bluuebunny/crossref_metadata_2025_split huggingface dataset:
Anisotropic failure behavior of ordered intermetallic TiAl alloys under pure mode-I loading
Authors: Neogi Anupam , Alam Masud , Hartmaier Alexander , Janisch Rebecca 
Published: July 2020

Abstract
Whether a metallic material fractures by brittle cleavage or by ductile rupture is primarily governed by the competition between cleavage and dislocation emission at the crack tip. The linear elastic fracture mechanics (LEFM) based criterion of Griffith, respectively the one for dislocation emission of Rice, are sufficiently reliable for determining the possible crack tip propagation mechanisms in isotropic crystalline metals. However, the applicability of these criteria is questionable when non-cubic, anisotropic solids are considered, as e.g. ordered intermetallic TiAl phases, where slip systems are limited and elastic anisotropy is pronounced. We study brittle versus ductile failure mechanisms in face-centered tetragonal TiAl and hexagonal Ti3Al using large-scale atomistic simulations and compare our findings to the predictions of LEFM-based criteria augmented by elastic anisotropy. We observe that the augmented Griffith and Rice criteria are reliable for determining the direction dependent crack tip mechanisms, if all the available dislocation slip systems are taken into account. Yet, atomistic simulations are necessary to understand crack blunting due to mixed mechanisms, or shear instabilities other than dislocation emission. The results of our systematic study can be used as basis for modifications of the Griffith/Rice criteria in order to incorporate such effects.
Simulation of attosecond transient soft x-ray absorption in solids using generalized Kohn–Sham real-time time-dependent density functional theory
Authors: Pemmaraju C D 
Published: July 2020

Abstract
Time-dependent density functional theory (TDDFT) simulations of transient core-level spectroscopies require a balanced treatment of both valence- and core-electron excitations. To this end, tuned range-separated hybrid exchange–correlation functionals within the generalized Kohn–Sham scheme offer a computationally efficient means of simultaneously improving the accuracy of valence and core excitation energies in TDDFT by mitigating delocalization errors across multiple length-scales. In this work range-separated hybrid functionals are employed in conjunction with the velocity-gauge formulation of real-time TDDFT to simulate static as well as transient soft x-ray near-edge absorption spectra in a prototypical solid-state system, monolayer hexagonal boron nitride, where excitonic effects are important. In the static case, computed soft x-ray absorption edge energies and line shapes are seen to be in good agreement with experiment. Following laser excitation by a pump pulse, soft x-ray probe spectra are shown to exhibit characteristic features of population induced bleaching and transient energy shifts of exciton peaks. The methods outlined in this work therefore illustrate a practical means for simulating attosecond time-resolved core-level spectra in solids within a TDDFT framework.
A Hybrid Machine Learning Model with Cost-function Based Outlier Removal and Its Application on Credit Rating
Authors: Mu Aurora Y. 
Published: July 2020

Abstract
This paper establishes a methodology to build hybrid machine learning models, aiming to combine the power of different machine learning algorithms on different types of features and hypothesis. A generic cost-based outlier removal algorithm is introduced as a step of pre-process of training data. Using this methodology, an experimental hybrid machine learning model is implemented for a crediting problem, which combines three types of machine learning algorithms SVM, DT and LR. Different metrics of training time, recall, precision, F1 score, and AUC are evaluated and compared with the results from single model of single SVM, DT, and LR. The new hybrid models in general shows improvement in performance on multiple metrics. this study further demonstrated that one learning model may work well on certain type of variables but not as effectively on other variables. Well defined procedures can be followed to create hybrid models to combine the power of different learning models, which are superior in dealing with dataset with complicate feature selection. This methodology can be explored with other algorithms and applications.
The Shortest Path Planning Based on Reinforcement Learning
Authors: Wang Xiaoqi , Jin Lina , Wei Haiping 
Published: July 2020

Abstract
This paper proposes a shortest path planning of agent in an environment based on reinforcement learning. This method adopts the Q-learning algorithm, which has gained increasingly using in agent path planning recently. This algorithm can be fully designed to a reasonable environment model and applied to other professional fields. Yet, reinforcement learning in path planning still needs to improve the rate of convergence. The learning process takes several iterations and spends a long time to find the final path. A local optimal problem in search process deals with other explorations that provided shortest path for agent to take optimal actions. For the agent, the reward value of inclined movement is introduced to find the shortest path. The autonomous obstacle avoidance is utilized to obtain the optimal path in the process. Finally, the model of intelligent agent movement environment has established in matlab. The effectiveness of the algorithm has proved by simulation results.
Research on Application of Improved Random Forest in Medical Ultrasound Image Classification
Authors: Min Cui , Haijiang Zhu 
Published: July 2020

