bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influence of gender on the use of kinship terms in English and Serbian
Authors: Tadić Dragana 
Published: September 2021

Abstract
Kinship terms are widely present in the English and Serbian language. However, research on their actual use and the role gender plays in the selection of those terms is fairly scarce. The purpose of this contrastive analysis is to explore the influence of gender on the use of kinship terms among the speakers of English and Serbian and to determine cultural conceptualizations underlying these terms. The study is corpus based. The data were collected by the means of questionnaires and analyzed statistically. Kinship terms represent a culturally constructed category reflected in the lexicon of these two contrasted languages. For this reason, Cultural Linguistics and its theoretical and analytical tools serve as the basis for the theoretical framework for this study. The results of the analysis have shown that gender significantly influences the use and the selection of kinship terms.
Short-Term Global Horizontal Irradiance Forecasting Using a Hybrid Convolutional Neural Network-Gate Recurrent Unit Method
Authors: Zhang Yu , Ma Jianfeng , Zeng Cong , Li Guangyao 
Published: September 2021

Abstract
The effectiveness of photovoltaic power generation systems—a clean and renewable use of solar energy—depends on the amount of solar radiation. Consequently, solar radiation forecasting (especially short-term) is crucial for photovoltaic plants. In this paper, a hybrid convolutional neural network (CNN) and gate recurrent unit (GRU) method is proposed for short-term (10-min) solar radiation forecasting based on image and time-series data (i.e., radiation data at different times). The method aims to achieve high performance in solar radiation forecasting, which can be useful for PV plant adjustment. CNN–GRU consists of two branches. One is based on the ResNet-18 structure, which can extract features from sky images. The other is a GRU branch, which consists of three fully connected layers used for meteorological feature extraction. Experiments on a public dataset showed that our method predicts the mean absolute error better than other benchmark models. The ablation experiments demonstrated that the hybrid model performs better than a single model and, therefore, shows promise for application in solar radiation forecasting.
Ship Classification Method for Massive AIS Trajectories Based on GNN
Authors: Li Tianyu , Xu Huiqi , Zeng Weigui 
Published: September 2021

Abstract
Since criminals and maritime terrorism may tamper with AIS data and make the track suspicious, it is urgent to classify ships accurately and improve maritime navigation safety. Ship classification based on trajectory data can make up for the deficiency of traditional radar identification and optical identification which has important academic significance and practical value. The target recognition technology based on the traditional neural network can only process conventional Euclidean structure data, while the emerging graph neural network shows great advantages in processing non-Euclidean structure data. The ship trajectory data has the characteristics of the time and space domain and shows a non-Euclidean structure; therefore this paper proposes a classification and recognition method based on the graph neural network to process ship AIS data. First of all, the ship trajectory data is preprocessed and converted into graph data with vertices and edges. Then we use GNN to classify 4 types of ships including fishing vessels, passenger ships, oil tankers, and container ships. Finally, we compare the results with the SVM method. And it shows that this method is valid and proves that it is an effective method of ship classification.
Research on the Algorithm of Detection for Small and Weak Target Based on the Mechanism of High-Resolution Amplification
Authors: Cheng Xianyi , Sun Lili , Tian Yachong 
Published: September 2021

Abstract
Aiming at the problem that the existing target detection framework based on big data and deep learning has poor recognition effect for small and weak targets with low resolution in complex scenes, in this paper, we improve the small targets detection accuracy and speed, based on the FCOS algorithm for target detection and the mechanism of high resolution amplification. The basic principle is to increase amplification precision gain regression network (R-NET) and amplification region selection algorithm, on the basis of enhanced learning. The former learns the correlation between coarse and fine detection, and the latter calculates the information gain of the amplified region. The results show that this method can detect small and weak targets in complex environment.
An Ensemble Learning Algorithm for Machinery Fault Diagnosis Based on Convolutional Neural Network and Gradient Boosting Decision Tree
Authors: Zhou Jing , Gao Yang , Lu Jianping , Yin Chun , Han Huan 
Published: September 2021

AbstractIn the massive mechanical fault data, the value density of fault information is low, and the data quality is uneven. What’s more, the multi-source signals collected by different sampling methods are different. At present, the expert system or shallow neural network model with weak self-learning ability cannot meet the requirements. Therefore, aiming at the characteristics of coupling, uncertainty and concurrency of mechanical faults, this paper constructs two kinds of 2D-CNN fault feature data sets, and uses Convolutional Neural Network (CNN) with strong self-learning ability to build two kinds of fault diagnosis models: CNN-Z and CNN-F. With CNN-Z and CNN-F models as base learners, this paper utilizes the ensemble algorithm Gradient Boosting Decision Tree (GBDT) to combine multiple bases. Compared with the results of the single base learner, the outcomes have higher accuracy and lower generalization error. Through the analysis and comparison of the performance indicators of the algorithm, this paper concludes that the diagnosis error of the fault diagnosis algorithm based on CNN and GBDT is the lowest with 1.79%, and the effectiveness, reliability and accuracy of the proposed algorithm in mining hidden fault state information are verified.
Based on Particle Group Algorithm of Route Planning for Transportable Charging Station
Authors: Ding Xiaoyin , Zhou Jun , Cai Jian , Zhang Jing , Li Taoyong , Liu Zhibin , Liu Boqian 
Published: September 2021

