bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adaptive streaming algorithm based on reinforcement learning
Authors: Feng Suliu , Wang Caihong , Jiang Xiuhua 
Published: March 2020

Abstract
As video streaming occupies a large part of the Internet traffic, and DASH gradually becomes the dominant standard for video transmission recent years, a lot of researches about client-side bitrate adaptive algorithms are constantly emerging.This paper mainly adopts the DRL(Deep Reinforcement learning) algorithm that combines Deep Learning and Reinforcement Learning techniques to optimize the Quality of Experience (QoE) of DASH.This method learns to make decisions by observing the network environment and adjust the strategy to get more reward based on feedback,which doesn’t rely on pre-programmed models or assumptions about the environment. After experiments on the real network trace datasets, we can find that the algorithm this paper adopted can obtain higher quality of user experience than the existing researches.Moreover, the experimental results show that less buffer underflow occurs and smoother video playback is guaranteed.
Lightweight Neural Network for Component Recognition and Fault Detection of Large-Scale Transmission Line
Authors: Zhao Guowei , Wang Qiang , Zhang Juan , Zhao Rui , Zhao Jielun , Dong Hongyue , Zhang Xingzhong 
Published: March 2020

Abstract
Recognition and fault detection of transmission line components is a basic problem that puzzles the development of smart grid. However, due to the limitation of current datasets, it is difficult to carry out large-scale model training and mobile reasoning based on artificial intelligence technology. Although the current standard datasets for the classification of components in transmission line scenarios contain many different types of objects, they are all smaller datasets based on a certain category. This paper constructs and annotates 10 226 standard image Power Database which contain 6 component categories (including 3 fault component categories). In addition, aiming at the problem that the general target detection algorithm infers too slowly or can not run in the real-time inspection process of transmission line mobile terminal equipment, a model MSFF-KCD (Multi-scale feature fusion in key component detection) is presented, which combines the multi-scale feature fusion method with the detection of transmission line key components. Experiments on real-time component identification and fault detection by deploying the model to mobile devices show that the proposed method establishes a new performance bound and this mothed suitable for mobile terminal detection of key components of transmission lines. We also compared the models of recognition and detection for large and small targets on Power Database, which provided enlightenment for the identification and fault detection of power components.
On Error Performance and Concatenated Coding of Polar Codes in AWGN Channels
Authors: Jin Jing , Deng Rui , Liu Tiansheng , Li Liping 
Published: March 2020

Abstract
In additive white Gaussian noise (AWGN) channels, construction of polar codes is needed for every operating signal-to-noise ratio (SNR). Recently, the proposal of the design-SNR reduces the computation effort in constructing polar codes. In this paper, we prove that although the BER performance of the design-SNR construction is not affected, the packet-error-rate (PER) performance is degraded compared with the point-by-point construction. Therefore, a concatenation scheme is proposed to improve the degraded PER performance. Results show the validity of the proposed concatenation scheme when employing the design-SNR construction.
Design of Airport Wireless Bird Repellent Monitoring System
Authors: Yang Xiufang , Wang Chi , Chen Zhuo , Wang Dian 
Published: March 2020

Abstract
At present, the status detection of the bird driving equipment in the airport is completed by manual periodic inspection. This method can’t grasp the status of the bird repelling equipment in real time and cost human resources. In view of this situation, a remote monitoring system based on STM32 is proposed. The system uses the embedded STM32 as the control and data processing core to collect the signals of the temperature sensor, pressure sensor, power supply voltage and sound sensor of the bird-repelling equipment. It is uploaded to the host computer through ZigBee technology to analyze the working conditions of the bird-repelling equipment and the situation of the gas gun. Through the test of the design system, the results show that the detection accuracy of the system can meet the actual need, the correct rate of detecting sound signal is 100%. In the environment of two concrete buildings, the wireless transmission distance is within 800m, and the error rate is 0.
Passive positioning method and error analysis of UAV based on TDOA
Authors: Zhang Di , Li Changgeng , Zhou Chao , Ma Chao 
Published: March 2020

