Accelerated Stochastic ADMM with Variance Reduction

Alternating Direction Method of Multipliers (ADMM) is a popular method in solving Machine Learning problems. Stochastic ADMM was firstly proposed in order to reduce the per iteration computational complexity, which is more suitable for big data problems. Recently, variance reduction techniques have been integrated with stochastic ADMM in order to get a fast convergence rate, such as SAG-ADMM and SVRG-ADMM,but the convergence is still suboptimal w.r.t the smoothness constant. In this paper, we propose a new accelerated stochastic ADMM algorithm with variance reduction, which enjoys a faster convergence than all the other stochastic ADMM algorithms. We theoretically analyze its convergence rate and show its dependence on the smoothness constant is optimal. We also empirically validate its effectiveness and show its priority over other stochastic ADMM algorithms

Construction of a Fish-like Robot Based on High Performance Graphene/PVDF Bimorph Actuation Materials.

Smart actuators have many potential applications in various areas, so the development of novel actuation materials, with facile fabricating methods and excellent performances, are still urgent needs. In this work, a novel electromechanical bimorph actuator constituted by a graphene layer and a PVDF layer, is fabricated through a simple yet versatile solution approach. The bimorph actuator can deflect toward the graphene side under electrical stimulus, due to the differences in coefficient of thermal expansion between the two layers and the converse piezoelectric effect and electrostrictive property of the PVDF layer. Under low voltage stimulus, the actuator (length: 20 mm, width: 3 mm) can generate large actuation motion with a maximum deflection of about 14.0 mm within 0.262 s and produce high actuation stress (more than 312.7 MPa/g). The bimorph actuator also can display reversible swing behavior with long cycle life under high frequencies. on this basis, a fish-like robot that can swim at the speed of 5.02 mm/s is designed and demonstrated. The designed graphene-PVDF bimorph actuator exhibits the overall novel performance compared with many other electromechanical avtuators, and may contribute to the practical actuation applications of graphene-based materials at a macro scale

CoCoFormer: A controllable feature-rich polyphonic music generation method

This paper explores the modeling method of polyphonic music sequence. Due to the great potential of Transformer models in music generation, controllable music generation is receiving more attention. In the task of polyphonic music, current controllable generation research focuses on controlling the generation of chords, but lacks precise adjustment for the controllable generation of choral music textures. This paper proposed Condition Choir Transformer (CoCoFormer) which controls the output of the model by controlling the chord and rhythm inputs at a fine-grained level. In this paper, the self-supervised method improves the loss function and performs joint training through conditional control input and unconditional input training. In order to alleviate the lack of diversity on generated samples caused by the teacher forcing training, this paper added an adversarial training method. CoCoFormer enhances model performance with explicit and implicit inputs to chords and rhythms. In this paper, the experiments proves that CoCoFormer has reached the current better level than current models. On the premise of specifying the polyphonic music texture, the same melody can also be generated in a variety of ways

Adaptive sequential nonlinear LSE for structural damage tracking with incomplete measurements

Parameter identification is very important in structural health monitoring for structural safety and management after emergency event. In practical applications, some external inputs, such as seismic excitations, wind loads, etc. and some structure responses may not be measured or may not be measurable. Herein detail analysis based on adaptive sequential nonlinear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) method is developed for effective parameter identification. Simulation studies using a 3-DOF linear system and experimental studies using a 3-DOF shear-beam model with different scenarios of unknown inputs and unknown outputs are carried out to verify the analytical results. Simulation and experimental results demonstrate that this analysis technique for effective parameter identification of ASNLSE-UI-UO method is reasonable and accurate

