bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research status of improvement based on DV-Hop non-range positioning algorithm
Authors: Yan Rongjie 
Published: April 2022

Abstract
In order to solve the problem of poor positioning of the DV-Hop algorithm, it is a current research hotspot. In view of the three aspects of error distribution, the research plan for effective improvement is listed. In the field of the minimum number of hops, the RSSI-based weighting correction and the use of deviation factor improvement; in the average distance of hops, the calculation of distance error improvement and the use of angle value weighting processing; in the calculation of unknown nodes, the weighting of the least square method and the triangle centroid method two. This correction is estimated. Relevant scholars of the above methods can solve the problem of poor positioning of the classic DV-Hop algorithm through simulation experiments.
Research on automatic test sequence generation method of computer interlocking test
Authors: Kuang Jiangyu , He Tao 
Published: April 2022

Abstract
Railway computer interlocking system is the core equipment of station signal control system, which directly affects the reliability and safety of the whole system. It is essential to test the interlocking software comprehensively and strictly [1]. The traditional interlocking test is conducted by testers to control the quality and efficiency of the test. According to the reference railway transportation “Interim Measures for railway signal interlocking test”, combined with the signal interlocking relationship test checklist, compared with the actual station yard of railway station, relying on manual preparation and manual implementation of test items, the test efficiency is relatively low. Therefore, the realization of automatic interlocking test has become a research hotspot at present. Interlocking function test belongs to the research field of black box test. On the premise of ensuring the comprehensiveness of test, generating efficient test sequence is the research focus of this subject.
Research on flame recognition technology based on local complex features
Authors: Zhu Di , Ahmad Kamarul Arifin , Chen Aolin 
Published: April 2022

Abstract
Traditional flame recognition methods based on image features are difficult to extract flame image features effectively, resulting in low flame recognition accuracy, while such methods mostly perform specific flame recognition for specific scenes, and when the scene, flame color and other features change, it is difficult to perform flame recognition effectively. To address this problem, this paper proposes a flame recognition scheme based on local complex features. Its main purpose is to fuse multi-scene flame data, introduce the characteristics of flame in color space through the process of extracting feature descriptors in SIFT, so as to filter the extracted feature descriptors with noise interferers, and transform the extracted feature descriptors into feature vectors by using the key point bag-of-words method, and finally a general fast flame recognition model based on the limit learning machine. In this paper, we explore the upper limit of the capability of traditional image feature representation to pave the way for the application of deep learning to the flame recognition problem.
Energy Consumption Optimization of Edge Computing Based on Reinforcement Learning
Authors: Jiang XiangYu 
Published: April 2022

Abstract
Cloud computing faces the problem of over-centralization, where the distance between users and cloud servers is very far. The communication between users and clouds faces long communication latency and high computational energy consumption. Edge computing is a hot research topic in academia now. Edge computing sinks cloud computing nodes to the edge, allowing users to offload tasks directly to edge servers. Compared with cloud computing, edge computing is closer to the user side. The communication between users and edge servers has lower transmission latency and less energy consumption. In order to better exploit the features and promote the development of edge computing, in this paper, we mainly focus on exploring the basic architecture of edge computing. In addition, we propose a scheme that can optimize the energy consumption of edge computing based on reinforcement learning methods. Finally, we verify the effectiveness of our proposed scheme by comparing it with other schemes through simulation experiments.
Research of Binocular Visual Inertial Algorithm Based on Point and Line Features
Authors: Zhou Yipeng , Li Maohai , Shao Guowei 
Published: April 2022

