bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical simulation of flow control in lift-increase effect using air-blowing
Authors: Kao Xu , Xiaojun Zhu , Jianghong Zhu , Feng Li 
Published: January 2020

Abstract
In this study, numerical simulation method is applied to analyze the aerodynamic characteristics of the airfoil influenced by the parameters of the jet port. The NACA0012 airfoil is studied by using the incompressible Navier-Stokes equations with the structural grid. The influence of air-blowing speed, width, position of the jet and angle of attack on the aerodynamic characteristics of the airfoil is presented. The results show that, for the airfoil, the lift coefficient can be impacted by blowing speed, width and position of the jet to a certain extent. Jet control can reduce the stall angle of attack of the airfoil, but before the stall angle of attack, the effect of lift-increase is almost same at different angle of attack. A basis for the jet design and the selection of jet position along the wing is provide.
Co-simulation of transfer alignment and CAN busbased on CANoe-MATLAB
Authors: Yongsheng Wang , Wei Luo , Fan Zha 
Published: January 2020

Abstract
The research on rapid transfer alignment for INS on moving base is very important in the field of the inertia technology. In order to build the real-time CAN bus network simulation system with the velocity matching model of the transfer alignmentalgorithm, CANoe(CAN open environment)-MATLAB interface is applied. The CANoe-MATLAB interface is connected with MATLAB/Simulink which has powerful modeling functions and CANoe which has perfect function of bus simulation. Modeling the transfer alignmentalgorithm by MATLAB/Simulink and providing the real-time CAN bus network communications in HIL(hardware-in-loop) simulation by CANoe. The simulations tests show that co-simulation system built by the CANoe and the MATLAB/Simulink performs favorably with rapid convergence and validity.
Effect of twist angle on starting capability of a Savonius rotor – CFD analysis
Authors: Zakaria Ahmad , Ibrahim M S N 
Published: January 2020

Abstract
Starting capability is an important feature of any high performance wind turbine. This parameter is usually referred to the rotor static performance. In this paper, the results of CFD analysis on four Savonius rotor designs are presented with the objective of investigating the effect of twist angle on the starting capability. Essentially it is a comparison between a conventional Savonius rotor and three helical rotors with 0°, 90° and 180° twist angle. The proposed methodology was first validated against the published wind tunnel test data. The best parameters were then used to evaluate the static performance of the four Savonius models. It is observed that helical Savonius rotor contributes to positive static torque coefficient for all rotor angles. The rotor with 90° twist angle gives the highest average static torque coefficient of 0.442 followed by 0° twist angle rotor and 180° twist angle with static torque coefficient of 0.434 and 0.385 respectively. This improvement in static torque coefficient will not only contribute to the better starting capability but also to the overall performance of Savonius rotor.
Finite element analysis of automotive arm brake pedal for rapid manufacturing
Authors: Sargini MIM , Masood SH , Palanisamy Suresh , Jayamani Elammaran , Kapoor Ajay 
Published: January 2020

Abstract
Current automotive arm brake pedal uses conventional manufacturing method such as metal press and CO2 welding processes, which are known to be heavy, costly and demand higher quality control. This paper will discuss how finite element analysis can be applied to optimise the design of a brake pedal for Rapid Manufacturing application based on current additive manufacturing process to produce automotive brake pedals with reduced material weight. The performance of the new design is compared with the current design of arm brake pedal to realise the potential of rapid manufacturing to save time and cost in automotive parts.
Research on improvement of high utility pattern mining algorithm over data streams
Authors: Guo Feng , Li Yuqiang , Li Lin 
Published: January 2020

