Pinion Tooth Surface Generation Strategy of Spiral Bevel Gears

Aviation spiral bevel gears are often generated by spiral generated modified（SGM） roll method.In this style,pinion tooth surface modified generation strategy has an important influence on the meshing and contact performances.For the optimal contact pattern and transmission error function,local synthesis is applied to obtain the machine-tool settings of pinion.For digitized machine,four tooth surface generation styles of pinion are proposed.For every style,tooth contact analysis（TCA） is applied to obtain contact pattern and transmission error function.For the difference between TCA transmission error function and design objective curve,the degree of symmetry and agreement are defined and the corresponding sub-objective functions are established.Linear weighted combination method is applied to get an equivalent objective function to evaluate the shape of transmission error function.The computer programs for the process above are developed to analyze the meshing performances of the four pinion tooth surface generation styles for a pair of aviation spiral bevel gears with 38/43 teeth numbers.The four analytical results are compared with each other and show that the incomplete modified roll is optimal for this gear pair.This study is an expansion to generation strategy of spiral bevel gears,and offers new alternatives to computer numerical control（CNC） manufacture of spiral bevel gears.

AvoidPwd: A Mnemonic Password Generation Strategy Based on Keyboard Transformation

Identity authentication is the first line of defense for network security.Passwords have been the most widely used authentication method in recent years.Although there are security risks in passwords,they will be the primary method in the future due to their simplicity and low cost.Considering the security and usability of passwords,we propose AvoidPwd,which is a novel mnemonic password generation strategy that is based on keyboard transformation.AvoidPwd helps users customize a“route”to bypass an“obstacle”and choose the characters on the“route”as the final password.The“obstacle”is a certain word using any language and the keys adjacent to the“obstacle”are typed with the“Shift”key.A two-part experiment was conducted to examine the memorability and security of the AvoidPwd strategy with other three password strategies and three leaked password sets.The results showed that the passwords generated by the AvoidPwd strategy were more secure than the other leaked password sets.Meanwhile,AvoidPwd outperformed the KbCg,SpIns,and Alphapwd in balancing security and usability.In addition,there are more symbols in the character distribution of AvoidPwd than the other strategies.AvoidPwd is hopeful to solve the security problem that people are difficult to remember symbols and they tend to input letters and digits when creating passwords.

New Solution Generation Strategy to Improve Brain Storm Optimization Algorithm for Classification

As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.

Time-Domain Analysis of Body Freedom Flutter Based on 6DOF Equation

The reduced weight and improved efficiency of modern aeronautical structures result in a decreasing separation of frequency ranges of rigid and elastic modes.Particularly,a high-aspect-ratio flexible flying wing is prone to body freedomflutter(BFF),which is a result of coupling of the rigid body short-periodmodewith 1st wing bendingmode.Accurate prediction of the BFF characteristics is helpful to reflect the attitude changes of the vehicle intuitively and design the active flutter suppression control law.Instead of using the rigid body mode,this work simulates the rigid bodymotion of the model by using the six-degree-of-freedom(6DOF)equation.A dynamicmesh generation strategy particularly suitable for BFF simulation of free flying aircraft is developed.An accurate Computational Fluid Dynamics/Computational Structural Dynamics/six-degree-of-freedom equation(CFD/CSD/6DOF)-based BFF prediction method is proposed.Firstly,the time-domain CFD/CSD method is used to calculate the static equilibrium state of the model.Based on this state,the CFD/CSD/6DOF equation is solved in time domain to evaluate the structural response of themodel.Then combinedwith the variable stiffnessmethod,the critical flutter point of the model is obtained.This method is applied to the BFF calculation of a flyingwing model.The calculation results of the BFF characteristics of the model agree well with those fromthe modalmethod andNastran software.Finally,the method is used to analyze the influence factors of BFF.The analysis results show that the flutter speed can be improved by either releasing plunge constraint or moving the center ofmass forward or increasing the pitch inertia.

Critical Nodes Identification: A Non-Cooperative Method for Unknown Topology Information in Ad Hoc Networks

The foundation of ad hoc networks lies in the guarantee of continuous connectivity.However,critical nodes,whose failure can easily destroy network connectivity,will influence the ad hoc network connectivity significantly.To protect the network efficiently,critical nodes should be identified accurately and rapidly.Unlike existing critical node identification methods for unknown topology that identify critical nodes according to historical information,this paper develops a critical node identification method to relax the prior topology information condition about critical nodes.Specifically,we first deduce a theorem about the minimum communication range for a node through the number of nodes and deployment ranges,and prove the universality of the theorem in a realistic two-dimensional scenario.After that,we analyze the relationship between communication range and degree value for each node and prove that the greater number of nodes within the communication range of a node,the greater degree value of nodes with high probability.Moreover,we develop a novel strategy to improve the accuracy of critical node identification without topology information.Finally,simulation results indicate the proposed strategy can achieve high accuracy and low redundancy while ensuring low time consumption in the scenarios with unknown topology information in ad hoc networks.

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations.Researchers have used Light Detection and Ranging(LiDAR)data for extracting the building damage maps.For producing building damage maps from LiDAR data in a rapid manner,it is necessary to understand the effectiveness of features and classifiers.However,there is no comprehensive study on the performance of features and classifiers in identifying damaged areas.In this study,the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated.In the proposed method,at first,a pre-processing stage was utilized to apply essential processes on post-event LiDAR data.Second,textural features were extracted from the pre-processed LiDAR data.Third,fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents.The proposed method was tested across three areas over the 2010 Haiti earthquake.Three building damage maps with overall accuracies of 75.0%,78.1%and 61.4%were achieved.Based on outcomes,the fuzzy inference systems were stronger than random forest,bagging,boosting and support vector machine classifiers for detecting damaged buildings.