bluuebunny/crossref_metadata_2025_split huggingface dataset:
Learning BiLSTM-based Embeddings for Relation Prediction in Temporal Knowledge Graph
Authors: Ma Sixia , Li Aiping , Zhao Xiaojuan , Song Yichen 
Published: April 2021

Abstract
At present, as the knowledge graph continues to increase, problems such as incomplete information in the graph also emerge, mainly reflected in the lack of links between entities [1], and the current commonly solution is to learn their representations that can represent more semantic information for the entities and relations in the graph. This article focuses on the temporal knowledge graph (TKG), consistent with the goal in the static knowledge graph, this paper tries to propose a better representation learning method to solve the link prediction problem. According to the time information in the temporal knowledge graph, the paper constructs a bag of words about time, uses the bag of words model to decompose the time in the temporal knowledge graph into sequences, and learns its semantic information together with relations and entities. The embeddings obtained in this way can learn more semantic information. In addition, proving the effectiveness of the model through experiments on the currently commonly used temporal knowledge graphs.
An exponential smoothing channel estimation method for MIMO-OFDM system
Authors: Yan Guo , Sheng Su 
Published: April 2021

Abstract
In order to solve the problem of inaccurate channel estimation of least square (LS) algorithm for multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system under time-varying channel, a novel channel estimation method using exponential smoothing algorithm (ESA) is proposed in this paper. Based on LS estimation algorithm and ESA theory, the proposed method fully exploits the variation law of time-varying channel, adopts the predicted results of the previous time to predict the estimation value of the current time by iterative computations, and utilizes the low-pass character of the ESA equivalent filter to reduce the random disturbance. Therefore, the additive white Gaussian noise (AWGN) existing in LS algorithm is effectively suppressed and the estimation accuracy is further improved. Simulation results show that, compared with the traditional channel estimation approaches, the proposed method can provide high estimation accuracy with low computational complexity, especially under time-varying channel and low SNR conditions..
Research on the method of multi-station integrated data center module location based on fuzzy analytic hierarchy process
Authors: Chen Zhuo Lin , Yin Yuan , Lin Xiao Ning , Sen Li Yang 
Published: April 2021

Abstract
Internet data center module(IDC) is an important part of the multi-station integrated substation, and its site selection will be an important basis for the site selection and construction of the multi-station integrated substation. In order to be useful to the practical engineering, the fuzzy analytic hierarchy method is proposed to analyze the location of IDC. There are six factors influenced in total which land price, policy environment, the annual average temperature, industrial electricity, network environment and peer development. FAHP has been adopted for calculating and sequencing priority membership value for every single factor thoroughly. According to the factor, different priority location has been chosen. Finally, based on the fuzzy analytic hierarchy process, the paper proves the effectiveness and convenience of the site selection method, through analysis of the three regions.
Research and Realization of Grouping Collection Algorithm of Infrared Cage Heating Circuit for Spacecraft Vacuum Thermal Test
Authors: Dong Hao , Xie Jihui , Liu Yang , Shen Bin 
Published: April 2021

Abstract
In the process of spacecraft thermal test, the infrared cage is mainly used to simulate the heat flow outside the space. In the design, the infrared cage is divided into several heating zones corresponding to the spacecraft. The heating strip of the infrared cage is divided into heating circuits, the heating circuits are grouped and collected, and the infrared cage heating node table is defined as an important basis for controlling the infrared cage. At present, there is a single method of grouping and gathering heating circuits, which is mostly limited by manual adjustment based on experience and lack of automation. In order to solve the shortcomings of the existing technology, this paper adopts the genetic algorithm hybrid BFD based on reinforcement learning to group and collect the heating circuits, and optimize the design results according to the constraints. The user interaction interface is designed to facilitate the user to do further analysis. Thereby improving the level of business automation, optimizing working procedures and reducing costs.
Research on Person Reidentification Method Fusing Direction Information in Multi-camera Pedestrian Tracking Problem
Authors: Liu Junliang , Shi Xiangbin 
Published: April 2021

Abstract
In multi-camera pedestrian tracking, pedestrian apparent features are usually used to solve the problem of cross-camera reidentification. In order to make the pedestrian apparent features robust to the directional changes of pedestrians in different cameras. In this paper, a pedestrian feature extraction method combining directional features is proposed by using deep network Resnet 50. Pedestrian feature extraction network is trained by adding pedestrian direction information. in order to improve the performance of person reidentification model, BNblock structure is added in the design, a batch normalization (BN) layer is added after the feature to get the normalized feature. It can make the triplet loss converge with the convergence of the ID loss, thereby improving the performance of the model. The proposed method was verified on DukeMTMC and Market-1501 data sets, and the results show that the pedestrian appearance features combined with direction information proposed by the pedestrian reidentification algorithm can significantly improve the reidentification accuracy. By adding BNblock into the network structure, the accuracy can be further improved.
Electromagnetic analysis for complex satellite targets using time-frequency representations
Authors: Sun Xumin , Guan Yongsheng 
Published: April 2021