Abstract
Aiming at the problem that the existing algorithms for high utility pattern mining over data streams based on sliding window have multiple datasets scans or redundant items, an efficient HUIGRT algorithm for mining high utility patterns over data streams based on global revision header table is proposed in this paper. First, the global revision header table and the utility tree are constructed. The global revision header table is used to store the items and transaction utility of the current data domain that need to be processed, and the utility tree is used to store all of the utility information on the item sets in the transactions to avoid multiple datasets scans. Then, this algorithm can mine all high utility patterns using the global revision header table and the utility tree. Finally, the redundant items are deleted by revising the global revision header table, meanwhile the utility tree is updated to fill in new data. This paper compares the algorithm with the existing high efficiency algorithm HUPMS and HUM-UT on the three datasets with different sparse: Mushroom, T10.I4.D100K and Retail. The results show that the space-time performance of HUIGRT algorithm is better than the two other algorithms.
DBSCAN-KNN-GA: a multi Density-Level Parameter-Free clustering algorithm
Authors: Mu Bin , Dai Meng , Yuan Shijin 
Published: January 2020

Abstract
DBSCAN is a popular tool to analyse datasets which can effectively discover clusters with arbitrary shapes. However, it requires two input parameters which are difficult to be determined, according to the fact that the performance of clustering result depends heavily on user-specified parameters. In addition, it uses global parameters which are not appropriate to those multi-density datasets. Aiming at these problems, we propose a parameter-free algorithm to perform DBSCAN with different density-level parameters. We select some classical datasets and a TLC taxi trip record used for experiments to compared our proposed algorithm with the original DBSCAN to evaluate the performance of our improved DBSCAN. The results show that the proposed algorithm is capable for efficiently and effectively detecting clusters automatically with variable density-levels. Compared with original DBSCAN, the proposed algorithm can discover more noise points and its execution accuracy is higher.
Optimal mission planning of active space debris removal based on genetic algorithm
Authors: Chen Yang , Bai Yuzhu , Zhao Yong , Wang Yi , Chen Xiaoqian 
Published: January 2020

Abstract
With the increasing space activities, the space debris has seriously deteriorated the safe performance of the on-orbit spacecraft and has attracted much attentions. To reduce the rising influence of the space debris and improve the safe performance of the space mission, the three-stage removal strategy for space debris is proposed in this paper. Firstly, the multiple spacecrafts including one main spacecraft and some following spacecraft for space debris removal mission is developed. Then, the fuel, time and the quantity of the following spacecraft are defined as the constraints. Moreover, using the minimum fuel consumption as the optimal object, the mathematical model of the debris removal problem is established. Finally, the genetic algorithm is applied to solve this problem. Compared with the prior space debris removal strategy, the proposed three-stage space debris removal can effectively reduce the fuel consumption. Numerical simulation verifies the effectiveness of the proposed space debris removal scheme.
Statistical testing data generation for UAS
Authors: Sui Jianwei , Gong Yunzhan , Jin Dahai , Wang Yawen 
Published: January 2020

Abstract
The reliability of UAS(Unmanned Aircraft Systems) is of vital importance in practical applications. The most effective method to guarantee the reliability of UAS is to test UAS with multiple types of testing data. In this paper, we propose a method for the testing data generation of UAS in consideration of the combination and weight of the input data, which improves the availability of the method in UAS. Then we make four experiments to verify the effectiveness of the proposed method: (1) the random testing data generation experiment; (2) testing data generation considering the combination of input data experiment; (3) testing data generation considering the combination and weight of input data experiment; (4) the history data from the actual usage situation. Experiments results show that the method proposed in this paper is closer to the reality. At last, we analyse the code coverage by using Code Test System(CTS). And the analysis reflects that the proposed method has a better coverage than other methods.
A scheme of D2D-based delay analysis and vehicle relay algorithm in VANET
Authors: Zhao Yinze , Jiang Lingyun 
Published: January 2020