Abstract
To solve the problem of poor performance of the binocular visual inertial odometer VINS-Fusion in scenes with low texture and large luminosity changes, a binocular visual inertial odometer PLVINS-Fusion is designed that integrates line feature measurement information, which use line features to easy to extract in low-texture scenes, and have the advantage of more robust tracking performance in scenes with large luminosity changes. Point and line features are extracted in the front-end visual extraction at the same time, and line feature residuals are added to the back-end nonlinear optimization, construct a bag-of-words model combining point and line features in the loop detection module. On this basis, a real-time photometric calibration algorithm is adopted to jointly optimize the exposure time, the camera response function and the vignetting factor, and the stability of KLT optical flow tracking is improved by correcting the image brightness. Experiments on benchmark dataset show that the optimized algorithm has higher robustness and effectively improves the positioning accuracy, and meets the real-time performance requirement.
An RFID multi-Tag anti-collision algorithm based on Successive Interference Cancellation with power adaptive regulation
Authors: Gao Xingyue , Lou Huanli , Miao Qingyuan 
Published: April 2022

Abstract
Radio Frequency Identification (RFID) is a key technology supporting the Internet of Things (IoT). The simultaneous identification of multiple Tag signals by a Reader will generate the Tag collision problem, which will significantly reduce the identification efficiency of the system and prolong the identification time in the scenario of large-scale Tags. In order to solve this problem, this paper researches RFID Tag collision prevention algorithm, and establishes a multi-Tag collision prevention algorithm model by analyzing the Aloha Tag collision prevention algorithm, which can accurately portray the slot efficiency and time efficiency of the system when recognizing multiple Tags; then establishes a power adaptive regulation RFID multi-Tag identification algorithm using Successive Interference Cancellation (SIC) technology. After simulation and verification results show that the constrained Reader can resolve the signal to noise ratio threshold of 6.9db, the slot efficiency and time efficiency of the SIC-based RFID Tag anti-collision algorithm is improved by 179% and 154% compared to the ISOC algorithm; the designed RFID power adaptive regulation algorithm is improved by 259% and 203% compared to the ISOC algorithm in terms of slot efficiency and time efficiency 203%.
An abnormal user login behavior detection method of industrial control system based on multi-dimensional probability analysis
Authors: Zhang Wenzhe , Zeng Chuyang , Cao Yang , Qin Zuming , Chen Haiguang 
Published: April 2022

Abstract
Aiming at the poor detection performance of user abnormal login behavior in the complex environment of industrial control system, a new method based on multi-dimensional probability analysis is proposed. First of all, login time, source, destination and other login parameters are collected. Secondly, the abnormal probability of login parameters is calculated. Thirdly, the login transfer probability among multiple destination hosts is calculated. Finally, through the comprehensive analysis of the above three probabilities, an abnormal login behavior detection model based on multi-dimensional probability analysis is constructed. Experiments show that this method can effectively identify the abnormal login behavior of users caused by various types of network attacks.
MATLAB simulation of performance evaluation model of time-frequency analysis method based on SVM
Authors: Gao Zejun , Cao Fei , He Chuan , Song Tianli 
Published: April 2022

Abstract
Time-frequency analysis is a prerequisite for intelligent recognition of radar signal types based on deep learning network. Deep learning uses Convolutional Neural Network (CNN) to automatically extract the time-frequency images (TFIs) features of radar signals to achieve intelligent recognition of radar signal modulation methods. However, the quality of TFI generated by different time-frequency conversions is usually different to a certain extent. At present, the selection of time-frequency analysis methods in many pieces of research is mainly done through the feature differences of TFIs. There are certain subjective factors, which cannot provide a reference for subsequent research. This paper proposes a time-frequency analysis method performance evaluation model based on Support Vector Machine (SVM). The simulation results show that the evaluation model can make an objective and optimal choice based on the data, and can provide a valuable reference for subsequent research.
Parameter Identification of Lithium-ion Battery Equivalent Circuit Model Based on Limited Memory Recursive Least Squares Algorithm with Variable Forgetting Factor
Authors: Peng Xianghua , Yin Jingyuan , Sun Longfei , Ye Zeyu , Wei Tongzhen 
Published: April 2022

