bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research of Face Detection Based on Skin Color Feature
Authors: Li Pei , Wang Hongjuan , Li Yeli , You Fucheng , Liu Mengyang 
Published: September 2020

Abstract
Face image contains many pattern features and this paper studies a face detection algorithm based on skin color features. Through the analysis of the face detection method based on skin color features, the calculation process and principle are understood, and the algorithm and detection techniques such as color space, preprocessing face image detection technology. Building a corresponding skin color model based on face characteristics are analyzed in depth. Because the skin color only occupies a specific range in the primary color of color space, the probability likelihood graph of skin color is obtained by calculating skin color similarity. Then the binary image of the possible face region is obtained by smooth filtering, threshold segmentation, and other operations. Then the area center and height ratio are used to calculate the center of mass and deflection angle, and the correlation coefficient is matched with the face module. Finally, the face area is detected and marked. Test results show that the algorithm can achieve face detection well.
Training and Operation of Multi-layer Convolutional Neural Network Using Electronic Synapses
Authors: Ding Yi , Li Penglong , Liu Jiaqi , Luo Ding , Li Xiaolong , Li Zhenghao 
Published: September 2020

Abstract
For the reason that electrotonic-based memristive devices have been developing rapidly, memristive synapses show a strong superiority in being exploited to construct the neural network system. Nanoscale of memristive devices provides wide prospects for making the hardware implementation of neuromorphic networks. The primary neural network can be satisfactorily implemented on the memristor, which means that memristors can be applied to simple machine learning tasks. However, training and operation of the peculiar neural network with multilayer special processing functions on memristors is still a challenging problem. In this paper, we introduce the experimental implementation of transistor-free metal-oxide memristive crossbars, with device variability sufficiently low to allow operation of integrated neural network, in a multilayer convolutional neural network. Our network consists of multiple 3×3 memristive crossbar arrays both on the convolutional layers and the last layer, which reduces the challenge for the practical implementation of the deep networks. To perform the perfect recognition of the shape in the 27×27 pixel binary images, we bring forward a new coarse-grain variety of the gradient descent algorithm to train the proposed network. Finally, our trained network achieves desirable accuracy.
Prediction of LEA Plant Proteins Based on Machine Learning
Authors: Hu Siquan , Zhao Meidi , Shi Zhiguo , Zhang Min 
Published: September 2020

Abstract
Plants’ ability to grow smoothly in harsh environments is restricted by various factors. Late embryogenesis abundant proteins play a vital role in plants’ resistance to environmental stresses. Therefore, identifying lea proteins has become a valuable task. Although biological experiments are very important in such task, machine learning methods could narrow the searching scale with a certain accuracy and the increased speed. At present, there is a lack of such work in this area. This paper proposes a set of machine learning methods for predicting late embryogenesis abundant proteins and compares the performance of SVM and random forest (RF) methods with CNN method. Samples are collected from UniProt and NCBI with experts’ confirmation. The results show that the RF model is better than other models. The accuracy of the RF model is higher than 89% on the training set and is higher than 91% on different independent samples. The work provides some new ideas and methods for the identification of LEA proteins.
Study of Underwater Target Recognition Using Feature Extraction Based on Wavelet Technology
Authors: Guo Lihua , Wang Hongling 
Published: September 2020

Abstract
The characteristic information in target echo’s that using geometry and elastic information is possibly the only way to recognize the underwater target. The findings indicate that if the material of the elastic target is different, the vertical wave velocity and the horizontal wave velocity’s relative value is different. Even if the shape is the same, its scattering difference in frequency characteristic is also very obvious, therefore the elastic information may be the only way to recognize the target of the same geometrical shape but material to be different. This paper studies three feature extraction methods based on wavelet technology to obtain the elastic information from the target echo. It can obtain the essential eigenvector and the characteristic dimension is lower than the conventional way. Using the three methods to analyse the target echoes of two different material targets in pond, the recognition ratio shows that the feature extraction method is effective.
A Novel Sleep Scheduling Strategy on RISC-V Processor
Authors: Zhou Weixin , Wu Dehua , Xiao Wan’ang , Gao Shan , Gao Wanlin 
Published: September 2020

Abstract
With the development of the Internet of Things, low-power technology has gradually become a primary factor in processor design. Processor sleeping mode is considered as an effective low-power technique. However, operation errors may occur if the long cycle instruction has not been completed during sleeping mode switch. To solve this problem, a novel sleep scheduling strategy based on RISC-V instruction set architecture is proposed. In this paper, the structure of RISC-V processor with task dispatching mechanism is described firstly. Then, WFI instruction and gating clock technology are adopted to realize the sleep scheduling strategy. Finally, hardware simulation is executed to demonstrate the feasibility of the novel sleep scheduling strategy.
Fast Retrieval Method of Image Data Based on Learning to Hash
Authors: Fang Shiyuan , Wang Jianfeng , Yang Cheng , Tong Pengpeng 
Published: September 2020