Abstract
The D2D-based VANET (Vehicular Ad-hoc Network) makes up for the defects of the traditional IEEE 802.11p network, and has the advantages of low latency and high throughput. In order to reduce the delay of message transmission between vehicles, a D2D-based hybrid V2I/V2V (Vehicle-to-Infrastructure/Vehicle-to-Vehicle) IoV (Internet of Vehicle) architecture was considered, which firstly clusters vehicles on the road according to motion consistency. The message is transmitted between the clusters, and in addition to being transmitted through the RSU (Road Side Unit), it can also be relayed by establishing a D2D link between the vehicles. Based on the SINR (Signal to Interference plus Noise Ratio) and the minimum capacity constraint, the optimal transmission path is selected by predicting the delay of each cellular link and D2D link to reduce the end-to-end delay. In addition, for accurate modeling and analysis, a three-step method is used to predict and compare end-to-end delays. The simulation proves that this relay algorithm significantly reduces the delay compared with other methods.
An improved authentication scheme for Internet of things
Authors: Xie Zhongliang , Jiang Lingyun 
Published: January 2020

Abstract
Designing a secure authentication scheme is of great importance to the practical applications of the Internet of Things (IoT). In 2017, Wang et al. proposed an improved mutual authentication scheme for the embedded devices and a server over Kalra et al.’s and Chang et al.’s. And they provided a formal proof for their scheme. However, their scheme is still insecure and cannot resist against key compromise impersonation (KCI) attack. In the paper, we first demonstrate the shortcoming of Wang et al.’s scheme, and then we carry out an improvement over Wang et al.’s scheme to address the revealed issue by using an auxiliary server.
Fault diagnosis of power transformer based on tree ensemble model
Authors: Liu Yunfei , Li Jing , Qiao Lin , Chen Shuo , Liu Sai , Liu Jiahua 
Published: January 2020

Abstract
During the running operation of the oil-immersed transformer, some gases may be dissolved in the insulating oil which can be used to diagnose the incipient failure of the power transformer. This is the Dissolved Gas Analysis (DGA). This paper proposes a power transformer fault diagnosis method based on tree ensemble model (Extreme Gradient Boosting, XGBoost): constructing a large number of classification and regression trees (CART) to fit the residuals obtained by each learning. Compared with the commonly used SVM and BPNN methods, our method has a significant improvement in accuracy, F1-score, precision and recall.
Real time outlier monitoring for power transformer fault diagnosis based on isolated forest
Authors: Shen Li , Du Hongjun , Liu Shuji , Chen Shuo , Qiao Lin , Liu Sai , Liu Jiahua , Li Kexin , Li Jing 
Published: January 2020

Abstract
In order to improve the accuracy and efficiency of transformer fault detection, this paper uses the isolated forest algorithm combined with the historical transformer characteristic gas data to establish the characteristic gas outlier recognition model, and then uses the uncoded ratio method to establish the abnormal event strategy and abnormal event library for the transformer historical fault information. Finally, the state of the transformer is diagnosed based on the established outlier detection model and the exception event library. The experimental results show that the proposed method has a great improvement in the detection efficiency and stability of outliers, and is more accurate in transformer fault diagnosis combine with abnormal event database.
Design and implementation of fault injection based on abstract syntax tree of C Program
Authors: Yu Hang , Gong Huiquan , Wang Yawen 
Published: January 2020

Abstract
Mutation testing is a fault-based technique to evaluate test quality by injecting bugs in programs under test. Traditional manual fault injection has many disadvantages such as inefficiency, large workload, low compilation success rate and so on. To improve the efficiency of fault injection, this paper aims to develop an automatic tool that supports fault injection for programs written in C. This paper first introduces the types of mutation operators and injection rules. Then, it proposes a framework, which mainly includes four modules: parser, fault location extraction, fault generation and data model. Finally, experiments show that our tool not only aids to produce a large number of program faults but also provides syntax-correct mutant programs, which would be further used in mutation testing.
Video stalling detection method
Authors: Yu Shushi , Wang Na 
Published: January 2020