Abstract
An analytical method for electromagnetic scattering characteristics of complex satellite targets by using multilevel fast multipole algorithm (MLFMA) and short-time fourier transformation (STFT) is proposed in this paper. By constructing different electromagnetic models of satellite target with multi-scale structures and coatings, the aspect and polarization characteristics of radar cross section (RCS) and time-frequency representation (TFR) are studied. To validate the influence of the multi-scale structure and coating, the full-polarization RCS at specific angles, RCS statistical characteristics and TFR correlation are simulated and compared. It is concluded that the multi-scale structures and coating materials should be sufficiently considered to calculate the accurate scattering characteristics.
Research and Improvement of Multi-granularity Method for Pedestrian Tracing System
Authors: Ni Ruixuan 
Published: April 2021

Abstract
In the field of video surveillance, with the continuous development of the Internet and artificial intelligence, the related technology for single device has been very mature, but how to accurately realize pedestrian tracing under cross-devices is still an active research field. In this paper, a multi-granularity pedestrian traceability method is proposed, which is based on the attribute extraction of pedestrian image and the person re-identification algorithm based on attention mechanism retrieves the same pedestrian image cross a lot of devices. The results show that the video surveillance system has good performance, which reduces the occurrence of tracking loss, and the track is visualized at the platform.
Research on Optimization Algorithm of auto-encoding neural network applied to rolling bearing fault diagnosis
Authors: Shi Shi , Tong Qingbin 
Published: April 2021

AbstractReal time, fast and batch processing of vibration signals has become a future development trend in the field of fault diagnosis, but data dimensionality disasters may arise. In view of the long running time of deep learning in the case of large samples, the gradient descent method and its variant algorithms are introduced for the loss function optimization problem, and the approximate optimal solution is solved in an iterative manner. The gradient descent method is used to minimize the loss function, and the research is carried out on the basis of MATLAB program implementation. The gradient descent method and its variant algorithm are applied to the rolling bearing fault diagnosis model for analysis. By comparing the algorithm’s convergence speed, loss value and accuracy of the rolling bearing fault diagnosis model, a relatively good optimization algorithm suitable for the rolling bearing fault diagnosis model is determined.
Channel Power Allocation Optimization Based on Water-filling Algorithm in 5G
Authors: Wei Song , Zheng Zige , Wu Chenwei 
Published: April 2021

Abstract
Since entering the information age, the popularity of mobile communication has become higher and higher, and people’s requirements for communication quality are also increasing, but the channel resources that people can control are limited. This paper proposes an optimization algorithm for channel resource allocation based on water-filling algorithm. Some improvements have been made on the basis of the original water-filling algorithm. The sgn function is used to optimize the transmit interference temperature limit (Sk), so that the screening result is not completely dependent on Sk, which improves the flexibility of channel screening and effectively optimizes the capacity of the communication network.
Research on Penetration Path Planning Method on Near Sea Bottom Based on the Sonar Detection Blind Zone
Authors: Yong-qi Gao , Peng Wang , Wei-qiang Ma 
Published: April 2021

Abstract
To improve the survivability and penetration probability of unmanned undersea vehicle(UUV) on near sea bottom combat missions, the factors leading to sonar detection blind zone are analyzed, and the detection blind zone produced by underwater terrain obscuration is mainly studied. A detection range calculation method based on DEM and acoustic ray is proposed, and the simulation study of the near sea bottom penetration path planning is carried out by using terrain blind zone and invasive weed optimization algorithm. Simulation results show the effectiveness of the proposed algorithm. This method can be applied to path planning of the UUV on near sea bottom penetration, which helps to improve Combat effectiveness and Operational effectiveness of UUV.
Design of Three Variable Camera Lens Control Circuit Based on C8051F311
Authors: Zeng Ziqiang , Wang Yuhan 
Published: April 2021

Abstract
A three variable lens control circuit based on C8051F311 is designed to solve some problems at the scene such as low precision or difficult quality control. In this system, the C8051F311 is used as the control, first a PWM signal is output, then the PWM signal through a RC filter circuit, finally through a signal processing circuit composed of comparator chip TL064 and amplifier IC TDA2030A to control the brushless DC motor, based on above all, the control of the lens is realized. At the same time, the circuit parameters are designed by using Multisim to simulate the circuit. The practice shows that the system has simple structure, convenient speed regulation and stable operation.
The early prediction of AD evolution based on Trusted-LGBM
Authors: Hao Pan , Li Jiyun 
Published: April 2021

