bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on functional safety inspection method for functional safety of escalators in service
Authors: Zhang Xupeng , Yong Zhao 
Published: July 2021

Abstract
As a new technology, functional safety technology has been widely used in escalators. Functional safety inspection is the basis of escalator maintenance operations. This paper introduces the distribution and application of escalator functional safety, proposes a functional safety inspection method for escalator functional safety based on functional safety theory, and constructs a technical framework for escalator functional safety maintenance operation guidance, including data integrity, working environment, and daily inspection, Sensors, cabinets and control panels, wires and connectors, etc. and inspection methods. On this basis, taking the overspeed inspection of the escalator as an example, the escalator function safety maintenance operation guidance technology is explained.
Parallel Large-scale MOSFET Circuit Simulation using Multi-core CPU and Time-saving Techniques
Authors: Chen Chun-Jung , Yang Tai-Ning , Lee Chih-Jen , Chou Li-Ping , Hsu Sheng-Hsuan , Chang Allen Y. , Liu Chung-Hsin 
Published: July 2021

Abstract
In this paper, we investigate the acceleration properties of fine-grain parallel large-scale MOSFET circuit simulation using multi-core CPU. Several CPU have been tested for understanding maximum possible accelerations. A parallel circuit simulator, MOSTIME, is used to simulate experimental circuits. Performing tests on CPU and MOSTIME, we find that more advanced accelerations on large-scale fine-grain parallel circuit simulation would rely on time-saving software techniques and GPU (Graphic Processing Unit) cards. A newly implemented time-saving technique for MOSTIME has been tested to show promising effects.
Pierson-Moscowitz spectrum simulation based on the rough sea surface
Authors: Xiu Menglei , Li Lihua , Wang Yongbin , Wang Longfei , Hou Wenda 
Published: July 2021

Abstract
By analyzing a variety of traditional wave spectrum models, combining the actual wavelength and wave surface displacement to compare the electromagnetic field propagation process, the P-M (Pierson-Moscowitz) spectrum is used to theoretically derive the electrical characteristics of the rough sea surface and its influence on the propagation process of high-frequency electromagnetic waves. Based on the bilinear superposition algorithm and Matlab numerical calculation software, the electrical characteristics of rough seas and the theoretical results of electromagnetic wave propagation in rough seas are simulated and verified. The results show that the modeling of rough sea surface based on P-M spectrum has the advantages of high reducibility and stable frequency spectrum.
Accurate Aspect Ratio Estimation in Scale Adaptive Tracking
Authors: Lu Weikang , Su Ang , Zhang Wenlong , Shang Yang 
Published: July 2021

Abstract
In visual object tracking, robust scale estimation is always a challenging problem. Because of the fast movement of the target, the background, the relative distance and relative azimuth angle between the target and the background usually change greatly. However, traditional correlation filtering tracking method cannot track the target accurately or even lose the target when the target scale and aspect ratio change. To address these problems, this paper adds two independent filters for the length and width scale estimation to complete the adaptive tracking on the basis of the translation filter. The experimental results in the public data set show that: compared with the original trackers, our method improves the tracking accuracy significantly, and meets the needs of real-time target tracking.
A revocable attribute-based access control system using blockchain
Authors: Liu Xin , Zheng Yue-guang , Li Xu-zhou 
Published: July 2021

Abstract
Attribute-based access control is an effective cryptographic mechanism that allows a data owner to perform attribute-level access control over users’ access capabilities. In the application background of blockchain, it is urgent to use attribute-based access control to solve a series of security problems. Although there exist several similar schemes, they do not consider attribute revocation. By adopting the attribute revocation technique based on binary trees, we extended Waters’ ciphertext policy attribute-based encryption (CP-ABE) scheme into a revocable CP-ABE (RCP-ABE) scheme. By combining our RCP-ABE with blockchain, we constructed a revocable attribute-based access control system. The new system supports expressive access control policies and allows the attribute authority to revoke users’ attributes or part of their attributes. The security analysis shows that the new system satisfies several ideal properties, i.e., forward security, backward security, confidentiality, and integrity.
Application of State Transition Simulated Annealing Algorithm in Community Detection
Authors: Qin Xiaowei , Han Xiaoxia 
Published: July 2021

