kang , You Wei , Tang Hongbo
Published: July 2019

The strong security and strong privacy protection features of the blockchain are important aspects of the development of the blockchain, but the security and privacy protection features of the blockchain are not perfect. With the continuous development and wide application of blockchain technology, the problem of privacy leakage is becoming more and more prominent and must be fully taken seriously. This paper proposes a new blockchain signature scheme based on the combination of aggregate signature and ring signature for the privacy protection of transaction addresses in blockchain. This scheme uses a ring signature as the basis for the signature algorithm. On the basis of this, it combines the aggregate signature, which hides the address information of the signer on the one hand and fixes the signature length on the other hand. This enhances the privacy protection capability of the transaction address in the blockchain, and also effectively reduces the signature space of the blockchain system, and solves the problem of capacity expansion to some extent.
Effect of Corporate Tax Avoidance on the Investment-cash Flow Sensitivity
Authors: Rui Ji
Published: July 2019

This paper aims to disclose the comprehensive effect of corporate tax avoidance on investment cash flow sensitivity. for this purpose, a number of enterprises listed on shanghai and shenzen stock exchanges (a-share enterprises) during 2009∼2015 were selected to explore the effect of corporate tax avoidance on the free cash flow of enterprises. in light of the preivous studies, an investment cash flow sensitivity model was created based on book-tax difference (btd), and the descriptive statistics were subjected to regression analysis. the research reveals that enterprises with a high level of tax avoidance have a high investment cash flow sensitivity; tax avoidance of an enterprise can directly increase the cash flow, and suppress the cash flow by raising the deferred financing cost. the research findings shed new light on the decision-making of corporate tax avoidance.
Flower identification based on Deep Learning
Authors: Tian Mengxiao , Chen Hong , Wang Qing
Published: July 2019

In the field of plant scientific research, agroforestry investigation and production and management, plant identification is crucial basic work, and flower identification is an important part of plant identification. Given the present artificial defects of labor cost, low efficiency and low accuracy in present artificial flower information query and traditional computer vision method, the study built a modified tiny darknet in flowers classification method. Seventeen types of flower datasets published by Oxford University are taken as the research objects and the input of the neural network model. The deep network classification model is trained to automatically extract the characteristics of flower images. Combined with softmax classifier, the flower test images are classified and identified. The experimental results show that the classification accuracy is 92% which is higher than the classification algorithm results of the original model and some current mainstream models. This model has a simple structure, few training parameters, and has achieved a good recognition effect. It is suitable for automatic classification and recognition in the field of flower planting and is convenient for the retrieval of agricultural plant information database.
Efficient and Dynamic Byzantine Fault-tolerant Protocol
Authors: Zhang Chengyun , Lin Pei
Published: July 2019

With the development of blockchain, distributed computing and other technologies, the problem of Byzantine fault tolerance has been paid more and more attention. After Lamport raised this problem and gave the corresponding solution, more and more people optimized the solution to the problem in terms of algorithm efficiency and security. After studying the previous algorithm, we used the idea of “predictive execution” and “resource minimization” to convert the original three-phase protocol into two phases and under normal circumstances only 2f+1 nodes can be used to complete the work. Greatly improved the efficiency of the algorithm. At the same time, we consider the total order problem and propose a system to execute the request made by the client in chronological order to prevent the correct client request from being stolen by the enemy. Finally, we consider that the previous BFT protocol did not fully consider the problem of external nodes dynamically joining the system, and optimized the previous solution to enable external nodes to join the BFT system safely and quickly.
Fast robust matching algorithm based on BRISK and GMS
Authors: Cen Chen , Li Runqi , Xu Xi
Published: July 2019

In the image stitching technology, traditional feature extraction algorithms have uneven feature points distribution, many redundant features, time-consuming feature points precision matching and low image registration accuracy. In view of these problems, this paper proposes an improved image registration algorithm based on BRISK and GMS. In this method, the image is first divided into meshes and BRISK algorithm is used for extracting image features, then the Brute Force matching algorithm is used for rough image matching. Finally, the mesh motion estimation method is used for feature quantity statistics, and error matching is removed to obtain a set of fine matching feature points for image registration. This paper verified the robustness of the improved algorithm by comparing it with other methods in the Mikolajczyk data set. The experimental results show that this algorithm achieves higher matching accuracy on the basis of maintaining speed compared with the original algorithm, and the average accuracy is improved by 8.02%. It has better performance than the traditional algorithm, which can be used for occasions with higher requirements on registration accuracy and real-time performance.
Research and Analysis of Blockchain Data
Authors: Yang Xiaojing , Liu Jinshan , Li Xiaohe
Published: July 2019