Abstract
Gray-scale ultrasound imaging methods are commonly used to assess synovitis in Rheumatoid Arthritis (RA) in clinical practice. This paper proposed an improved classification algorithm for ultrasound image of metacarpophalangeal joint in Rheumatoid Arthritis(RA). Using several features of gray scale co-occurrence matrix and improved Random Forest method to achieve automatic classification of Rheumatoid Arthritis(RA). Three grading experiments were carried out in the experiment: the first one is for the binary classification of grade 0 (normal) and grade 3 (lesion) ultrasound images of 80 × 50 pixels, 130 × 70 pixels, and 185 × 90 pixels. The second is for the binary classification of grade 0 (normal) and grade 1/2/3 (lesion) ultrasound images of 80 × 50 pixels, 130 × 70 pixels, and 185 × 90 pixels. The third is for four-grade classification of ultrasound images at grade 0(normal), grade 1(mild), grade 2(moderate), and grade 3(severe). Conclusion shows that use the method of combing gray scale co-occurrence matrix and improved Random Forest algorithm can achieve automatic classification of Rheumatoid Arthritis(RA), which has a high Classification Accuracy.
Tobacco-disease Image Recognition via Multiple-Attention Classification Network
Authors: Sun Y , Wang H Q , Xia Z Y , Ma J H , Lv M Z 
Published: July 2020

Abstract
The recognition of disease images is of great importance in modern tobacco agriculture. Compared with other plant diseases, the early disease spots of tobacco are highly similar, and the quantity of tobacco disease images is scarce due to the difficulty of data collection. As such, the image recognition of early tobacco diseases is challenging. In response to these problems, this study proposes a novel image-classification method, namely, Multiple-Attention Classification Network (MACN). It improves the existing image classification network from two aspects: 1) the acquisition of low-level features based on transfer-learning and 2) the extraction of high-level features by modelling the multiple dependencies in low-level features. The pretrained model is used to obtain the initial feature map, thereby breaking the limitation of small-scale dataset. A novel multiple-attention module (MAM) is designed to learn fine-grained differences between classes. To evaluate the proposed method, this paper conducts experiments on field-collected images. Results show that this method is superior in identifying early tobacco diseases and has demonstrated better robustness when dealing with the actual field images with complex factors.
Application of Neural Network Based Knowledge Graph in Vertical Industry
Authors: Zhao Xiaohong , Zhao Yang , Wang Wenting , Ren Tiancheng , Zhang Hao 
Published: July 2020

AbstractKnowledge graph was mentioned first by Google, now is used to refer to a wide variety of large-scale knowledge bases, in both general areas and vertical industry areas. Techniques such as knowledge fusion, knowledge extraction, knowledge reasoning and knowledge expression are key techniques in knowledge graph which need to be researched urgently. Neural network technologies have achieved hugely these years, the technique has been readily applied to a variety of practical engineering problems such as pattern recognition, signal processing and control systems. In this work, we represent how to implement the neural network technique to knowledge reasoning technology, to achieve the completion of knowledge base, to predict hidden relationships among entities through reasoning inside a specific knowledge base. We also show the possibility of knowledge graph applied to one vertical industry, namely the electrified power grid industry. This paper shows that knowledge graph can apply to the whole power processing procedures, such as electric power producing, operating and marketing procession, and electric power equipment operation and maintenance, as well as customer service.
Multi-Sensor Data Fusion Algorithm Based on BP Neural Network
Authors: Shuai Liu 
Published: July 2020

Abstract
In multi-sensor detection system, the application of multi-sensor accurate detection system parameters is limited due to the existence of measurement noise. Using multi-source data fusion technology can be more accurate, timely detection and data processing system. Data fusion is a basic function in humans and other biological systems. In this paper, in order to make the system adaptive multi-source data fusion, using the BP neural network algorithm is a good way to deal with incomplete test data and test the noise problem. In this paper, the characteristics of three levels of data fusion and the derivation process of BP neural network algorithm are introduced in detail. In order to verify the role of BP neural network algorithm in the process of detection system filtering, a MATLAB simulation experiment is carried out. The experimental results show that the BP neural network algorithm can effectively reduce the measurement error of multi-sensor detection system and improve the detection accuracy.
Anti-reconnaissance Model of Host Fingerprint Based on Virtual Node
Authors: Zhang Tao , Lu Bin , Li Ding , He Kang , Zhu Yuefei 
Published: July 2020