Abstract
ISP providing network access services has a huge user group; therefore the competition between the ISP providers is extremely fierce. Under the situation that video occupies the most of network flow, providing high satisfaction network services for video users can effectively improve the competitiveness of ISP, in which video Stalling plays an important index. For protecting the privacy of users, most video websites use HTTPS as the application layer protocol for video transmission, so ISP could not get the playback of video users by parsing HTTP packets, and could not directly detect the video users. In this assay, a new method, protocol transport layer Video Stalling Detection Method based on HTTPS is proposed. This method can effectively match the YouTube, Youku, Letv, Tencent video and other major video sources of the stalling event.
Effects of residual stresses and shape deformities on failure of rollover protective structures
Authors: Abou-Hanna J. , Tahamtam S. , Kumareshan V. 
Published: January 2020

Abstract
Rollover Protective Structures (ROPS) are manufactured typically from components that require forming such as pipe bending process. The manufacturing process creates residual stresses and shape deformities in the pipe structure. These parameters in turn may affect the strength of the pipe to bear the load during rollover. Conventional modeling does not capture the effects of residual stresses and shape irregularities in the pipe resulting from forming. A detailed analysis is therefore required to determine the significance of the manufacturing process on the strength of ROPS. Finite Element Method (FEM) is employed to simulate the bending process. Residual stress and deformation data from bending process analysis are used to further investigate failure of the structure under different loading conditions. The maximum load the structure can withstand, before it loses its stiffness, under a specific displacement is referred to here as failure load. Results revealed that there is a significant difference in the failure loads due to presence of bending process induced residual stresses and due to shape deformities. The model with the residual stresses and shape deformities has higher failure loads as compared to the conventional model. It also has been noted that residual stress is the main contributing factor to this increase in failure load. Parametric study was performed to investigate the effect of pipe wall thickness and radius of the formed bend on the failure loads.
Study on the impact of LNG carrier entering and leaving port on the passing capacity of narrow channel
Authors: Huang Tai-kun , Pang Ran , Wang Yuan-zhan , Yang Xing-yan , Jia Han 
Published: January 2020

Abstract
As a clean energy source, LNG has been widely used. LNG ships need to enter and leave the port area through the channel. With the development of the port, the ship traffic will gradually increase. As a manual excavation of narrow and long channel, its passing capacity will become a constraint factor for LNG receiving stations and port development. In this paper, the system simulation method is used to study the impact of LNG ship entry and exit on the passage capacity of the channel, which can provide basis and reference for the planning, site selection and channel construction of the LNG receiving station.
Information cascade prediction based on T-DeepHawkes model
Authors: Wang Shijie , Zhou Lihua , Kong Bing 
Published: January 2020

Abstract
It is an important research point of social network analysis to predict future propagation range of information based on its early propagation characteristics. DeepHawkes model combines the Hawkes model in the traditional methods with deep learning, which not only inherits the high interpretability of Hawkes model, but also contains the high prediction ability of deep learning. However, the DeepHawkes model ignores the effect of the text content of the information on the propagation. Therefore, on the basis of DeepHawkes model, this paper further considers the influence of the text content on the diffusion, and proposes a T-DeepHawkes model which merges a topic classification model into DeepHawkes model. The experimental result shows that T-DeepHawkes model has a better prediction accuracy.
Fine-grained analysis method for Android volatile memory
Authors: Feng Peijun , Li Qingbao , Chen Zhifeng 
Published: January 2020

Abstract
Android dominates the mobile operating system market. Volatile memory analysis of Android devices has been the focus of research on mobile forensics technology. However, due to the semantic gap between the kernel and the volatile memory allocator, existing Android volatile memory analysis methods are coarse-grained. With the volatile memory capacity of Android devices increasing, these methods cannot satisfy the need of Android volatile memory analysis accuracy. In this paper, we first discuss the address space layout of Android processes and the management mechanism of Jemalloc, the default Android volatile memory allocator. Then, we bridge the semantic gap by utilizing the boundary auto alignment feature of the data structure of Jemalloc. Finally, we propose a Fine-grained Analysis Method for Android volatile Memory, called FAMAM. Experimental results shows that FAMAM has an accurate data analysis capability as well as a good robustness. In addition, we successfully use FAMAM to discover new storage patterns for username and password of Wechat.
Reservoir inflow prediction using a hybrid model based on deep learning
Authors: Luo Bin , Fang Yang , Wang Handong , Zang Di 
Published: January 2020