Abstract
At present, the phenomenon of "black fly” and "flooding” of drones frequently occurs, which brings various security risks to the country, society and individuals. Aiming at the passive positioning problem of UAV, this paper proposes a method based on TDOA for direct closure to solve the target position.The four-station time difference method is analyzed. The position coordinates of UAV are expressed as a function of the radial distance between the UAV and the main station.Then solve the quadratic equation of the radial distance between the UAV and the main station.Finally, obtain the position coordinates of the UAV by radial distance.According to the distribution of the solution of the quadratic equation, the unique solution zone, the fuzzy solution zone and the non-solution zone are analyzed, and the fuzzy solution is solved. The method of defuzzification is proposed in the area.The positioning error is studied, and three factors affecting the positioning error are discussed: the distance between base stations, the height of the main station and the measurement accuracy of distance difference.The research conclusion has important guiding significance for the station design.
A Dual-mode Communication Power Internet of Things Routing Algorithm Based on RPL Protocol
Authors: Ye Fangbin , Zhao Ling , Jiang Chi , Sun Jianqiao 
Published: March 2020

Abstract
Medium frequency (less than 12 MHz) Power Line Communication (PLC) and micro-power wireless Radio Frequency (RF) communication dual-mode communication technology can realize the depth and breadth coverage of the power Internet of Things (IoT) and enhance the robustness of the network. But dual-mode communication also brings difficulties to the application of the RPL protocol. According to the characteristics of PLC and RF dual-mode communication technology, the routing communication delay is the optimization objective function, and a routing metric algorithm of RPL routing protocol is proposed. The RPL protocol is applied to the dual-mode communication in power Internet of Things. Experiments show that the proposed algorithm has higher communication efficiency and can meet the needs of dual-mode communication hybrid networking and operation. It is a feasible method.
Experimental Study on Doppler Performance of Satellite Emergency Communicator Based on LoRa System
Authors: Zhang Yonggang , Chen Jianyun , liu Sili 
Published: March 2020

Abstract
Aiming at the propagation loss and Doppler frequency shift problems faced by LEO satellite in realizing Internet of Things, the LEO satellite emergency communication machine is tested. The emergency communication machine adopts SX1280 chip as the transceiver unit, analyzes the bit error rate and Doppler frequency shift index of LoRa signal generated by SX1280, and proposes a test scheme combining satellite channel simulator and emergency communication machine. In the range of Doppler frequency shift less than 53 kHz, changing Doppler frequency and its change rate has less than 4 dB effect on signal communication sensitivity. Reducing signal transmission rate and bandwidth can improve communication sensitivity. It proves that LoRa signal generated by SX1280 chip can be effectively used in LEO satellite Internet of Things.
Probabilistic hesitant triangular fuzzy power aggregation operator and its application
Authors: Qiu Wenjing , Qiu Jinfei 
Published: March 2020

Abstract
Based on the hesitant triangular fuzzy set, the probabilistic hesitant fuzzy set and the power aggregation operator, the probabilistic hesitant triangular fuzzy multi-attribute decision problem with the correlation between the aggregated data is studied. Firstly, the probabilistic hesitant triangular fuzzy set (PHTFS) is defined, and the algorithm, scoring function and distance measure of the probabilistic hesitant triangular fuzzy element (PHTFE) are proposed. Then, according to the PHTFS and power aggregation operator, the power aggregation operator of PHTFS is proposed. Finally, a multi-attribute decision making model is constructed based on the probabilistic hesitant triangular fuzzy power aggregation operator, and the validity of the model is verified by a case.
Accelerate fine-scale geological mapping with UAV and convolutional neural networks
Authors: Zhan Liang , Liu Bin , Sang Xuejia , Xue Linfu 
Published: March 2020