Abstract
Hash is a method that is widely used in nearest neighbor retrieval, and its goal is to convert high-dimensional image data into low-dimensional representations or into a set of ordered binary code. As one of the more efficient methods of data storage and retrieval, the hash method is widely used in the nearest neighbor retrieval of large-scale image data. The traditional hashing method generates a hash code by manually extracting features, so that the feature and the hash code do not have the best fit, so that the generated hash code is suboptimal. The rapid development of deep learning makes the computer have a good effect on image visual feature recognition. Combining the learning of hash function makes the performance better than the traditional hash method. In this paper, a deep hash method based on triple constraint is proposed to extract the similarity features of the same category and distinguish the features between different categories. Further learning the hash code makes the similarity of the image preserved. Experiments show that the Hash-based learning method has better performance on CIFAR-10 and NUS-WIDE than other methods.
Modal Parameters Identification Based on Equalization Principal Component Analysis
Authors: Zhang Xuan , Zhang Jingjing , Xu Zequan , Luo Jinglin 
Published: September 2020

Abstract
Aiming at the magnitude influence of different discrete points in modal parameters identifications with traditional principal component analysis (PCA), a method of modal parameters identifications by equalization PCA is proposed in this paper. The basic idea is using the mean of time domain response data before identifying modal parameters with PCA. On the premise of not losing the main information of the original data, the influence of magnitude on the data is weakened. Taking a doubly-clamped beam as an example, modal parameters of the beam are identified by traditional PCA and equalization PCA with the same constraints of principal component cumulative contribution rate. The results show that compared with the traditional PCA, equalization PCA can effectively identify more orders of modal parameters. The errors of vibration modal shapes and natural frequencies identified by equalization PCA are allowable.
Virtual Simulation of Fruit Picking Robot Based on Unity3D
Authors: Wang Chenglin , Liu Suchwen , Zhao Lijun , Luo Tianhong 
Published: September 2020

Abstract
With the development of the fruit farming industry, there have been breakthroughs in both scale and harvesting requirements, and the resulting problem is that the demand of picking robot is more and more high, the function, cost and quality of the harvest and picking efficiency compared with the traditional manual operation mode with strong competitiveness for market, that is to say, in terms of cost cheaper, picking at a faster rate, and can avoid damage on the fruit in the process of picking. Therefore, in terms of mechanical structure design, the transmission accuracy and efficiency of the picking robot should be improved. At the same time, the structural design of the picking robot should simplify the structure as much as possible, reduce the manufacturing cost, and ensure the feasibility of the picking robot’s functions. Picking robot combined with automatic walking system, automatic detection system and control system-centered intelligent system, can achieve accurate and efficient work in various conditions. Realize picking robot toward automation, intelligence, scientific development. The design of this topic is mainly aimed at the development of the picking robot and the use of analysis, determine the feasibility of the picking robot design and virtual simulation.
Chinese Sentiment Analysis of Online Education and Internet Buzzwords Based on BERT
Authors: Zheng Jibin , Wang Jinhai , Ren Yafeng , Yang Zeming 
Published: September 2020

Abstract
In recent years, sentiment analysis has made a great breakthrough in the field of natural language processing. Many researchers attempt to build a user comment analysis model of e-commerce products to infer the quality of goods. However, the application of sentiment analysis technology in online education platform and intelligent feedback to students and managers is almost irrelevant. In this paper, we collected a large number of online education platform web resource reviews to build a large corpus to pre-train the Word2vec model for comparative experiments. At the same time, the BERT+BiGRU model is designed for the Feedback System Structure (FSS), which achieves the effect of intelligent interaction between students and teachers and the improvement of the curriculum. On the self-built data set, the BERT+BiGRU model has the best prediction effect, with an accuracy of 98.82%. Compared with traditional machine learning methods such as Naive Bayes, the classification accuracy rate is improved by 21.54%. The experiment result shows that we use BERT+BiGRU on the FSS system to achieve the effect of teacher-student interaction and intelligent feedback of academic intelligence. In addition, we also carry out sentiment analysis on the text with Chinese Internet buzzwords. The experimental results show that the sentiment analysis model of our intelligent feedback system (FSS) has strong analytical capability.
Transmission Line Scene Classification Based on Light-VGGNet
Authors: Wang Hongxing , Huang Zheng , Liu Bin , Huang Xiang , Han Wei , Li Hongchen 
Published: September 2020