Abstract
Community detection is one of the most important attributes to reveal the hidden structure of complex networks. The way of community detection based on the intelligent optimization algorithm has been widely used. Aiming at the initialization problem of the solution, the paper uses the density peaks clustering (DPC) algorithm to obtain a stable and high-quality solution. Aiming at the search strategy, state transition simulated annealing algorithm (STASA) is proposed. On the basis, three types of operators are designed: vertex replacement operator, community fusion operator and cross mutation are designed. In the initial optimization stage, the vertex substitution operator is used to generate diverse solution. The community fusion operator speeds up the optimization in a certain sense. The introduction of the cross mutation operator in the later optimization stage is only suitable for high-quality solution and enhances the local search ability of the algorithm. Finally, the experimental results on the GN benchmark and the real-world networks show that the algorithm is superior to other classic community detection algorithms in terms of stability and accuracy.
Sound source localization based on improved adaptive beamforming
Authors: Xu Keni , Liu Wenhong , Wang Mianmian 
Published: July 2021

Abstract
In adaptive systems, the least mean square error (LMS) adaptive algorithm is widely used. The variable step size LMS algorithm (SVSLMS) based on the Sigmoid function has greatly improved the convergence speed, and the normalized LMS algorithm is stable Sexual performance has certain advantages. Therefore, improvement is made on this basis, the SVSLMS algorithm is normalized, and the iteration speed is increased by changing the iteration factor, and the stability of the algorithm is also improved. This paper uses MATLAB simulation tools to verify the superiority of the new algorithm. Finally, the improved algorithm is applied to beamforming to estimate the azimuth of arrival of the simulated sound source signal at the partial discharge point of the submarine power cable.
A GM-CPHD Filtering Algorithm Assisted by Luminance Information
Authors: Li Yue , Sun Quan , Wang Dongya , Huang Jian , Li Pengyuan , Wang Yupeng , Niu Zhaodong 
Published: July 2021

Abstract
Optical tracking of small and weak targets in space, has problems such as a large and unknown number of targets, dense clutter, close measurement of neighboring targets, and false detection and missed detection. In this paper, a Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filtering algorithm assisted by luminance information is proposed. By establishing the luminance likelihood function, the luminance information is introduced into the CPHD filtering process, and the Gaussian mixture implementation method is given. The simulation results indicate that the proposed algorithm can effectively adapt to the dense clutter environment, and compared with the traditional GM-CPHD filtering algorithm, its tracking accuracy for multiple targets is significantly improved.
An improved Simhash algorithm based malicious mirror website detection method
Authors: Chen Guangxuan , Chen Guangxiao , Wu Di , Liu Qiang , Zhang Lei , Fan Xiaoshi 
Published: July 2021

Abstract
There are a large number of similar or even identical webpages on the Internet. These webpages will cause unnecessary loss of network resources, including waste of storage space, decreased web search speed, and decreased user experience. And some malicious mirror websites will become tools for criminals to carry out illegal activities such as phishing attacks. In this paper, the autours analyzed the mainstream text similarity detection algorithms and webpage deduplication algorithms, and proposed an improved webpage deduplication algorithm based on Simhash. The algorithm converts the text collection into Simhash fingerprints for storage through mapping, and calculates the similarity of the two fingerprints through Hamming distance, thereby obtaining the similarity of the webpage. Experiments show that the algorithm proposed in this paper has a higher accuracy rate and recall rate, and can be better applied to the identification and detection of malicious mirror websites.
An Improved Denoising Method for Optical Fiber Signal
Authors: Guoyou Wang , Weijin Xu , Zhipeng Wang , Shunjie Han , Hong Jiang , Tianqi Lu 
Published: July 2021