Abstract
Aiming at the problem of insufficient defense ability of fingerprint detection, the anti-reconnaissance model of host fingerprint based on virtual node is proposed. The model constructs periodically reconfigurable virtual nodes, dynamically camouflages the fingerprint information of the host to deceive the detector, and redirect attack traffic targeting virtual nodes to honeypots that can capture and analyze attack behavior. Honeypot, as an active defense technology, can effectively improve the model’s defense capabilities. This paper introduces probabilistic models for the defense model to provide a deeper understanding of the theoretical effect their parameters have for cybersecurity, which quantifies the impact of different parameters on the probability of attack success, such as the number of probes, number of honeypot mapping rules, the virtual node deception rate, the honeypot detection rate and allowable losses. Furthermore, our prototype system using Software Defined Network (SDN) and Data Plane Development Kit (DPDK) verifies the effectiveness of the model against reconnaissance.
Application of Directional Transmission Data Technology for Transmission Lines
Authors: Bai Demeng , Lv Xuebin , Wang Jian , Zheng Wenjie 
Published: July 2020

Abstract
The transmission line is realized by boosting the electric energy from the generator with a transformer and then connecting it to the transmission line through the circuit breaker and other control equipment. Structure, transmission lines are divided into overhead transmission lines and cable lines. At present, the real-time state acquisition of overhead transmission line mainly depends on the online monitoring device installed by itself. After the equipment obtains the monitoring information, it will send the relevant information to the remote monitoring system through the network formed by the network. However, the length of the working time of the online monitoring device is limited by the battery, so improving the power utilization rate of the transmission line monitoring device can significantly extend the working time of the device. This paper proposes adirectional transmission data technology which can solve the problems of high energy consumption and poor signal transmission quality of monitoring devices, and effectively extend the network lifetime.
Research on Channel Estimation Technology in Power Wireless Communication Based on MIMO Systems
Authors: Zhao Yang , Zhao Xiaohong , Ren Tiancheng , Liu Xin , Liu Donglan 
Published: July 2020

Abstract
Power wireless communication system has received more and more attention in recent years. At present, power wireless communication system has not been applied on a large scale in Shandong Province. With the proposal of ubiquitous power Internet of things (UPIOT), the State Grid Corporation of China (SGCC) is paying more and more attention to the construction of wireless networks. Shandong Province is gradually carrying out the construction of wireless communication networks, with corresponding research undertaken accordingly. This paper mainly studies the channel estimation technology of MIMO based power wireless communication system. According to the movement patterns of the user terminal, we manage to model the downlink channel, as well as give out the channel estimation and the corresponding training sequence design method. The relationship of channel capacity and training sequence length is verified by simulation, and the optimal value of training sequence length under given parameters is obtained. At the same time, the channel training optimization scheme is given by the use of channel correlation, in which the specific parameters are determined by the specific system.
Understandable Capacity Level Assessment Method for Networking Information-Centric System-of-Systems Interoperability
Authors: Wang Shuang-Ling 
Published: July 2020

Abstract
According to the characteristics of the networking information-centric system-of-systems, starting from the understanding ability of the mutual understanding subjects and the information sharing degree of the mutual understanding objects, this paper analyzes the influencing factors of mutual understanding of networking information-centric system-of-systems, extracts four evaluation attributes of education, situation, decision-making and control, describes the selection process of four evaluation attributes, analyzes the factors that affect the mutual understanding level of the networking information-centric system-of-systems in each attribute field, constructs the mutual understanding maturity model of the networking information-centric system-of-systems, analyzes the basic characteristics of each grade, constructs the mutual understanding grade model of the networking information-centric system-of-systems, analyzes the symbolic characteristics of each attribute corresponding to each grade, and puts forward the mutual understanding evaluation method of the networking information-centric system-of-systems, which provides guidance for measuring the ability of the networking information-centric system-of-systems to achieve information advantages and decision-making advantages.
Situation Plotting Algorithm and System Implementation Based on Web
Authors: Chen N Y , Chen L , Xiong W 
Published: July 2020

Abstract
Based on the integration of existing programming languages and framework technologies, this paper designs and implements a Web-based situational plotting system. This system combines Web-oriented and map services, it uses Leaflet framework and SVG (Scalable Vector Graphics) technology design and implementation, classify the symbols and generate corresponding situational symbol frameworks, which can be extended to different areas such as emergency rescue, logistics coordination, communication planning, and realize the situational symbol mapping and editing, map browsing, and plotting Sample set saving and uploading functions have good real-time, cross-platform and interactive features. At the same time, aiming at the problem of generating linear plotting symbols, a plotting algorithm based on Centripetal Catmull–Rom curve is proposed, which avoids sharp angle and self-intersections in the process of plotting linear symbols. The experimental results prove that compared with the traditional curve plotting algorithm, the proposed algorithm has the characteristics of fewer control points and good fitting effects, which improves the plotting efficiency.
Openflow Table Decision Method under Mimic Defense
Authors: Lei Rui , Li Chuanhuang , Tang Zhongyun 
Published: July 2020