Abstract
In recent years, power departments have gradually utilized UAV to carry out regular inspections of transmission lines to ensure the safety and stability of power systems. However, a large number of images without useful information are usually captured during the UAV inspection. These images that contain no transmission line information are transmitted to the ground station with informative images, which leads to the surge of workload. To solve this problem, we propose an intelligent image filtering method based on the VGGNet, which is called Light-VGGNet. Firstly, this paper collects the aerial images captured by cameras during the UAV inspection and builds an aerial dataset of transmission line scenes. Then, the Light-VGGNet is proposed, which achieves much lower memory consumption and faster running speed than those of the VGGNet. We use the aerial image dataset to train the proposed network. Finally, the best weight is loaded and utilized to predict the label of the test dataset.
Prioritized Experience Replay in Multi-Actor-Attention-Critic for Reinforcement Learning
Authors: Fan Sheng , Song Guanghua , Yang Bowei , Jiang Xiaohong 
Published: September 2020

Abstract
Experience replay is a significant method of off-policy reinforcement learning (RL), which makes RL reuse the past experience and reduce the correlation between samples. Multi-Actor-Attention-Critic (MAAC) is a successful off-policy multi-agent reinforcement learning algorithm, due to its good scalability. To accelerate convergence, we use prioritized experience replay (PER) to optimize the experience selection in MAAC, and propose the PER-MAAC algorithm. In the PER-MAAC, the priority metric is based on the temporal-difference error during training. The algorithm is evaluated in the scenarios of Multi-UAV Cooperative Navigation and Rover-Tower. The experimental results show that PER-MAAC improves the speed of convergence.
Machine Fault Detection under the Background of Strong Interference Based on Line Learning Music Algorithm
Authors: Jiang Yi , Chen Eryang 
Published: September 2020

Abstract
First, this article introduces the importance of fault diagnosis in social production, and analyzes many existing machine fault detection methods. Considering the use conditions of the algorithm, these methods may be complicated and may not be suitable for online applications. Especially for the background of strong interference, this phenomenon occurs in the early stage of machine failure, and the traditional weak signal detection method loses its function. The characteristics of random noise modulation are given, and then an improved online learning evolutionary MUSIC algorithm is designed. First, it constructs a data array based on the sampling sequence, and then analyzes the signal spectrum under the condition that the number of signals is underestimated. Because traditional analysis methods are limited to current data, it is difficult to obtain stable analysis results under strong interference. Finally, some historical analysis data is weighted to obtain the true spectrum. The effectiveness of the algorithm can be seen through simulation. For weak fault signals under strong interference background, this method has better detection performance than FFT algorithm.
Design of the Segmentation Algorithm of HCC Based on the Improved U-net Network
Authors: Zhou S , Li W Y , Lin R 
Published: September 2020

Abstract
Computed tomography is an important method for clinical evaluation of the disease status and the therapeutic efficacy of hepatocellular carcinoma (HCC). In order to effectively reduce the workload of artificial segmentation of CT images and to minimize the obvious influence of subjective factors upon the result, a segmentation algorithm based on the improved U-net network and region growing was proposed. Median filtering and adaptive histogram equalization were performed firstly on abdominal CT images with HCC, secondly the CT images were trained by the improved U-net network in which residual structure and deep separable convolutions layer were used, and then region growing was used for capturing edges of CT images. 126 groups of target CT images were selected for analysis, which were divided into training set, verification set and test set according to the ratio of 8: 1: 1, of which the training set was expanded to 800 by image enhancement technology. By comparing the lesion regions extracted with handled consequences manually labelled by clinicians, the sensitivity reached 83.18%, the accuracy 85.25%, and the Dice coefficient 83.05%. The algorithm revealed a good result on the segmentation of CT image with HCC, and could provide support for the evaluation of the treatment efficacy of HCC.
CMS System Identification Based on Improved Algorithm of Apriori
Authors: Liu Zhonglin , Wang Yan 
Published: September 2020

Abstract
Web applications on the Internet are often attacked by various vulnerabilities. Once various vulnerabilities are published, it becomes more and more important to quickly and accurately locate the affected Web application or system from the list of website resources. In this paper, based on the data and information of similar CMS websites, the improved Apriori algorithm is used to extract the features of CMS websites and their association rules, which can be used to quickly identify whether unknown websites are CMS templates of this type. Through experimental tests, four feature information used to identify WordPress is obtained. According to these features and relevance, the system type website can be identified more accurately.
An Improved Bacterial Foraging Algorithm for Multi-modal Problems
Authors: Hu Jianjun , Jin Nianfu , Li Pu , Zhang Huimin 
Published: September 2020