Abstract
We propose a new fine-scale mapping process, which UAV and CNNs to distinguish the rock mass. Studies have shown that with UAV high-resolution images, comparing with traditional classification methods (52.92%~67.11%), the CNNs method has a much higher classification accuracy rate (86.54%). Although they can’t completely replace ground work, UAV and CNNs, together with appropriate field geological survey work, can quickly fill fine-scale geological maps. This is significant for harsh areas.
An adaptive hierarchical transmission mechanism of 3D models for mobile terminals
Authors: Wei Chen , Shang Yanlei 
Published: March 2020

Abstract
An adaptive hierarchical transmission mechanism of 3D mesh models for mobile terminals based on WebAR technology is introduced. By proposing a vertex merging algorithm based on vertex megring degree, the original model is decomposed into multiple groups of data consist of a simplified model and a layer file for reconstruction according to the specific compression ratio. The experiments show that the initialization time of the AR scene using the simplified model as the base model is effectively reduced and the simplified model can be reconstructed to the original model losslessly with the layer file. The proposed mechanism can be used for adaptive transmission, lossless reconstruction and real-time rendering on mobile terminals.
Research on Power Load Forecasting and Visualization Method Based on Deep Neural Network
Authors: Wang Yifei , Li Xian , Mo Shuang 
Published: March 2020

Abstract
Short-term load forecasting (STLF) has attracted much attention in the last decade, which uses data from historical power grid operation monitor to forecast future power demand for a few days. Previous works on STLF mainly focus on features, which are widely used to capturing external factors in power system. There are also approaches based on physics, which use linear combination of active and reactive power consumption to model dynamics of power demand. However, in many real-world applications they don’t work well. In the self-service power grid, STLF is a very important part in power scheduling and planning. But STLF is a challenging task for its complex power demand and the effects of weather. In this paper, we study the problem of combination of complex and seasonality of power demand, as well as effect of external weather. Therefore, we come up with an approach, called DNNCast, it’s a model based on deep neural network, which can achieve a good accuracy and fast training speed. In the structure it uses a deep neural network to model various power loads and external weather conditions. Finally, with visualization tools we can wisely make decision about power scheduling and planning. Compared with the current STLF methods, our model has a better prediction effect, and faster in the experiments of the extended experiment on 133 real power datasets.
Research on the Design and Application of Implicit Semantic Model in Bundle Recommendation Method
Authors: Li Zhitao , Li Shuqing 
Published: March 2020

Abstract
The recommendation system has been studied for a long time. Data of more users and commodities has been used to improve precision of the recommendation system. However, few studies are on utilization of bundling information. The bundling information refers to the inherent connection of the commodities, and depended by objective properties of commodities. This paper adds bundling information to the recommendation method based on traditional implicit semantic model and collaborative filtering method. This paper sets the bundling coefficient to show the connection degree between commodities and recommended decisive factors. Experiment shows the precision of recommendation could be improved with bundling information in specific environment through changing bundling coefficient.
Research on Text Mining of Material Science Based on Natural Language Processing
Authors: Gao Xiang , Tan Rong , Li Guanghui 
Published: March 2020

Abstract
Facing large-scale and rapidly growing material science literature data, text mining has become a research hotspot of material science. In recent years, natural language processing technology and machine learning methods have become the main technical means of text mining in materials science. The main task of text mining is to transform unstructured text data into structured material data by information extraction methods such as Named Entity Recognition and entity relationship extraction. This research proposes a general solution framework for material information extraction tasks, and introduces the main concepts and processes of text processing, text annotation, entity relationship extraction, etc., and discusses the current research progress and possible future research directions.
Parameter Analysis and Simulation Experiment of Ant Colony Optimization on Small-scale TSP Problem
Authors: Yang Lin , Wang Yongjie , Zhang Jun 
Published: March 2020