Abstract
SDN is an implementation of network virtualization. It implements flexible control of network traffic by separating control and forwarding of network equipment. However, there are security issues in SDN networks. If the controller is hijacked, an attacker can manipulate the entire network and tamper with and intercept network traffic at will, causing serious problems such as network paralysis. Based on the overall architecture of the mimic controller, this paper proposes a controller flow table decision method for the key decision module. Finally, the decision algorithm is developed and implemented, and the attack defense verification is performed. The flow table decision method in this paper greatly improves the decision efficiency, and successfully implements the random active defense of the controller’s flow table.
Research on Quality Evaluation of Customized Plank Furniture Based on the Entropy Weight-Analytical Hierarchy Process
Authors: Li Ying , Xia Jianbing , Xu Yingcheng , Huang Dongmei 
Published: July 2020

Abstract
Custom-made furniture is a product of the marketplace. At present, the concept of whole-house custom-made furniture has not been clearly defined. In the market, custom-made furniture for the whole house is mainly designated panel furniture. As the custom furniture market continues to expand, consumer complaints about custom furniture are increasing. However, there is no effective supervision model for the new decoration business of whole-house custom-made furniture. The market for customized furniture is constantly expanding. Under the current circumstances, the regulatory approach is difficult to implement effectively for a time. Faced with this transitional stage, this article proposes to evaluate the quality risk of custom-made furniture by means of evaluation. Combined with industry experts’ discussion on consumer complaints and concerns, this article builds a customized furniture product quality evaluation system. According to the constructed quality evaluation system of customized furniture products, with the help of the basic theory of entropy weight method and analytic hierarchy process, determine the weight of each index. Finally, through empirical research, it is proved that this method has a strong operability for the quality evaluation of customized furniture.
A Design of Simulation Environment for Small Fixed-wing Aircraft
Authors: Wang Zi-Quan , Yang Cheng-Wei , Hu Xiao-Lin , Xiong Jing , Li Juan , Liu Chang 
Published: July 2020

Abstract
Multi-Agent Particle Environment (MPE) [1] proposed by OpenAI is applied to the study of multi-agent reinforcement learning strategies. However, the motion rules of the agent are excessively simplified. In order to make the environment more suitable to small fixed-wing aircraft, we have made following improvements: 1. The dynamic model of the agent in the MPE does not conform to the characteristics of the fixed-wing aircraft. In order to simulate the dynamic characteristics of the fixed-wing aircraft, a speed-related damping mechanism is introduced into the two-dimensional motion environment. 2. Since the MPE lacks the control module for single agent, the MPE cannot meet the challenges raised by single agent control. A two-layer controller is proposed which includes the outer layer (Total Energy Control System and L_1) and the inner layer (PID). 3. The MPE does not contain any decision module. In order to comprehensively study the collaborative decision-making behavior of aircrafts in target allocation, a swarm decision module is added to the environment. In addition, the concept of control period is introduced to reduce the gap between simulation and the actual situation. Finally, several simulations were carried out to test the improved Multi-Agent Aircraft Environment (MAE). The test cases include the outer layer with L1 and Total Energy Control System (TECS) algorithm in two dimensions, the PID inner layer control algorithm and the designed auction algorithm. The tests complete the process of single aircraft flight, Multiple aircrafts scan-search flight and Multiple aircrafts dynamical-waypoint flight, which verifies the effectiveness of MAE.
An Improved Electromagnetic Flowmeter
Authors: Li Xuejing , Yao Xinhong , Wang Chongyuan , Zhu Liang 
Published: July 2020

Abstract
Electromagnetic flowmeters are widely used in industrial production or control. However, the structure of the traditional electromagnetic flowmeter makes it restricted in some applications. With the development of medicine, aviation and other fields, people’s demand for multiphase flow measurement is increasing. However, the theoretical study of the multi-electrode electromagnetic flowmeter has always lacked systemicity, especially in the relationship between the tube wall, the electrical characteristics of the fluid and the output signal. Based on the existing research, this paper studies several key technologies of multi-electrode electromagnetic flowmeter. The structure of the existing electromagnetic flowmeter is improved, and a multi electrode non insulated pipe wall electromagnetic flow sensor is proposed. According to the equivalent circuit, the influence of the sensor structure on the measurement is analyzed, and the disturbance of the electrical characteristics of the tube wall to the output signal is obtained by combining with the weight function analysis. According to the results of theoretical calculation and analysis, a negative feedback circuit is designed. According to the experimental results, the accuracy and reliability of the novel electromagnetic flowmeter are verified.