Abstract
In the large-capacity FBG sensor network demodulation system, the process of collecting the light source is often accompanied by noise interference. Signal denoising is an important factor that affects the accurate demodulation of the FBG sensor system. Aiming at the problem of constant deviation of soft and hard threshold functions in denoising of traditional wavelet threshold algorithm and the shortcomings of modal aliasing easily in empirical mode decomposition, a modified semi-soft threshold-VMD denoising algorithm is proposed. The simulation results show that the function in this paper is better than the traditional threshold function in continuity and signal reduction degree, and has achieved good results.
Research on Gear Signal Fault Diagnosis Based on Wavelet Transform Denoising
Authors: Li Yuan , Wang Zhuojian , Li Zhe , Li Hao 
Published: July 2021

Abstract
As an important part of modern machinery, gears are safe and reliable directly related to the normal operation of the mechanical system, and when the gears are abnormal, it is very important to carry out fault diagnosis in time and effectively. Wavelet transform is effective in signal time-frequency analysis and fault diagnosis. Based on the wavelet analysis method, this paper first filters and de-noises the vibration signal data of a certain type of gear, and then performs fault analysis. Time-frequency analysis of time-domain signals has a better effect on gear fault diagnosis.
Research on the method of data architecture construction for joint intelligent simulation
Authors: Li Suike , Zhao Yongchao , Wang Haijun 
Published: July 2021

Abstract
In view of the data problems restricting the joint intelligent simulation, this paper constructs a data classification system. By mining and sorting the data, rules and models in cases and experiences, and combining with the basic data and ability data of the relevant system simulation model, it carries on the digital and systematic annotation, constructs the conceptual model, establishes the segmentation and part of speech standards, and adopts the methods of concept mining, attribute mining and instance mining. The data system of joint intelligent simulation is established by mining and instance mapping, and the multi-layer logical template and intelligent matching rules are used to solve the data problems of joint intelligent simulation.
Pedestrian Detection Method Based on Deep Convolution Neural Network
Authors: Mengmeng SHEN , Yong WANG , Jiaqi MA , Chuanguo LI , Liangbo HE , Barnawal Gaurav , Wei SHAN 
Published: July 2021

Abstract
Compared with the traditional pedestrian detection technology, the pedestrian detection technology based on deep learning has achieved overwhelming advantages. However, due to the large scale of deep convolution network, the demand for dedicated processor limits the popularization of pedestrian detection system. To solve these problems, this paper proposes a deep convolution network model with moderate network scale, which improves the universality of the detection model on the premise of ensuring the detection accuracy. Based on the low dimensional shallow convolution neural network, the optimal network structure is found from three aspects of network layer number, sensor field size and characteristic graph, and the final network parameters are determined by guiding experimental evaluation. The pedestrian image to be detected is input into the above network model, and the pedestrian detection result is obtained. The pedestrian detection results on several popular pedestrian databases such as Daimler, MIT and INRIA show that the network structure designed in this paper not only has moderate network size, but also has good detection performance. Cross experiments also verify the robustness and generalization of the algorithm. The work of this article is funded by the undergraduate training program for innovation and entrepreneurship (202010373050, 202010373051, 202010373046).
A denoising algorithm for linear frequency modulation signal based on window function and empirical mode decomposition
Authors: Zhang Kuo 
Published: July 2021

Abstract
The linear frequency modulation signal is widely used in radar, sonar detection and communications as a typical non-stationary signal. In order to better analyse the linear frequency modulation signal received by the receiver, it is necessary to perform denoising processing on the received signal. As a new type of time-frequency analysis method, empirical mode decomposition algorithm is very suitable for processing non-stationary signals. However, due to the large bandwidth of the linear frequency modulation signal, more noise will be retained when the traditional empirical mode decomposition algorithm is used to denoise the linear frequency modulation signal. Under above background, this paper proposes a denoising method based on the window function and the empirical mode decomposition to achieve further denoising of the linear frequency modulation signal. According to the simulation results, when the signal-to-noise ratio is about 0dB, the method proposed in this paper improves the signal-to-noise ratio by about 1.5dB compared with the traditional empirical modal decomposition algorithm.
Improved Harris Corner Detection Algorithm Based on Canny Edge Detection and Gray Difference Preprocessing
Authors: Luo Chuan , Sun Xiaoliang , Sun Xiangyi , Song Junyao 
Published: July 2021