Abstract
In order to achieve the purpose of early diagnosis of Alzheimer’s disease, this study uses machine learning technology to process 14,659 original data from the public data set ADNI. First use the KNN algorithm to fill in the null value. Then a feature transformation method is proposed to transform regression problems into classification problems. Then this study calculates the trust level of each sample based on the feature correlation and the weight of null value, and proposes an improved LightGBM algorithm-Trusted-LGBM. Finally, the prediction results of the Trusted-LGBM algorithm are compared with SVM, XGBoost, and the unimproved LightGBM algorithm. The experimental results show that the Trusted-LGBM algorithm proposed by this research has a higher Fl-score (0.784) and AUC (0.91). It can be seen that the method proposed in this study can more effectively support the early diagnosis of Alzheimer’s disease.
Software-based online self-testing using bounded model checking for 3D network-on-chips
Authors: Ji Pengfei , Zhang Ying , Jiang Jianhui 
Published: April 2021

Abstract
Online testing has become essential for the reliable operations of manycore systems using three-dimension (3D) network-on-chip (NoC) fabric. This paper presents a software-based online self-testing solution using bounded model checking (BMC) for 3D NoCs. The proposed method implements BMC on the extended finite state machine of the 3D NoC and extracts leading sequences to triggerinternal functions. Next, it detects structural defects in each function activated by the leader sequence using constrained ATPG. Finally, it effectively organizes packets to test the 3D NoC and reduce test cost. Experimental results demonstrate that the proposed method achieves high fault coverage online with extremely lower test cost and outperforms existing methods.
Discussion on The Data Extraction Strategy Of FAQ System Based On Chat Records
Authors: Li Jia , Chen Xin 
Published: April 2021

Abstract
Q&A data based on chat records have one characteristic: customers ask, service answers. There is a large amount of knowledge between the questions and answers of customer service. By optimizing the Q&A extraction algorithm to extract knowledge, a very excellent Q&A library can be constructed, thus the accuracy of FAQ system is greatly improved. By analyzing the existing data, this paper cnosiders the extraction strategy of question answering from machine learning and non-machine learning respectively. Then We compares their performance from three aspects of precision, recall and Fl-score according to their different characteristics. In order to ensure the best classification performance, grid search and K-fold crossover are also used to test the optimized classifier performance. After selecting the optimal data extraction strategy, We developed a FAQ system use this strategy, the system results show that the performance is reliable.
Research on Movie Recommendation Algorithm Based on Stack De-noising Auto-encoder
Authors: Wang Lihong , Song Xiaoming , Cong Wanjuan 
Published: April 2021

Abstract
Focused on the problems that the randomness of the noise-adding operation in the de-noising auto-encoder (DAE), and the data matrix does not consider the impact of trusted users on the deep preferences of target users, this paper proposes a recommendation algorithm based on stack de-noising auto-encoder (SDAE) which integrates the preferences of trusted users. Firstly, the score vector is used as the input of the auto-encoder, and the mask vector is designed to train the potential preference of the target user. Secondly, the deep preference of the target user and the trusted user is obtained by the weighted fusion of the features of the two hidden layers of the auto-encoder. Thirdly, in order to reduce the impact of noise on the prediction accuracy, the cascaded auto-encoder model is constructed and trained according to the greedy training method layer by layer training. Finally, SDAE model is compared with other models on different data sets. The experimental results show that SDAE model has better recommendation performance.
Load-balanced routing algorithm for Network-on-Chip employing fair arbiter
Authors: Liu Lu , Yang Yanfei , Lei Qianqian , Wang Huhu , Lixun Song 
Published: April 2021

Abstract
Balanced load is one of the most important performance metrics for on-chip communication. To design flexible, adaptive load-balanced routing algorithms, an arbiter that alternated the priority order fairly is employed. With fair arbiter to decide the routing path for network-on-chip, balanced load can result in performance improvement in speed and capacity for both congestion-oblivious and congestion-aware routing schemes.
A deep learning model S-Darknet suitable for small target detection
Authors: Yang Miao , Shi Xiangbin 
Published: April 2021

Abstract
In order to solve the problem of low accuracy of small target detection in target detection, a small target detection model S-Darknet is proposed. The algorithm is designed based on the Darknet-53 network. First, a new backbone network is proposed, which fully extracts the feature of small objects and adapts to multi-scale detection. Then, in order to enhance the feature information of the target after the fusion, a feature enhancement module is added before each upsampling. Finally, the proposed algorithm was verified on the VOC2007, VOC2012 data sets and the actual data sets of railway freight locks. Experimental results show that this method has high detection accuracy under the premise of ensuring real-time performance.
Research on injection speed control algorithm of oral anesthesia booster based on dynamic pressure detection
Authors: Li Jingdong 
Published: April 2021