Abstract
Aiming at the problem of path planning for transportable charging stations, a site selection model and path planning model for dedicated routes for transportable charging stations are proposed. The path planning problem model includes the vehicle path problem and the dynamic path problem, and the path planning model is emphatically studied. Considering the network optimization model of chargers and transportable charging station paths, by establishing a path planning model, setting parameters and models, using particle swarm algorithm to calculate the path planning model of chargers and mobile charging stations (Vehicle Routing Problem Considering Stationary and Movable Charging Points (referred to as “VRP-SMCP”) is solved, and the feasibility and effectiveness of the proposed model and algorithm is verified through comparative analysis of calculation examples, and the optimal path planning under the condition of minimizing cost is obtained.
Monocular Depth Estimation via Convolutional Neural Network with Attention Module
Authors: Lan Lingling , Zhang Yaping , Yang Yuwei 
Published: September 2021

AbstractThe depth accurately estimated from the RGB image can be used in applications such as 3D reconstruction and scene display. In depth estimation, Convolutional Neural Network (CNN) plays an important role. However, most of the existing CNN-based depth estimation methods often do not take full advantage of the space and channel information in the local receptive field, resulting in lower-resolution depth maps. Based on the extant Encoder-Decoder network architecture, a simple and effective attention module is introduced to learn the discerning feature and the semantic feature in this paper. First, the intermediate feature map is generated by the encoder, and then the attention module is used to generate the attention maps along the two dimensions of the space and channel of the feature map. Finally, the refined output generated by multiplying the attention maps to intermediate feature maps is input to the subsequent neural network for further work. Our works show that the suggest approach has obtained good achievements on NYU Depth v2.
Posture Data Automatic Extraction of Ornamental Turtle Based on Computer Vision Technology
Authors: Ma Yinchi , Wei Wei 
Published: September 2021

Abstract
The posture data of ornamental turtle is a quantitative index to evaluate the quality of the turtle. From the young turtle to the adult turtle, the posture data is constantly changing and the quality can also change accordingly. An integrated observation platform is designed and implemented. The automatic extraction of five posture data, i.e., the length of carapace, the width of carapace, the height of carapace, the length of plastron, and the width of plastron, is realized by using OpenCV based computer vision technology. The results of the experiment and the data of artificial measurement were compared and analyzed. The average fitting degree of the two values reached 99.2%. This method can extract the posture data of ornamental turtle quickly and accurately, and then track its growth and development. It will greatly reduce the workload of data collection in the process of ornamental turtle breeding, and effectively improve the accuracy of data collection. At the same time, it will reduce the risk of damage, and have important production and application value.
Applying Particle Swarm Intelligence in PolSAR Image Clustering
Authors: Zhu Teng , Gao Zhaozhong , Huang Tielan , Shen Chen 
Published: September 2021

Abstract
The clustering problem of polarimetric SAR image is an optimization problem with high dimension and large amount of data. Aiming at the problem that the classical unsupervised classification methods for High Resolution Polarimetric SAR images are difficult to find the global optimal solution. The Particle Swarm Optimization (PSO) algorithm was proposed in High Resolution PolSAR images clustering. For the first beginning, the scattering eigenvalues of PolSAR data were used for initial classification, and then followed by the computation of clustering center and initialization of PSO algorithm, finally the particle swarm are introduced in the iterative steps to reduce noise effect and improve the classification results. The performance of this novel method is demonstrated in experiments using L-Band PolSAR image of San Francisco Bay.
Research on Error Concealment for Video Communication
Authors: Zhang Yan , Li Xiangqun 
Published: September 2021

Abstract
The video sequence restoration described in this article has been extended to many areas of our lives today. People use it to perform various restoration tasks, making it possible for some pictures that were impossible to restore in the past. This paper uses experimental methods to study the error concealment and traditional image restoration in video compression, and mainly studies the CDD model algorithm, TV model algorithm and error mode. This article also has a very important meaning for the related research of video communication error control, that is, the research of digital image restoration technology, in the post-processing of film and television works, the fuzzification of network video, and the restoration of old documentaries.
Research on Data Gateway for Ship Based on Dynamic Adaptation
Authors: Tong Liang , Chen Xuemei , Zheng Pengfei 
Published: September 2021

