Diffusion-based clock synchronization for molecular communication under inverse Gaussian distribution

Nanonetworks are expected to expand the capabilities of individual nanomachines by allowing them to cooperate and share information by molecular communication. The information molecules are released by the transmitter nanomachine and diffuse across the aqueous channel as a Brownian motion holding the feature of a strong random movement with a large propagation delay. In order to ensure an effective real-time cooperation, it is necessary to keep the clock synchronized among the nanomachines in the nanonetwork. This paper proposes a model on a two-way message exchange mechanism with the molecular propagation delay based on the inverse Gaussian distribution. The clock offset and clock skew are estimated by the maximum likelihood estimation (MLE). Simulation results by MATLAB show that the mean square errors (MSE) of the estimated clock offsets and clock skews can be reduced and converge with a number of rounds of message exchanges. The comparison of the proposed scheme with a clock synchronization method based on symmetrical propagation delay demonstrates that our proposed scheme can achieve a better performance in terms of accuracy

A New Method of SHM for Steel Wire Rope and its Apparatus

Steel wire ropes often operate in a high‐speed swing status in practical engineering, and the reliable structural health monitoring (SHM) for them directly relates to human lives; however, they are usually beyond the capability of present portable magnet magnetic flux leakage (MFL) sensors based on yoke magnetic method due to its strong magnetic force and large weight. Unlike the yoke method, a new method of SHM for steel wire rope is proposed by theoretical analyses and also verified by finite element method (FEM) and experiments, which features much weaker magnetic interaction force and similar magnetization capability compared to the traditional yoke method. Meanwhile, the relevant detection apparatus or sensor is designed by simulation optimization. Furthermore, experimental comparisons between the new and yoke sensors for steel wire rope inspection are also conducted, which successfully confirm the characterization of smaller magnetic interaction force, less wear, and damage in contrast with traditional technologies. Finally, methods for SHM of steel wire rope and apparatus are discussed, which demonstrate the good practicability for SHM of steel wire rope under poor working conditions

Excited State Calculations In Solids By Auxiliary-Field Quantum Monte Carlo

We present an approach for ab initio many-body calculations of excited states in solids. Using auxiliary-field quantum Monte Carlo, we introduce an orthogonalization constraint with virtual orbitals to prevent collapse of the stochastic Slater determinants in the imaginary-time propagation. Trial wave functions from density-functional calculations are used for the constraints. Detailed band structures can be calculated. Results for standard semiconductors are in good agreement with experiments; comparisons are also made with GW calculations and the connections and differences are discussed. For the challenging ZnO wurtzite structure, we obtain a fundamental band gap of 3.26(16) eV, consistent with experiments

Auxiliary-field quantum Monte Carlo calculations with multiple-projector pseudopotentials

We have implemented recently developed multiple-projector pseudopotentials into the plane-wave-based auxiliary-field quantum Monte Carlo (pw-AFQMC) method. Multiple-projector pseudopotentials can yield smaller plane-wave cutoffs while maintaining or improving transferability. This reduces the computational cost of pw-AFQMC, increasing its reach to larger and more complicated systems. We discuss the use of nonlocal pseudopotentials in the separable Kleinman-Bylander form, and the implementation in pw-AFQMC of the multiple-projector optimized norm-conserving pseudopotential ONCVPSP of Hamann. The accuracy of the method is first demonstrated by equation-of-state calculations of the ionic insulator NaCl and more strongly correlated metal Cu. The method is then applied to calibrate the accuracy of density-functional theory (DFT) predictions of the phase stability of recently discovered high temperature and pressure superconducting sulfur hydride systems. We find that DFT results are in good agreement with pw-AFQMC, due to the near cancellation of electron-electron correlation effects between different structures

Quantum Monte Carlo Calculations in Solids with Downfolded Hamiltonians

We present a combination of a downfolding many-body approach with auxiliary-field quantum Monte Carlo (AFQMC) calculations for extended systems. Many-body calculations operate on a simpler Hamiltonian which retains material-specific properties. The Hamiltonian is systematically improvable and allows one to dial, in principle, between the simplest model and the original Hamiltonian. As a by-product, pseudopotential errors are essentially eliminated using frozen orbitals constructed adaptively from the solid environment. The computational cost of the many-body calculation is dramatically reduced without sacrificing accuracy. Excellent accuracy is achieved for a range of solids, including semiconductors, ionic insulators, and metals. We apply the method to calculate the equation of state of cubic BN under ultrahigh pressure, and determine the spin gap in NiO, a challenging prototypical material with strong electron correlation effects

Probability weighted four-point arc imaging algorithm for time-reversed lamb wave damage detection

Damage imaging based on scattering signals of ultrasonic Lamb waves in plate structure is considered as one of the most effective ways for structural health monitoring area. To improve location accuracy and reduce the impact of artifacts, a probability weighted four-point arc imaging algorithm for time reversal Lamb wave damage detection is proposed in this paper. By taking the defect as a secondary wave source, the four-point arc positioning method is used to calculate the propagation time of the signal from transducer to defect. And the amplitude of damage signal corresponding to the time of flight is used for imaging. In order to eliminate the artifacts, a damage probability weighting is combined with four-point circular arc imaging algorithm. The effectiveness of the proposed method is experimentally verified in aluminum plate. Experimental results indicate that damage location accuracy and imaging quality has been improved in both single-flaw and double-flaw samples compared with conventional delay-and-sum method

A modified damage index probability imaging algorithm based on delay-and-sum imaging for synthesizing time-reversed Lamb waves

Imaging for damage in plate structure by Lamb waves is one of the most effective methods in the field of structural health monitoring. In order to improve the accuracy of damage localization, a novel method is proposed to modify damage exponent probability imaging algorithm based on delay-and-sum imaging by using time reversal Lamb waves. A new probability distribution function is introduced to improve the damage index probability method and is combined with delay-and-sum method for damage localization. Experimental results on aluminum plate show that the hybrid algorithm achieves better accuracy of damage location and imaging quality than the conventional delay-and-sum method

CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement

Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data. This avoids heavyweight training (from scratch) using conventional methods and saves storage space by reducing the number of old data to store. But it also leads to poor performance in fairness. In this paper, we show that CIL suffers both dataset and algorithm bias problems, and existing solutions can only partially solve the problem. We propose a novel framework, CILIATE, that fixes both dataset and algorithm bias in CIL. It features a novel differential analysis guided dataset and training refinement process that identifies unique and important samples overlooked by existing CIL and enforces the model to learn from them. Through this process, CILIATE improves the fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three popular datasets and widely used ResNet models