Blockchain technology is characterized by anti-counterfeiting, non-tampering and easy to implement smart contracts, and is known as a new technology that will lead to social change. Therefore, the study of data in blockchain has important theoretical and practical significance. The author proposes a three-layer model from the perspective of data analysis; on the basis of this model, the data structure and data type of blockchain, as well as the data structure and operation principle of smart contract are studied. Finally, the seven problems and correlations of blockchain data analysis are summarized.
Research on Recommendation Algorithms Based on Collaborative Filtering
Authors: Zhao Yongxia , Zhang Bo
Published: July 2019

With the rapid development of Internet industry in the era of Web 2.0, creation and sharing of content becomes easier and easier, and people’s accessing methods to information have changed. For information consumers, the exponential growth of information makes users use information inefficiently. Users are eager to get more valuable information for themselves. For information producers, they also need a way to make their information be concerned by more potential users. Collaborative filtering algorithm is the most widely used and successful recommendation algorithm. A good recommendation algorithm can help users to find their favorite videos in massive videos, and even relatively unpopular videos can be pushed to users who may be interested in it.
Research on Method of Improving Thermal Efficiency of Oilfield Horizontal Water Jacket Heating Furnace Based on Smith Algorithms
Authors: Xu Xiang-qian , Zhou Hao-bin
Published: July 2019

In view of the low thermal efficiency of oil field horizontal water jacket heating furnace, the single neuron SVM-Smith predictive control method was proposed to improve the heating efficiency according to the structure of the reformed heating furnace. The Support Vector Machine (SVM) was used to predict the flow rate of heated medium and improved the accuracy of Smith prediction and compensation. The single neuron PID control and Smith predictive compensation were combined to tune the parameters of the control system. The experiments showed that the thermal efficiency had been significantly improved, and the control algorithm had achieved good control effect.
STRNN: End-to-end deep learning framework for video partial copy detection
Authors: Hu Yanzhu , Mu Zhongkai , Ai Xinbo
Published: July 2019

The task of partial copy detection in videos aims at determine if one or more segments of the query video are already present in the data-set, while giving the information of similar portion time period. At present, most effective algorithms of partial copy detection in videos are designed as three steps: feature extraction, feature matching and time alignment. The separation of feature matching and time alignment module ignores the spatio-temporal information of partial copy to some extent. Therefore, satisfactory performance is not obtained. In order to reduce this loss, this article does not decompose it into two separate tasks, but using a single convolution neural network to solve these two aspects. First, we sample video frames and extract CNN features, calculate the spatio-temporal relationship matrix of the source video and the query video, and then graphically map the matrix and train the convolution neural network based on the object detection task of the RefineDet model. Finally, in the query phase, the time period of the partial copy is deduced based on the detection result. In this paper, we evaluate the performance of the algorithm on the real complex video copy detection data-set VCDB which is significantly improved compared with the state-of-the-art partial copy detection framework.
Dynamic Adaptive Streaming based on Deep Reinforcement Learning
Authors: Chen Jian , Wu JianYing
Published: July 2019

Dynamic adaptive streaming over HTTP (DASH) is the dominant technology of multimedia delivery over the Internet. In DASH system, adaptive bitrate (ABR) algorithms running on client-side video player are the key to improve user quality of experience (QoE). However, most existing ABR algorithms employ fixed control rules to make bitrate decisions based on throughput, playback buffer size, or a combination of the two. As a result, their performance in the complicated and fluctuant network environment is incompetent. In this paper, we propose QRL, a bitrate adaptation approach based on deep reinforcement learning. QRL uses double Q-Learning, an enhanced Q-Learning method. After training the neural network model, the algorithm can select proper bitrates for future video segments based on all the information collected by client during the video playback process. Simulation results show that QRL achieves better performance than other algorithms.
Research on geographic information servicing construction plan and application of urban and rural water conservancy
Authors: Xitao Huang , Huaien Li , Yu Zhang , Shaobing Zhao
Published: July 2019

With the vigorous development of digital and smart cities in China, the digitalization and informationization level of various industries have improved. However, the level of informatization in the township area and below is relatively low, and resources are relatively scarce. Remote sensing and geographic information technology can provide diversified products, services and interconnections for urban construction, planning, management, environment, water conservancy, electricity, pipelines, and transportation. In view of the application requirements of the water conservancy department, this paper discusses the construction framework design of the grid-based fine management service platform and the urban and rural water conservancy management system, in order to provide scientific reference for the water resources information management of relevant departments.
An Analysis of the Application of Flipped Class Teaching Mode in Photoshop Courses in Higher Vocational Colleges
Authors: Cong Han , Jun Chen , Haimei Gong
Published: July 2019