Abstract
Reservoir inflow prediction plays a significant role in the field of hydrological prediction. Accurate and reliable prediction of reservoir inflow is the key to flood control decision. In this paper, we combine the deep belief network (DBN) with the Long Short-Term Memory (LSTM) to present a hybrid model based on deep learning (HDL) for reservoir inflow prediction. We take a full consideration of the basin flow and rainfall factors, which significantly affect the inflow flow. According to the rainfall data, we divide the corresponding flow data into two cases: rain and no rain. The proposed approach consists of three parts: we apply the DBN to learn the characteristics of the flow data and get predicted values of the reservoir inflow in case of rain and in case of no rain respectively. Then, we use the basin rainfall data and adopt the LSTM to fit the differential between the predicted inflow value generated by DBN in case of rain and the real inflow value, and get the predicted differential. Finally, the outputs of these three parts are added to obtain the final predicted result. Experiments are evaluated by the historical flow and rainfall data of a reservoir in China, and the results have proved that our method is effective and has higher prediction accuracy.
Rapid entry trajectory planning without segmentation
Authors: Jun Li , Zheng-yu Jiang 
Published: January 2020

Abstract
A new rapid entry trajectory planning algorithm for hypersonic glide vehicles is presented in this paper. Firstly, build a new technique to calculate the boundary of the control variable that satisfy the path and control constraints without using quasi-equilibrium glide condition (QEGC). Secondly the optimal control variable is expressed as function of current state. Then combine two of them by control coordination. Finally, introduce one or two constant parameters to satisfy the final state condition. The approach is tested using the CAV model. This algorithm is efficient and will provide feasible trajectory without reversal of bank angle. It can apply for entire entry process, segmentation and the QEGC is unnecessary.
Multi-echelon inventory allocation of multi-indenture spare parts considering maintenance ratio
Authors: Xia Jun , Feng Yunwen , Xue Xiaofeng , Lu Cheng 
Published: January 2020

Abstract
The existing multi-indenture spare parts maintenance has a certain maintenance proportion, a multi-echelon inventory allocation method with maintenance ratio for multi-indenture spare parts was proposed. Multi-echelon inventory allocation model for multi-indenture spare parts was established, which integrates maintenance rate into METRIC (Multi-echelon technique for recoverable item control) model, and then the marginal analysis method was applied to solve this model. First, the theory of multi-echelon inventory allocation model with maintenance ratio for multi-indenture spare parts was elaborated, and the mathematical model was established with the spare parts inventory cost as the optimization objective, the fleet availability as the constraints. Then, the procedure of solving the model by using marginal analysis method is discussed. Finally, taking an aircraft multi-indenture LRU (Line repairable units) as the research object, multi-echelon inventory allocation was investigated by considering maintenance ratio. The feasibility and effectiveness of the developed method are verified by an example analysis.
Research on integrated logistical support systems of medium altitude long endurance UAS
Authors: Haoran Deng 
Published: January 2020

Abstract
The Medium Altitude Long Endurance (MALE) UAS in modern warfare is becoming more and more widespread, which makes the UAS more and more frequently used in warfare. The equipment characteristics of UAS are embodied in the following aspects. The composition of UAS is complex, Ground Stations is distributed deployment, Mission payloads can flexibly change, and the whole system need highly cooperative ability between air and ground. That put forward higher requirements for integrated logistical support system of UAS. Aiming at the unique support characteristics of UAS, this paper constructs the integrated support system of UAS based on the integrated support system of traditional aircraft, which is mainly reflected at five aspects: support organization, support resources, support function, support process and support management. This systems can ensure the integration of all support links in the actual process of UAS support, and meet the operational and mission requirements of UAS to the maximum extent.