Abstract
Oral anesthesia booster is an automatic injection system that replaces manual precise injection and solves the problem of abnormal pain caused by traditional oral anesthesia injection. Through the analysis of the system composition and model, it is determined that the pressure at the output end of the motor collected by the system in real time mainly reflects the load torque of the motor, so as to obtain the dynamic pressure detection method. According to the results of comparison with the high and low pressure thresholds, the oral anesthesia booster is controlled to select an appropriate injection speed mode. In addition, the pressure detection process is described, and the dynamic characteristics of the system are simulated and analyzed, which proves that the speed response of the system is timely after the dynamic pressure changes. The system can automatically alarm for obstruction or leakage according to the monitoring results, remind the doctor to correct the pressure and needle insertion position applied to the handle, so as to maintain the most rational needle insertion position and pressure, reduce the patient’s pain, and improve the comfort of treatment.
Research on 3D global path planning technology for UUV based on fusion algorithm
Authors: Hu Zhiyuan , Wang Zheng , Yin Yang 
Published: April 2021

Abstract
Aiming at the global path planning problem of unmanned underwater vehicle (UUV) in three-dimensional environment, a fusion algorithm named PACO based on particle swarm optimization (PSO) and ant colony optimization (ACO) is proposed. In order to meet the needs of three-dimensional path planning, the grid method is used to model the actual marine environmental data. Besides, PSO and ACO are optimized respectively. Taking the path length as the measurement index, the simulation is realized by using MATLAB. The simulation results show that the initial path search ability of the fusion algorithm is significantly improved, and the effectiveness of the algorithm is verified.
Application of parameter estimation in Logistic model
Authors: Zhao Jing , Wu Gang , Zhang Fan , Zhang Xuan , Wang Haitao , Cao Xinyu 
Published: April 2021

Abstract
Firstly, Logistic model and parameter estimation are simply introduced. Secondly, we make some analysis and summary of various methods of solving Logistic model estimation, which have been put forward by scholars in recent years. These methods include: using Logistic model’s integral form, linearization, optimization method, numerical method, grey system dynamic model, population model to solve the modeling parameter identification method. What’s more, the practical applications of Logistic model are introduced such as the application in the process of urbanization.
Research on Computer Intelligent Proofreading System of Improved English Phrase Translation Model
Authors: Zheng Hairong 
Published: April 2021

Abstract
When carrying out English translation based on phrases and sentence patterns, the contextual coherence of phrases and sentence patterns is relatively poor, which will directly affect the effect of English translation. In the process of designing and improving the English translation computer intelligent proofreading system, we need to study the specific modules of the system design. At the same time, we need to improve the design level of the English translation computer intelligent proofreading system based on the improved phrase translation model. Only in this way can it be ensured that vocabulary can be proofread when translating English, and the translation effect can be improved. This is also the main goal to realize the design of an intelligent proofreading system for English translation. In the process of analyzing and researching the proofreading system, we need to analyze and compare the accuracy of the design system and proofread the incoherent context in the English translation results in time. This can give full play to the advantages of the intelligent angle system with high accuracy and contextual coherence.
Research on operation characteristics of UHV converter valve hall based on intelligent image processing and 3D modeling technology
Authors: Shiling Zhang 
Published: April 2021

Abstract
There are many power equipments in the UHV valve hall, including the converter transformer, converter valve tower, all kinds of bushing, tubular bus. The operation condition monitoring of the above main equipment mainly includes electrical performance parameter measurement, oil and gas insulation medium performance parameter measurement and other means. This paper mainly introduces a monitoring method for the operation status of converter valve hall from the perspective of image processing technology. The technology mainly includes: 1) the application of intelligent image processing technology to identify and classify the database of infrared thermal imager and ultraviolet imager; 2) the real-time on-line measurement for the insulation distance of typical fittings through Kalman filter technology, including the measurement of the hottest temperature The insulation distance between partial discharge point and zero potential grounding point; 3) the electric field simulation model of typical main equipment in valve hall is established based on three-dimensional modeling technology of finite element method, and the surface electric field distribution of key main equipment fittings is obtained. Combined with the image database information, insulation distance information and typical main equipment electric field distribution information, the operation state parameters are effectively obtained, and the intelligent algorithm is applied to automatically evaluate its operation state, discover the latent fault and locate the positive fault, so as to provide effective data support and the protection strategy for the main equipment operation and maintenance.