Flipped class is a new teaching method, which changes the traditional teacher-centered teaching mode, and improves the class participation of higher vocational students, helps students to think actively and understand the relevant knowledge deeply. In the process of teaching Photoshop course in higher vocational colleges, improve students’ interest in learning and promote the quality of teaching.
Application of Automatic Speech Recognition (ASR) Algorithm in Smart Home
Authors: Chen Hong , Zhang Bo
Published: July 2019

Study on practical ASR system for smart home had significant meanings for the development of the smart home. Through analysis of embedded ASR technology and the control technology of smart home, people could utilize VS1003 to record the speech for audio decoder chip by taking the NL6621 template as the platform. And, the ASR system on smart home could be realized by utilizing the Hidden Markov Model (HMM) algorithm to conduct speech model training and speech matching. Test showed that, the ASR system has relatively high recognition rate and real-time property.
A novel SVM parameter tuning method based on advanced whale optimization algorithm
Authors: Yin Xuehao , Hou YanDong , Yin Jiabao , Li Chao
Published: July 2019

The classification performance of support vector machine (SVM) algorithm is highly dependent on the careful tuning of hyper-parameters and penalty coefficient. This paper introduces a novel SVM parameter optimization method by using the advanced whale optimization algorithm (AWOA) that is an improved whale of algorithm (WOA) with external archiving strategy. A new framework for SVM parameter optimization based on AWOA is built. To demonstrate the performance of our proposed method, six typical data sets are chosen to evaluate the effect of SVM classification problem. Experimental results show that the higher accuracy and better convergence can be achieved by AWOA compared with other three usual parameter optimization methods (WOA, PSO, and DE).
Artificial Intelligence and Its Application in Computer Network Technology
Authors: Sun Wenhui , Li Cuimin , Ren Ripeng
Published: July 2019

This paper briefly introduces the concept, origin, characteristics and application of artificial intelligence, expounds the application advantages of artificial intelligence in computer network technology, analyzes the problems existing in computer network technology, and launches the application of artificial intelligence in computer network technology. In-depth study, this article can play a certain reference and help for the application of artificial intelligence in computer network technology, gives full play to the value and role of artificial intelligence, promotes the development of computer network technology, and better meets the current social and economic development process.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Design of Artificial Music Intelligence System Based on Fuzzy Perception Learning
Authors: He Yumeng , He Ping
Published: July 2019

The music intelligence of simulated human is of great significance in the research of music informatics. The purpose of this paper is to establish a formal system for describing music intelligence from the perspective of music memory-perception relationship, namely artificial music intelligent system. First, music intelligence is defined as the memory-perception ability of music elements, and a music intelligent system based on music memory mapping and music perception inversion is established by applying the principle of relational mapping-inversion. Secondly, from the perspective of music memory-perception fuzziness, music intelligence index based on fuzzy memory-perception and non-fuzzy memory-perception is proposed, and the solution method is given. Finally, a learning model of music intelligent system is established. Research shows that artificial music intelligence system is a kind of learning system of fuzzy memory-perception, the learning system embodies the basic characteristics of the music nervous system of the human brain, and to establish the artificial music intelligence system to explore people’s music intelligence, improve people’s music cognitive ability and value of music research has far-reaching significance.
Design and Implementation of Intelligent Measurement Testing and Calibration Management Information System based on Big Data
Authors: Zhu Yangpeng , Liu Juanjuan
Published: July 2019

In recent years most testing and calibration laboratories in developed and developing countries have generally enhanced the awareness of information construction by adopting laboratory information management system to strengthen the information construction. This paper designs an intelligent measurement testing and calibration management information system based on large data, aiming at the problems of data connection and transfer, sample flow management, weak quality management and customer’s informed inspection status in testing and calibration laboratory.The system adopts B/S and C/S mixed development mode, uses the current popular information technologies and utilize reasonably SQL Server database resources.Based on the network topology and architecture of the system, the system realizes function modules. In addition, this paper makes innovations in measurement equipment, sample testing, operation record, certificate life cycle management, multi-channel notification to customers, intelligent early warning.Therefore, the intelligent measurement and calibration management information system provides guarantee for improving refined management level of measuring and testing Institutes and third-party detection institutions, standardizing business processes, improving work efficiency and quality management, and providing better service to customers.