Abstract
Ant colony optimization (ACO) is a simple and efficient bionic intelligent algorithm, which can be applied to various optimization problems. There are many controllable parameters in the ant colony optimization, so the parameter setting has a great impact on the efficiency of the algorithm. In order to further improve the efficiency of the algorithm, many researchers have proposed various improvements. However, the current improved method of ACO ignored the consideration of its original parameter settings. In order to analyze the effect of parameter setting on the efficiency of the ant colony optimization, this article uses the ant colony optimization to solve small-scale TSP problems as an example for simulation experiments. Explore the impact of three parameter pairs on the efficiency of algorithm optimization by setting variable pairs. The experimental results show that when the ant colony size is set to approximately 0.6 times the TSP problem size, a better algorithm efficiency can be obtained. Within the value range of [1, 10], setting the information heuristic factor to 1, and the expected heuristic factor to a range of [5, 10] can get a better efficiency. In the last experiments, the pheromone volatility coefficient is generally better to be set greater than 0.2, and in a small-scale TSP problem, totally randomness has little effect on the optimization accuracy of the algorithm, and the value of pheromone enhancement coefficient has little effect on the efficiency of the algorithm.
Ship Agent model for traffic flow simulation in inland waterway
Authors: Xu Wuxiong , Wu Siqing 
Published: March 2020

Abstract
Water traffic is a nonlinear complex system, which is difficult to be described by a mathematical model. Naturally, simulation has become an important method to study it. In the simulation system, how to establish a ship simulation model is very crucial. In this paper, according to characteristics of ship’s behaviour, the Ship Agent model for simulation in the inland waterway is constructed, and the Multi-Agent simulation system is composed of these Ship Agents. As a case, the simulation system is set up based on the Monte Carlo method. The conclusion can be drawn that this Multi-Agent system can imitate the ship’s behaviour, and achieve the basic operation, such as the ship’s free navigation, overtaking, turning, collision avoidance and so on.
Collision detection based on octree for virtual surgery system
Authors: Hu Yanrong , Yan Zhiyuan , Yin Zhenlun , Du Zhijiang 
Published: March 2020

Abstract
In the virtual surgery system, the organ is always non-convex and deforms over time which makes the collision detection problem more difficult to handle. In this case, we put forward an innovative collision detection algorithm based on octree. This algorithm can perfectly apply to deal with non-convex organs and organ deformation scenario. In addition, it can achieve high run-time efficiency.
Research on the Classification of Fish Parasitoids Based on Digital Image Processing Technology
Authors: Zhan Yekang , Geng Xiebo , Liu Yang , Zhang Ying 
Published: March 2020

Abstract
The traditional classification method of morphology and the combination of morphology and molecular biology have low efficiency and accuracy in the classification of fish parasitoids. In this paper, Four kinds of blepharoplasma, such as ugly round Myxobolus, Myxobolus shantungensis, Myxobolus Koi and Myxobolus dispar, were selected as the research objects. A new method based on digital image processing technology is proposed to extract the edge of a spore and pouches, and calculates various morphological parameters of the Myxobolus. Based on the obtained parameters, a classification standard is established to classify four different Myxobolus and a new classification method based on morphological criteria was proposed.
Optimization Algorithms for Tibetan-Chinese Neural Machine Translation
Authors: Jiang Jing , Li Yachao 
Published: March 2020

Abstract
Tibetan-Chinese neural machine translation (NMT) is facing serious resource scarcity problem. This paper compares the application effect of multiple neural network optimization algorithms under the condition of resource scarcity, and proposes an optimization method which is suitable for resource scarcity languages. Then, by which it improving the performance of the Tibetan-Chinese NMT. Experimental results show that when choosing a suitable optimization algorithm, the Tibetan-Chinese NMT can still exceed the traditional statistical machine translation (SMT) and achieve better translation performance.
Research on the Congregation of Cross-border E-commer Talents Based on Structural Equation Model
Authors: Tian Xiao , Yan Xiaogai 
Published: March 2020