System identification of rubber-bearing isolators based on experimental tests

Rubber-bearing isolation systems have been used in buildings and bridges. These base isolation systems will become more popular in the future due to their ability to reduce significantly the structural responses induced by earthquakes and other dynamic loads. To ensure the integrity and safety of these base isolation systems, a structural health monitoring system is needed. One important problem in the structural health monitoring is the identification of the system and the detection of damages. This problem is more challenging for the rubberbearing isolation systems because of their nonlinear behavior. In this paper, experimental studies have been conducted for the system identification of nonlinear hysteretic rubberbearings. Experimental tests of a rubber-bearing isolator under El Centro and Kobe earthquakes have been performed. The Bouc-Wen models with 3, 5 and 6 unknown parameters, respectively, have been investigated to represent the hysteretic behavior of rubber-bearing isolators. The extended Kalman filter (EKF) approach has been used to identify the nonlinear parameters of the Bouc-Wen models for the rubber-bearing isolators. Our experimental studies demonstrate that the Bouc-Wen models are capable of describing the nonlinear behavior of rubber-bearing isolators, and that the EKF approach is effective in identifying nonlinear hysteretic parameters

Parameter identification of aircraft thin-walled structures using incomplete measurements

Early parametric identification is critical for the decision making of repair or replacement in order to guarantee structural safety. Nowadays, aircraft thin-walled structures are widely applied in aero-/astronautics areas and their health conditions receive considerable attention. Parameter identification in aircraft thin-walled structures is more challenging because of the structural complexity. In this research, a new time-domain analysis method, the sequential nonlinear least square estimation (SNLSE) method, along with model reduction technique is proposed to identify the parameters of aircraft thin-walled structures using vibration data, which is referred to as the reduced order model based SNLSE approach. Herein, model reduction technique is used to reduce the number of degrees of freedom for conducive to the placement of sensors and high-efficiency calculation by SNLSE method. Simulation and experimental studies have been conducted for the parameter identification of the aluminum thin-walled structure. As demonstrated by simulation and experimental results, the proposed approach using incomplete measurements is very effective in parameter identification of aircraft thin-walled structures

In situ TEM Characterization of Microstructure Evolution and Mechanical Behavior of the 3D-Printed Inconel 718 Exposed to High Temperature

This in situ transmission electron microscopy work presents a nanoscale characterization of the microstructural evolution in 3D-printed Inconel 718 (IN718) while exposed to elevated temperature and an associated change in the mechanical property under tensile loading. Here, we utilized a specially designed specimen shape that enables tensile testing of nano-sized thin films without off-plane deformations. Additionally, it allows a seamless transition from the in situ heating to tensile experiment using the same specimen, which enables a direct correlation of the microstructure and the mechanical property of the sample. The method was successfully used to observe the residual stress relaxation and the formation of incoherent γ′ precipitates when temperature was increased to 700°C. The subsequent in situ tensile test revealed that the exposure of the as-printed IN718 to a high temperature without full heat treatment (solutionizing and double aging) leads to loss of ductility

Characteristics of Pollen from Transgenic Lines of Apple Carrying the Exogenous CpTI Gene

AbstractIt is fundamental for gene transformation and ecosystem hazard evaluation to study the pollen characteristics of transgenic plants. In this research, the characteristics of pollen from 7- or 8-year-old transgenic apple plants carrying an exogenous CpTI gene were analyzed. The results showed that there was no significant difference in terms of size, morphology, or exine ornamentation between the pollen of the transgenic plants and the non-transgenic control. However, the transgenic plants had more abnormal pollen grains. Of the 13 transgenic lines tested, 12 had a significantly lower amount of pollen and six exhibited a significantly lower germination rate when cultured in vitro. The pollen viability of three transgenic lines was determined, with two showing significantly lower viability than the control. The transgenic Gala apple pollen grains germinated normally via controlled pollination on Fuji apple stigmas. However, the pollen tubes extended relatively slowly during the middle and late development stages, and another 8h were needed to reach the ovules compared with the control. The gibberellic acid concentration in transgenic Gala apple flowers was lower than in the non-transgenic control during all development stages tested. The abscisic acid concentration in the transgenic flowers was lower during the pink stage, and higher during the ball and fully open stages. Microscopic observation of the anther structure showed no difference. The tapetum of the pollen sac wall in transgenic plants decomposed late and affected pollen grain development, which could be one of the reasons for the lower number of pollen grains and poor viability in the transgenic plants