Abstract
In view of the immature status of the old-fashioned ship information collection and data analysis system in inland rivers, after analyzing the characteristics of ship navigation information, the problem of low efficiency, inaccuracy and poor real-time information collection is solved. Starting from ship equipment (such as main engine, power station, control system, etc), a ship data acquisition gateway based on dynamic adaptation is designed. Based on the principle of making full use of the old system of marine equipment in the engine room, a multi-protocol data acquisition method based on an adaptive model is constructed, which realizes the separation of information acquisition and information use, eliminates all the close coupling relations between system equipment and applications, and realizes the integration of different data information on the ship and the dynamic display management function on the client.
Identification of Tomato Pests and Diseases Based on Transfer Learning
Authors: Yang Liu , Yu Limin , Tao Sha , Yang Zhenghong , Gao Wanlin , Ren Yanzhao 
Published: September 2021

Abstract
There are numerous kinds of tomato diseases and insect pests. Their pathology is complex and different. It is hard to rely on manual identification purely and the error rate is high. After collecting a mass of leaf table pictures, our aim is to classify nine kinds of common tomato diseases in China. The idea of transfer learning is applied to achieve recognition and classification of tomato data set by the lightweight convolutional neural MobileNet. Finally, the model can obtain test classification accuracy of 97.19%. Experiments have proved that this method is not only simple to operate and easy to implement, but also can achieve high accuracy on plant diseases.
Research on Application of Assembling Parts Method Based on Virtual Reality Technology in Interior Design
Authors: Xie Guanyi , Chang Xiaowen 
Published: September 2021

Abstract
The research of this article originated from the reflection on the methods of interior design training after the intervention of new technologies. Due to the wide application of virtual reality technology, it can intuitively display the material, texture, color and other very concrete things of interior design. It helps designers form a good spatial perception ability, thereby solving the problem that traditional design tools cannot solve the problem of insufficient expressiveness of hand drawings and models. However, the designer always pays attention to the description of the details. This kind of interior design usually has a good partial decorative effect. At this time, the internal abstraction order is often ignored by us. As a result, there is a contradiction between the concrete perception and the abstract order, and the effect of interior design based on VR technology is out of touch. Therefore, how to harmonize and unify the concrete perception and abstract order of interior design based on virtual reality technology is my focus. In this paper, the assembly method is applied to the interior design involving virtual reality technology. Through the operability, repeatability, and strong logic of the assembly method, the contradiction between the above-mentioned concrete perception and the abstract order is solved, and the application of virtual reality technology is realized.
An Impedance-Control Based Teleoperation System for Live-Line Maintenance Robot
Authors: Wang Yang , Wu Hui , Mai Xiaoming 
Published: September 2021

Abstract
Robotic operation is an effective way to upgrade the live-line maintenance safety, efficiency, and quality. This paper proposes an impedance control-based teleoperation system to improve the adaptability of live-line maintenance robot in outdoors environment. The key technology of this system is utilizing three virtual spring-damper systems to model the elastic connection between the end-effector of slave manipulator and the environment, between the end-effector of slave manipulator and the counterpart of master haptic device, and between the end-effector of master haptic device and its base, respectively. Experiment results show that, under control of our proposed teleoperation system, the slave manipulator is able to track the motion of master haptic device and the robot is able to complete a set of complex action to peel the coat off the cable.
Prediction of Process Parameters of New Injection Molding Products Based on Historical Qualified Product Data
Authors: Niu Shiyin , Yu Junhe , Zhan Hongfei , Wang Rui 
Published: September 2021

Abstract
In the field of thermoplastic injection molding, there are a large number of qualified product data, mining and analyzing these data is of great significance to enterprise production. At present, most scholars do optimization research around given process parameters, and cannot fundamentally solve the problem of process parameter setting values. To this end, this article proposes a new method, through the analysis of the material, structure and process of historical qualified products, from the perspective of the product, find the factors that affect the process parameters, and use the BP neural network model to train the non-linear mapping relationship between the product and several main process parameters, and predict the main process parameter values of the new product. For inexperienced manufacturers of injection products, this method can greatly improve their productivity.
Grey-Box Technique of Software Integration Testing Based on Message
Authors: TanLi Mengqing , Zhang Ying , Wang Yulin , Jiang Yan 
Published: September 2021

Abstract
In the software integration testing, the combined exploding problem was appeared for GUI (Graphic User Interface) software with many advantages. Based on the engineering thinking of staging and the feature of GUI software, the grey technique of integration testing for GUI Software based on message is proposed to solve this problem, in which, the white-box method is used to deal with the consistent and unified part and black-box method is mainly engaged to dispose the difficult and varied part of software testing. By analysis of basic GUI software, the application of this grey technique will improve the testing efficiency with 333% according to testing executed number, and the efficiency in terms of testing executed time will be 438%. An actual example in product quality monitoring software has verified the approach and its efficiency of the grey technique.
Design of Toxic Environment Monitoring System for Industrial Sites Based on Wireless Sensor Networks
Authors: Zhang Xinrong , Chang Bo 
Published: September 2021