Abstract
Harris corner detection algorithm has been widely used in many computer vision allocations. However, it has low efficiency and accuracy, poor noise immunity and needs to set an artificial threshold. In this paper, an improved algorithm based on Canny edge detection and gray difference preprocessing is proposed. Firstly, Canny edge detection and gray difference preprocessing are used for corner prescreening to improve the detection efficiency, anti-noise, and rotation invariance. Secondly, non-maximum suppression is applied to the screened corners to reduce the number of false corners. Finally, the average of adjacent points method is used to solve the problem of corner cluster, and the detection results are compared the measurement accuracy is improved to sub-pixel level. Experimental results indicate that the proposed algorithm can accurately extract the corners in the image and remove the false corners and corner clusters. It achieves superior performance than existing methods.
Research on CO-word network topic mining and topic differences based on haze microblog data
Authors: Lu Haiyue , Rui Xiaoping , Li Runkui 
Published: July 2021

Abstract
Some studies have shown that haze not only poses a threat to people’s health, but also affects the secretion of human hormones, making people depressed and endangering mental health. Microblog has the advantages of short content, rapid communication and convenient interaction. When the haze comes, a large number of topic microblogs related to the haze will be generated. Mining the topics of concern and psychological reactions contained in these microblogs is helpful for resource allocation and public opinion publicity in the case of haze. At present, the research of microblog topic mining in haze situation only involves a single research area, and few studies discuss the spatial differences of different regions. Based on this, this study collected the microblog data of seven provincial capitals in the severe haze areas in 2017, and used the community-based co word network method to complete a series of experimental steps, such as keyword extraction, co-occurrence matrix construction, co-word network construction and topic community detection. On this basis, we detected the topic community in the microblog data set, and analyzed the horizontal differences of topics in different cities. The results show that different cities have not only the same but also different concerns about haze. The results can provide theoretical guidance for the healthy development of cities.
Semantic query expansion method based on pay-as-yougo fashion for graph model
Authors: Wu Nan , Pan Ying 
Published: July 2021

Abstract
The search requirements of users are usually vague and unclear, and the traditional keyword query method is difficult to obtain satisfactory query results. Therefore, the semantic query expansion technology uses semantic information to modify and extend the initial search requirements of users, to obtain more comprehensive and accurate query results. However, the construction cost of the extended query graph is high, and the existing research is difficult to adjust and optimize the user’s query demand in time, resulting in a poor query effect. To solve the above problems, this paper proposes a semantic expansion method based on pay-as-you-go fashion for graph model: Firstly, the initial query graph is constructed according to the user’s search requirements, and the semantic similarities between the search requirements and knowledge bases are calculated; Then, the extended sets of attribute-values and edges are generate by sorting the similarity value in descending order; Finally, all or part of the elements in the extended set are combined to generate the extended query graph. This method can dynamically adjust and optimize the initial query based on the relevant semantic information in time, and improve the query efficiency and effect.
Identification of Class number of ship radiated noise based on RK-Means algorithm
Authors: Ding Yuanming , Liu Lijia , Yang Yang 
Published: July 2021

