Synthesis and Tribological Properties of Copper-alumina Nanocomposites Prepared by Coprecipitation Technique

The objective of this work is to study the synthesis of copper-alumina nanocomposites using the coprecipitation process and hot-pressing method, and investigate their mechanical properties. The effects of calcination temperature on the average size of composite particles and chemical composition after calcination were also analyzed. The sintering parameters including sintering temperature, hot pressure and packing time were optimized to fabricate the alumina nanoparticles reinforced copper matrix composites（CMCs）. The density, microhardness and tribological properties of the CMCs reinforced with 1 wt%, 2 wt%, 3 wt%, 4 wt% and 5 wt% of alumina nanoparticles were investigated correspondingly. The results showed that the optimum preparation parameters for the CMCs were 900 ℃ of hot pressing temperature, 27.5 MPa of hot pressure and 2 hrs of packing time. The CMC reinforced with 2 wt% of alumina nanoparticles had the lowest wear rate, with the relative wear resistance of 3.13.

Nonlinear instability suppression of closed-loop pilot-vehicle system with rate-limiting actuator based on anti-windup compensation

To solve the flight safety problem caused by nonlinear instabilities（category II pilot induced oscillations, PIOs） of the closed-loop pilot-vehicle system with rate-limiting actuator, the antiwindup（AW） compensation method to avoid category II PIOs is investigated. Firstly, the AW compensation method originally used for controlling input magnitude limited system is introduced, then this method is extended for controlling input rate-limiting system through a circle criterion theorem. Secondly, the establishment of the AW compensator is transformed into the solving of linear matrix inequalities. Finally, an AW compensator establishment algorithm for the closed-loop pilot-vehicle system with the rate-limiting actuator is obtained. The effectiveness of the AW compensation method to avoid category II PIOs is validated by time-domain simulations,and compared with rate-limited feedback（RLF） command rate compensation method. The results show that the AW compensation method can effectively suppress category II PIOs and maintain the nominal performance when the closed-loop pilot-vehicle system is normal. Unlike the command rate compensator which works upon system uninterruptedly, the AW compensation method affects the closed-loop pilot-vehicle system only when the rate-limiting of actuator is activated, so it is a novel PIO avoidance method.

Optimal Dependence of Performance and Efficiency of Collaborative Filtering on Random Stratified Subsampling

Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering(CF)algorithms.The effect of this subsampling on the computing time and accuracy of CF is not fully understood,and clear guidelines for selecting optimal or even appropriate subsampling levels are not available.In this paper,we present a Density-based Random Stratified Subsampling using Clustering(DRSC)algorithm in which the desired Fraction of Users Dropped(FUD)and Fraction of Items Dropped(FID)are specified,and the overall density during subsampling is maintained.Subsequently,we develop simple models of the Training Time Improvement(TTI)and the Accuracy Loss(AL)as functions of FUD and FID,based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens,Yahoo Music Rating,and Amazon Automotive data.Simulations show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven methods.The TTI linear regression of a CF method appears to be same for all datasets.Extensive simulations illustrate that TTI can be estimated reliably with FUD and FID only,but AL requires considering additional dataset characteristics.The derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL.A simple sub-optimal approximation was found,in which the optimal AL is proportional to the optimal Training Time Reduction Factor(TTRF)for higher values of TTRF,and the optimal subsampling levels,like optimal FID/(1-FID),are proportional to the square root of TTRF.

Closed-Form Models of Accuracy Loss due to Subsampling in SVD Collaborative Filtering

We postulate and analyze a nonlinear subsampling accuracy loss(SSAL)model based on the root mean square error(RMSE)and two SSAL models based on the mean square error(MSE),suggested by extensive preliminary simulations.The SSAL models predict accuracy loss in terms of subsampling parameters like the fraction of users dropped(FUD)and the fraction of items dropped(FID).We seek to investigate whether the models depend on the characteristics of the dataset in a constant way across datasets when using the SVD collaborative filtering(CF)algorithm.The dataset characteristics considered include various densities of the rating matrix and the numbers of users and items.Extensive simulations and rigorous regression analysis led to empirical symmetrical SSAL models in terms of FID and FUD whose coefficients depend only on the data characteristics.The SSAL models came out to be multi-linear in terms of odds ratios of dropping a user(or an item)vs.not dropping it.Moreover,one MSE deterioration model turned out to be linear in the FID and FUD odds where their interaction term has a zero coefficient.Most importantly,the models are constant in the sense that they are written in closed-form using the considered data characteristics(densities and numbers of users and items).The models are validated through extensive simulations based on 850 synthetically generated primary(pre-subsampling)matrices derived from the 25M MovieLens dataset.Nearly 460000 subsampled rating matrices were then simulated and subjected to the singular value decomposition(SVD)CF algorithm.Further validation was conducted using the 1M MovieLens and the Yahoo!Music Rating datasets.The models were constant and significant across all 3 datasets.

Time-dependent reliability analysis for repairable consecutive-k-out-of-n:F system

In a repairable consecutive C(k,n:F)system,after the system operates for a certain time,some components may fail,some failed components may be repaired and the state of the system may change.The models developed in the existing literature usually assume that the state of the sys-tem varies over time depending on the values of n and k and the state of the system is known.Since the system reliability will vary over time,it is of great interest to analyse the time-dependent system reliability.In this paper,we develop a novel and simple method that utilizes the eigen-values of the transition rate matrix of the system for the computation of time-dependent system reliability when the system state is known.In addition,the transition performance probabilities of the system from a known state to the possible states are also analysed.Computational results are presented to illustrate the applicability and accuracy of the proposed method.

Charting the ‘composition–strength’ space for novel austenitic,martensitic and ferritic creep resistant steels

We report results of a large computational ＇alloy by design＇ study, in which the ＇chemical composition-mechanical strength＇ space is explored for austenitic, ferritic and martensitic creep resistant steels. The approach used allows simultaneously optimization of alloy composition and processing parameters based on the integration of thermodynamic, thermo-kinetics and a genetic algorithm optimization route. The nature of the optimisation depends on both the intended matrix（ferritic, martensitic or austenitic） and the desired precipitation family. The models are validated by analysing reported strengths of existing steels. All newly designed alloys are predicted to outperform existing high end reference grades.