Abstract
Equivalent circuit method is the most widely used methodology in dynamic modeling of lithium-ion battery. An equivalent circuit with second-order RC network is used to model lithium-ion battery, and a limited memory recursive least square with variable forgetting factor (VFF-LMRLS) is proposed to identify the model parameters in this paper. Firstly, based on the current and voltage data measured from the battery cyclic discharging experiment, the VFF-LMRLS algorithm is used to identify the time-varying parameters of equivalent circuit model. Then, the model verification system is constructed by taking the average value of the identification results in the stable stage as the component parameter value of the equivalent circuit. Finally, through the comparative experiment and analysis with the variable forgetting factor RLS (VFFRLS), it is verified that the terminal voltage error of the proposed method is smaller, indicating that the identified model parameters are closer to the actual parameters.
Research on control of new energy vehicles based on intelligent power conversion
Authors: Wang Yiyun , Feng Ling , Fan Guangfa 
Published: April 2022

AbstractThe conventional control method of new energy vehicles has the problem of imperfect driving motor coupling model, which leads to excessive car current. A control method of new energy vehicles based on intelligent electricity conversion is designed. For lithium battery performance parameters, computing power values change, the drive motor coupling model was established based on intelligent power conversion, the direct superposition of two motor torque, extract the new energy vehicles basic range of needs power, optimization of braking energy recycling process, hierarchical control type, the different working conditions, set up new energy auto control mode. Experimental results: The mean current of the new energy vehicle control method in this paper and the other two methods are 32.94A, 42.46 A, 42.96 A respectively, indicating that the new energy vehicle control method in the text is more effective.
Research on Energy Supply and Demand Forecast and Carbon Neutralization Path Based on Grey-Monarch Butterfly Optimization Model
Authors: Su Qi , Wang Haibo , Shi Xiaochen , Li Guixin , Sun Kuo 
Published: April 2022

Abstract
Energy and electricity are the key areas for China to achieve the double carbon target, and accurate forecasting of future energy supply and demand and carbon emissions is beneficial to develop a feasible path for low carbon transition. The gray prediction model GM (1, 1) is one of the most widely used dynamic prediction models in the field of energy forecasting, but it requires high raw data and the model may fail when the development coefficient of GM (1, 1) is large. On the other hand, the gray action of GM (1, 1) directly determines the model prediction accuracy, this paper introduces a novel population intelligence algorithm monarch butterfly optimization (MBO), which has excellent performance in practical optimization problems, into the optimization process of gray action of GM (1, 1), and proposes a new Gray-Monarch Butterfly optimization prediction model to realize the prediction. By comparing the prediction data with the classical literature, the effectiveness and superiority of the proposed Gray-monarch butterfly optimization prediction model are confirmed.Finally a carbon neutral pathway is given for Tianjin based on the prediction results.
New energy vehicles taking into account user needs participate in the FM model
Authors: Gu Jinhui , Guo Chunlin 
Published: April 2022

Abstract
In this paper, a new energy vehicle participation FM model considering user needs is established and verified by simulation. It has been proved that the model can guarantee user travel needs. The new energy vehicle participation FM model is divided into three parts: energy storage calculation, power constraint and energy constraint. Energy storage calculation is to model and analyse the available energy of new energy vehicles participating in FM, and get the variation of participating FM capacity in a day considering the user SOC demand. Power constraint is the charge and discharge power constraint of new energy vehicles, which is the necessary condition to ensure that new energy vehicles do not violate the technical conditions of new energy vehicles and ensure that the energy constraint of user demand participates in frequency modulation.
Research on technical Optimization of solid regenerative electric Boiler
Authors: Li Ting , Zou TongHua , Li HongXuan 
Published: April 2022

Abstract
This paper briefly introduces the principle and device of solid heat storage in electric boiler, analyzes the advantages and necessity of solid heat storage boiler technology. The development status and application examples of heat storage technology for solid thermoelectric boiler are introduced, and the efficiency of solid heat storage device is verified through experiments.
Research on fracture trend of repeated fracturing
Authors: Liu Mengxuan 
Published: April 2022