Abstract
Ship radiated noise recognition is one of the basic problems in underwater target recognition. To identify the number of ship types according to ship noise, the ship scale can be predicted in advance and the next step of processing can be carried out. Aiming at the problem of identifying the number of ship types based on ship radiated noise, a RK-Means (Recurrent K-Means) is proposed in this paper. According to the short-time energy characteristics of the target signal, the dimension of the feature data is reduced by principal component analysis; The K-Means algorithm is used to cluster and identify the dimension-reduced signals through cyclic iteration; A single connection algorithm is used to determine the appropriate clustering center, which reduces the similarity value of data objects between classes and makes the difference value between classes large. Furthermore, the algorithm can automatically determine the K value, which is the number of ship types. Verified by the experimental data, compared with the contrast algorithm, the K-value recognition result and clustering effect of the RK-Means clustering algorithm are better.
Spectrum Reconstruction Algorithm based on Resource Partition Allocation in Elastic Optical Networks
Authors: Wu Lingyue , Guo Pengfei , Du Li 
Published: July 2021

Abstract
In order to solve the problem of spectrum fragment in elastic optical networks, a spectrum reconstruction algorithm based on resource partition allocation is proposed in this paper. Services are classified according to the different frequency of the services that occupy different number of frequency slots in the networks. Region division of spectrum resources is made according to the type of services and services are allocated in the corresponding spectrum partitions. When defragmenting in the networks, we reconstruct the spectrum for different regions with comprehensively considering the factors in time domain and frequency domain, integrating current resources and preventing the generation of spectrum fragment in the future. Simulation results show that the algorithm can reduce the degree of network fragmentation and reduce traffic blocking.
Color space conversion algorithm and comparison study
Authors: Bi Zhicheng , Cao Peng 
Published: July 2021

Abstract

Purpose The advantages and disadvantages between various conversion algorithms are analyzed, and the relatively optimal conversion algorithm is obtained by comparing color difference, conversion accuracy, and operability. Method Using 729 sets of data from the K = 0% part of the ECI2002 standard color target as samples, experiment 2° was conducted under the D50 light source in the field of view, and the minimum, average, and maximum color differences of C, M, and Y were analyzed and compared by three unused color space conversion algorithms, and the operability of the three algorithms was also analyzed. Phenomenon Of the three algorithms, the polynomial regression algorithm and the neural network algorithm have similar errors, while the Nünchberg equation is less accurate and has a larger error. The operability of each of the three algorithms has advantages and disadvantages. Conclusion The polynomial regression algorithm has the highest accuracy, the lowest error, and the best method.
The research of multi-target tracking based on improved YOLOv3
Authors: Xu Jiawen , Lu Xinbiao , Zhang Chi 
Published: July 2021

Abstract
Multi-target tracking based on detection is a hot topic in the field of computer vision. Aiming at solving the problem of inconvenient application of large network model, this paper proposes a multi-target tracking algorithm based on improved YOLOv3. We use MobileNetV3 to replace the deep network as the backbone of YOLOv3. Compared with Faster R-CNN and YOLOv3, the model of MobileNetV3-YOLOv3 is greatly reduced and the detection speed is improved. Compared with the MobileNetV1-YOLOv3, the accuracy is improved and FPS is higher. The improved YOLOv3 model is used in DeepSORT algorithm for multi-target tracking. The experimental results show that the algorithm used in this paper has the best detecting and tracking effect.
Geographical distribution of liver cancer incidence based on big data analysis
Authors: Tian Yuchi , Huang Qian , Jiang Xiaogang , Chen Guojie , Zheng Chongwei , Yang Xiaodong 
Published: July 2021

Abstract
The liver cancer has become the sixth most common cancer and the second leading cause of cancer deaths worldwide. The incidence of liver cancer has characteristics of the geographical distribution, which vary from region to region. Based on the data from Cancer Incidence in Five Continents, this study analyzed the spatial distribution of liver cancer globally and explored the effect of meteorological factors of surface temperature and wind field on incidence of liver cancer, to find the geographical distribution characteristics of liver cancer and the impact of geographical environmental factors on the incidence of liver cancer, which will be of great significance to the epidemiological study of liver cancer and the prevention of liver cancer. The results show that the humid and hot oceanic climate may be conducive to the development of liver cancer. The environmental factors play an important role in the occurrence of liver cancer.