Abstract
In order to improve the performance of BFO (bacterial foraging optimization) algorithm in multi-modal problems, the algorithm is optimized and improved in this paper. In the chemotaxis operation, the gradient accumulation variable swimming step size is realized, and the convergence speed of the algorithm is accelerated. In the reproduction operation, a new population competition mechanism is added, and a double-index sorting is realized, which improves the ability of the algorithm to jump out of the local optimum and the diversity of the population. In the migration operation, the vitality index of bacteria is added to dynamically affect the migration probability, ensure the survival probability of elite individuals, and further improve the global optimization ability of the algorithm. Finally, a multi-modal function is selected to test. Compared with the genetic algorithm, the improved BFO algorithm is better than the genetic algorithm in speed and quality.
Research on the Influence of Improved K-shell Algorithm on Commodity Profit
Authors: Chen Jing , Huang Jincheng 
Published: September 2020

Abstract
In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN (Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities.
The Chaotic Slime Mould Algorithm with Chebyshev Map
Authors: Zhao Juan , Gao Zheng-Ming 
Published: September 2020

Abstract
In this paper, we proposed an improvement for the newly raised swarm-based algorithm called the slime mould algorithm (SMA) with chaos. The so-called chaotic SMA introduced the specific Chebyshev mapping, which had already been verified to perform better in optimization. Three types of simulation experiments were carried out with the unimodal, multi-modal benchmark functions and those which have basins/valleys in their profiles. In order to reduce the influence of randomness involved in the algorithms, 100 Monte Carlo experiments were carried out and the final results were their averages. Results confirmed the capability of the improvements and demonstrated that the chaotic SMA with Chebyshev map would perform better, steadier, and faster than the original one in optimization. Discussions on the capability in optimization of the chaotic SMA together with the original SMA were made, and the chaotic SMA was recommended in applications for real engineering problems.
Accurate ECG Data Generation with a Simple Generative Adversarial Network
Authors: Wang Haiyan , Ge Zhaoyang , Wang Zongmin 
Published: September 2020

Abstract
Since ECG data is highly sensitive medical data, the acquisition of ECG data is highly restricted. However, with the increasing demand for ECG big data research, the improvement of computer computing capabilities, and the development of deep learning, the direction of ECG intelligent analysis is facing a serious lack of standard clinical data. In order to generate more precise ECG data, this paper proposes a GAN architecture for generating ECG heartbeat data. The network structure is simple and does not require any domain knowledge. In this paper, the MIT-BIH Arrhythmia database is selected, from which all left bundle branch block heartbeats are selected to form a training dataset. The training process shows that the proposed GAN structure is effective and accurate, and the generated results show that not only the generated simulated ECG heartbeat data is diverse, but also highly similar to the real data.
A Comparison of Four Algorithms for Land-Use Classification Based on Landsat 8 OLI Image
Authors: Zhang Yanghua , Zhao Hu , Ni Jianbo 
Published: September 2020

Abstract
Accurate mapping and monitoring of land-use is essential for reasonable land management and planning. To extract land-use classes based on remote sensing images, many classification algorithms have been proposed. However, the comparison between some main supervised classification algorithms is rarely researched. This study selected the eastern fringe area of Jinan as the study area and the Landsat 8 OLI image of 2019 as data to compare the performance of four supervised classification algorithms that are MLC, SVM, ANN and RF especially. The results shown that the overall accuracy and kappa of RF is 86.2% and 0.8, and the overall accuracy and kappa of SVM is 83.2% and 0.75, and the overall accuracy and kappa of ANN is 81% and 0.72. The overall accuracy and kappa of MLC is 73.6% and 0.63. These denote that the RF can achieve the best classification result in four algorithms, followed by SVM, ANN and MLC.
Real-Time Recognition Algorithm of Vehicle External Specific Sounds Based on Spectrum Characteristics
Authors: Lei Hongbin , Wang Botao 
Published: September 2020

Abstract
As the development of vehicle sound insulation technology, drivers ignore some external sound information regularly. It is particularly important for the recognition of external sounds when the vehicle is driving. The real-time detection of the fundamental frequency of the external sound is not accurate due to the complexity of the external environment and the noise interference. In this paper, a recognition algorithm of vehicle external specific sounds is presented. The spectrum information of the external sound is converted into an image form, and the analysis result is obtained after processing the spectrum image. We choose five different types of sounds and the total number of 1000 samples for testing. The experiment data shows that the overall accuracy of the algorithm’s processing data can reach more than 90%, and the recognition rate can reach more than 90%. This algorithm has the characteristics of high accuracy and strong robustness, and it can easily be realized in real time.