Medium- and high-entropy alloys based on the CrCoNi-system have been shown to display outstanding strength, tensile ductility and fracture toughness (damage-tolerance properties), especially at ...cryogenic temperatures. Here we examine the JIc and (back-calculated) KJIc fracture toughness values of the face-centered cubic, equiatomic CrCoNi and CrMnFeCoNi alloys at 20 K. At flow stress values of ~1.5 GPa, crack-initiation KJIc toughnesses were found to be exceptionally high, respectively 235 and 415 MPa(square-root)m for CrMnFeCoNi and CrCoNi, with the latter displaying a crack-growth toughness Kss exceeding 540 MPa(square-root)m after 2.25 mm of stable cracking, which to our knowledge is the highest such value ever reported. Characterization of the crack-tip regions in CrCoNi by scanning electron and transmission electron microscopy reveal deformation structures at 20 K that are quite distinct from those at higher temperatures and involve heterogeneous nucleation, but restricted growth, of stacking faults and fine nano-twins, together with transformation to the hexagonal closed-packed phase. The coherent interfaces of these features can promote both the arrest and transmission of dislocations to generate respectively strength and ductility which strongly contributes to sustained strain hardening. Indeed, we believe that these nominally single-phase, concentrated solid-solution alloys develop their fracture resistance through a progressive synergy of deformation mechanisms, including dislocation glide, stacking-fault formation, nano-twinning and eventually in situ phase transformation, all of which serve to extend continuous strain hardening which simultaneously elevates strength and ductility (by delaying plastic instability), leading to truly exceptional resistance to fracture.
PID control is a kind of control method based on the error of the system, using the proportion, integral and differential to calculate the control quantity and adjust the system error. The PID ...controller is widely used in various fields of industrial control because it does not need to establish the accurate mathematical model of the system. However, the three parameter values of classical PID control methods are usually artificially assigned, and artificial assignment often depends on experience, so the control efficiency is relatively low. In this paper, an accelerating BP neural network based on momentum constant is used to achieve the self-adjustment of PID controller parameters, and the method is applied to the control system. The simulation experiment shows that using the method of accelerating BP neural network proposed in this paper to adjust the parameters of PID controller has faster convergence ability and can realize the fast approximation function of the system. The method of using intelligent algorithm to adjust the parameters of PID control is widely used in various fields of industrial control. In this paper the commonly used PID controller parameter tuning methods are compared.
Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues. Methods in the ...Entire Space Multi-task Model (ESMM) family leverage the sequential pattern of user actions, i.e. \(impression\rightarrow click \rightarrow conversion\) to address data sparsity issue. However, they still fail to ensure the unbiasedness of CVR estimates. In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion. To this end, we devise a principled approach named Entire Space Counterfactual Multi-task Modelling (ESCM\(^2\)), which employs a counterfactual risk miminizer as a regularizer in ESMM to address both IEB and PIP issues simultaneously. Extensive experiments on offline datasets and online environments demonstrate that our proposed ESCM\(^2\) can largely mitigate the inherent IEB and PIP issues and achieve better performance than baseline models.
The standard 2-norm support vector machine (SVM for short) is known for its good performance in classification and regression problems. In this paper, the 1-norm support vector machine is considered ...and a novel smoothing function method for Support Vector Classification(SVC) and Regression (SVR) are proposed in an attempt to overcome some drawbacks of the former methods which are complex, subtle, and sometimes difficult to implement. First, using Karush-Kuhn-Tucker complementary condition in optimization theory, unconstrained non-differentiable optimization model is built. Then the smooth approximation algorithm basing on differentiable function is given. Finally, the paper trains the data sets with standard unconstraint optimization method. This algorithm is fast and insensitive to the initial point. Theory analysis and numerical results illustrate that the smoothing function method for SVMs are feasible and effective.
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