Abstract
The competition in modern industries is the competition for talents. The booming development of cross-border e-commerce has brought new challenges to talent gathering. Based on a questionnaire survey of 241 Zhengzhou cross-border e-commerce practitioners, based on the three dimensions of regional factors, organizational factors, and personal factors, a structural equation model was used to analyze its impact path on the cross-border e-commerce talent gathering. The results of empirical research show that: organizational factors are the main factors affecting the gathering of cross-border e-commerce talents, personal factors are the important factors affecting the gathering of talents, and regional factors are the effective factors affecting the gathering of talents. Finally, corresponding suggestions are put forward.
Research on Tolerance Redistribution Method Based on Measurement Data
Authors: Fu Tianzhao , Lyu Ruiqiang , Wang Mingyang , Yang Weimin , Wang Mingyang , Xie Pengcheng 
Published: March 2020

Abstract
As the performance requirements of aircrafts increase, the mechanical structure of the product is more complicated and the requirements for assembly are getting higher and higher. Due to the accumulation of errors in the processing and assembly process, assembly may not meet the product design requirements. It is necessary to repair some of the processed parts before performing the assembly process. This paper proposes an assembly simulation analysis and a tolerance redistribution approach based on measurement data that is making decisions on which part should be repaired. Firstly, the processed parts and assembly fixture are measured by measuring devices and fitted into the digital model with measurement data. Secondly, the assembled digital simulation is performed to verify whether the processed parts can be assembled successfully. Finally, if the parts cannot be assembled successfully, an improved genetic algorithm is used to optimize the tolerance to form a repair scheme.
Preparation and Force Sensitive Properties of ZnO/Graphene Composite Films
Authors: Wang Jianming , Luo Zhaomin , Zheng Feng , Wang Qiaozhi , Xu Xingming 
Published: March 2020

Abstract
In this paper, the preparation and force sensitivity of ErGo-ZnO composite films were studied. In order to improve the application value of graphene composite film in optical devices, sensors, photocatalysis, etc., ZnO is added. In this experiment, the ErGo-ZnO film was successfully prepared by vacuum filtration. At the same time, by adjusting the self-assembled layer of the composite film and the concentration of graphene oxide in the self-assembly growth process, the optimal design parameters are further optimized. The experimental results show that the self-assembled 6 layers have the best performance of the graphene oxide concentration of 0.5mg/ml.The crystallinity of the 0.05wt% graphene oxide ErGo-ZnO composite film is obviously improved. The amount of phase in the film after axial stretching is greatly increased (from 20% before stretching to 92%). In addition, comprehensive characterization and experimental results show that the excellent mechanical properties and specific surface area of graphene oxide and the excellent compatibility with ErGo-ZnO materials are the key factors to improve the interface stress transfer efficiency. This makes the crystallinity and mechanical properties of the composite film greatly improved, and the piezoelectric properties are further enhanced, which greatly improves the application value of the graphene composite film in optical devices, sensors, photocatalysis and the like.
Design and finite element analysis of thick shell mussel shell opener
Authors: Li Zhenhua , Luo Erlin , Tan Jinling , Ni Yang , Song Zuchano , Wang Jian 
Published: March 2020

Abstract
In order to obtain fresh mussel meat, this paper comes up with a design of mechanical automatic thick-shell mussel open shell machine. According to the physiological parameters and structural characteristics of thick shell mussel, it is determined that the scheme of automatic shell opening and the related parameters of open shell cutter tool. At the same time, Using Solidworks simulation and ANSYS Workbench on the open shell tools and pneumatic cylinder and other major component of finite element analysis, which is ensure that thick-shell mussel open shell machine open shell with stable and efficient. And the relevant part of parameters are confirmed that the thick shell mussel open shell machine avoid some adverse vibration frequency in theory. The purpose of the thick-shell mussel open shell machine is to replace manual opening shell, improve the efficiency of mussel shell opening and provide further reference for the production design of the automatic thick shell mussel opening shell equipment.