Abstract
In view of the actual demand of toxic gas monitoring in the production, storage and use field environment of chemical enterprises, considering the large amount of monitoring data and the difficulty of real-time transmission of information, a toxic gas environment monitoring system is designed. The system based on low-power microcontroller chip CC2530 by using wireless sensor intelligent information processing and data communication technology, which evaluates and improves the ambient air conditions and provides theoretical basis and technical support. The sensor node collects the concentration data of CO, formaldehyde, ozone, SO2 and other toxic gases in real time, processes them and sends them to the receiving end. According to the stored data, the receiver monitors the air quality of the industrial production and storage environment in real time, and effectively solves the problems of complex wiring and maintenance difficulties of traditional cable sensors to reduce the risk of accidents or early warning and reduce the hazards of accidents. The test results show that the system has stable performance, can effectively detect industrial toxic environmental parameters, and can be widely used in various fields, especially in unattended or inaccessible environment for monitoring tasks.
Research of Adaptive Frequency Search Based on Dichotomy
Authors: Mao Weiwei , Zhu Ru , Jin Xiaoyu , Li Zonghao , Zhang Zhiguo 
Published: September 2021

Abstract
In order to solve the problem of finding out the frequency of signal through adaptive filter as soon as possible, the adaptive frequency search of adaptive filter has been focused in this paper. According to the change of the signal frequency, the proposed adaptive filter based on the technology of dichotomy decides the frequency of the input signal, realizes the narrowband filtering and the large dynamic variation of specific frequency signal. And the single frequency sinusoidal signal has been theoretically analyzed by the way of DTFT and discrete Fourier transform. Finally, the estimated value of the signal frequency has been preliminary measured by the method of DFT, then based on the characteristic of DFT, the dichotomy search for the real signal frequency has been designed so that the accuracy of frequency measurement would be guaranteed within 2Hz.
A flexible control system for reconfigurable assembly lines
Authors: Xiao Leicai , Jing Xinghui , Zeng Long , Liu Xueping 
Published: September 2021

Abstract
With the rising demand for customized and frequently updated products, the current assembly systems require a higher level of reconfigurability and flexibility. Thus we proposed a scheme of reconfigurable assembly line and hierarchical control system. To increase the flexibility of control systems, a communication method and control strategy is presented in this paper. This approach will endow the reconfigurable assembly system with flexibility by encapsulating the ladder diagram program and providing an external interface for the control software to call. This approach is validated with one assembly unit assembling shuttle valve, which takes 40s during the process. The control system can not only monitor the assembling process, but also control the action of the actuator via control panel or control commands.
The Control System and Application Based on ROS Elevating Fire-Fighting Robot
Authors: Gan Zhijian , Huang Guofang , Zhang Jing , Liu Xiaoming , Shan Chao 
Published: September 2021

Abstract
Nowadays, fire accident is happening frequently which brings serious damages to people’s lives and properties; at present, fire-fighting robot is unable to perform targeted fire-fighting and combustion source at high places has brought difficulties to fire control and fighting; therefore, fire-fighting and assistance are facing unprecedented challenges. In view of this, the paper researched and developed an automatic control system for elevating fire-fighting robot, so as to solve the key difficulties and problems for fire control and fighting of fire-fighting robots. The research target consists of three aspects, firstly, the system has realized the elevating fire control and fighting function; secondly, we have designed an automatic control system for elevating fire-fighting robot based on ROS structure; thirdly, the paper has established the automatic control strategy play for raise arm of the elevating fire-fighting robot featured with stable and controllable operation, so as to solve the problem of unsteadiness in the elevating process and speed up application and popularization of fire-fighting and disaster rescue of the fire-fighting robot.
Prototype of a Solar-powered Fix-winged Unmanned Aerial Vehicle
Authors: Li Ziheng 
Published: September 2021

Abstract
The usage of solar power is a plausible and environment-friendly solution to solve the problem of limited duration of operation of current Unmanned Aerial Vehicle (UAVs) in market. Based on the state-of-the-art experiments in integrating solar power into aircraft and the basic theoretical calculations, this study investigates an attempt to construct a remote control solar-panel powered autopilot fixed-wing aircraft with technology accessible for home crafting. The aircraft is designed to recharge itself using sunlight within the duration of its operation and execute certain simple autopilot routes. The success of this model sheds light for further research and investment in making small-scale aircrafts solar-powered.