Abstract
The fracture trend of repeated fracturing is directly related to the effect of repeated fracturing and whether it can effectively expand the swept volume. In this paper, a large-scale hydraulic fracturing process simulation device is used to conduct laboratory experiments on fractured reservoirs to study the initiation and extension of repeated fracturing fractures under different stress states. The experimental results show that new fractures are easy to extend from the natural fractures near the wellbore, and the initiation pressure of the secondary fracturing is reduced due to the inducing effect of the initial fractures. The conclusion has a certain guiding function for the prediction of repeated fracturing fracture propagation in field fractured reservoirs, and it is of great significance for fracturing construction.
Experimental Study on Flame Propagation Characteristics of Premixed Laminar Combustion of Hydrogen-rich Coke Oven Gas
Authors: Li Ping 
Published: April 2022

Abstract
Coke oven gas (COG) is the main by-product of the coking industry, with a hydrogen content of more than 50%. Upon purification, COG can be used as a hydrogen-rich, efficient, high-quality alternative fuel, to improve the energy structure, alleviate global warming, and reduce polluting emissions. In the present paper, a constant-volume combustion experiment bench was established to carry out the premixed laminar combustion experiments of the COG-air mixture. A high-speed camera was used to record the flame propagation process and the effects of the influencing factors such as fuel-air equivalence ratio, initial pressure and H2 concentration on the flame speed. The results indicated that when Φ= 1.1, i.e., for a relatively concentrated fuel, the flame speeds of the stretched and unstretched flame both reached a maximum. With the increase in initial pressure, the flame speeds of both the stretched and unstretched flame decreased. As the H2 concentration increased, the propagation speeds of the stretched and unstretched flame both increased and the enhancements were more obvious; mainly since hydrogen combusts faster than methane.
Study on the condensable particulate matter removal characteristics of air pollution control devices in a Chinese ultra-low emission coal-fired generating unit
Authors: Zhang Ziwei , Zhao Bing , Wang Haigang , Zhou Baozhu , Zhao Qi , Dong Libin , Li Xukai , Jin Xuliang , Zhang Ziwen , Zhang Meng , Ma Lin , Li Shendong , Jia Jia , Xu Chao 
Published: April 2022

Abstract
Condensable particulate matter (CPM) belongs to PM25. In addition, it is one of PM2.5 main contributors. But CPM emissions are often overlooked in coal-fired power plants. As a result, this paper studies the removal characteristics of (CPM) from air pollution control devices (APCDs) in a Chinese ultra-low emission coal-fired generating unit. The quality and content of flue gas particles, including CPM and filterable particulate matter (FPM), were measured at four points: the selective catalytic reduction (SCR) input, air preheater (APH) inlet, electric precipitator (ESP) outlet, and wet flue gas desulphurization (WFGD) outlet. The removal characteristics of each APCD were studied. Within the scope of this study, it is found that: ® CPM emission concentration accounts for 94.9% of total particle emission concentration, and is higher than the ultra-low particulate matter outflow guidelines for coal-fired power plants in China; ©WFGD has the highest CPM inorganic component removal radio of 97.02%, and ESP has the highest CPM organic component removal radio of 58.46%, among APCDs; ©SO4
2−, NH4
+, F−, and Cl− are the most abundant water-soluble ions in CPM.
Multi-objective distribution network planning method with distributed generation based on non dominated sorting differential evolution algorithm
Authors: Wu Lei , Zhang Hai , Hu Zhaojie , Wang Yinghua , Wang Hairong , Yang Hua , Fan Bin , Chang Hao 
Published: April 2022

Abstract
Combined with the specific problems of distribution network planning with distributed generation, this paper constructs a multi-objective optimization model of distribution network planning with distributed generation. According to the distributed generation distribution network layout planning with distributed generation, under the condition of uncertain load prediction value of distributed generation distribution network, taking the minimum voltage stability index, minimum network loss and minimum investment cost of distributed generation as sub objectives, a multi-objective programming model is established, and the model is solved by non dominated sorting differential evolution (NSDE) algorithm.
Simulation analysis of distribution network with distributed generation based on ADPSS
Authors: Wu Lei , Zhang Hai , Hu Zhaojie , Wang Yinghua , Wang Hairong , Yang Hua , Fan Bin , Chang Hao 
Published: April 2022

Abstract
In this paper, the steady-state and transient models of distributed generation are modeled and simulated based on ADPSS system. Through the analysis of the simulation results, the fault characteristics of the distribution network with distributed generation are extracted. Through the analysis of the steady-state and transient models with distributed generation and the impact of the access of distributed generation on the power quality of the distribution network, the fault characteristics of the distribution network with distributed generation are extracted, which lays a theoretical foundation for the location and capacity determination of distributed generation.
Electron transport at TiO2/perovskite interfaces by considering the defect effects: a first-principles insight
Authors: Pu Wenhua , Wang Jianwei , Xiao Wei , Li Xiaowu , Wang Ligen 
Published: April 2022

Abstract
The interface property of perovskite solar cells (PSCs) is very important, which can influence the electron transmission efficiency and stability of the cells. In this text, we have discussed the stability and bonding characteristics of PbI2/TiO2 interfaces by using the first-principles method. The PbI2/TiO2 interfaces have a high interfacial binding energy of -0.93 J/m2, where the Ti-I and Pb-O bonds could form. Furthermore, the electron transport at the interfaces has been analyzed by the partial density of states by comparing the clean interfaces and interfaces with different defects. The results show that the clean PbI2/TiO2 interfaces could cause a stronger internal electric field, which might make the electron-hole pairs separate more easily at the interfaces. Also, it is found that common defects VI and Ii are relatively easy to form at the interfaces. Some defects at low concentrations might have little effect on the electron transport at the interfaces, while they are harmful only when the concentration increases. However, VPb with a high formation energy could adversely affect the electron transmission even at low concentrations. Controlling the defects at the interfaces is essential to improve the power conversion efficiencies (PCEs) and stability of PSCs.
The Design of Thermodynamic System Instrument & Control for the Daya Bay Turbine High Pressure Cylinder Retrofit
Authors: Hu Yanjun , Wan Tian , Pan Weiwei , Liu Shengzhi , Li Min 
Published: April 2022

Abstract
Before the retrofit, the equipments of the turbine high pressure cylinder of the Daya Bay Nuclear Power Plant is of the old technology. The design of the leaf shape and the steam runner is not advanced, and the equipments are aged after more than 20 years’ operation. From the point of increasing the power and improving the reliability of the equipments, it’s decided to retrofit the turbine high pressure cylinder of Daya Bay Nuclear Power Plant unit 1 and unit 2. The retrofit relates to the design and modification of many secondary loop thermodynamic systems’ instrument & control parameters, in order to match with the first loop reactor control systems, and also the mechanism and electric systems, it’s necessary to verify and optimize these designed parameters of the units after the retrofit. This design and analysis method and experience of the secondary loop thermodynamic systems’ instrument & control parameters can be referred by the similar retrofit of other nuclear power plants.
Influence of electric vehicle distributed energy storage access on safety and stability of distribution network
Authors: Wang Shengfeng , Yang Shaobing 
Published: April 2022

Abstract
This paper proposes a distributed energy storage control strategy for electric vehicles to improve the security and stability of distribution network when electric vehicles are connected. Firstly, the load characteristics of electric vehicles are investigated, and the optimal power flow model including energy storage power station, electric vehicle charging station considering V2G and distribution network is established. The objective function is established to minimize network loss, voltage deviation and load peak to peak. The problem is transformed into a mixed integer second-order cone optimization problem for solution, and based on the analysis of distributed energy storage model and constraints, the distributed energy storage control strategy of electric vehicle is established. Through the solution analysis in IEEE33 node system, it can be concluded that using this strategy can improve the power quality of distribution network and effectively improve the security and stability of distribution network.