An Optimal NARX Neural Network Identification Model for a Magnetorheological Damper With Force-Distortion Behavior

This paper presents an optimal NARX neural network identification model for a magnetorheological (MR) damper with the force-distortion behavior. An intensive experimental study is conducted for designing the NARX network architecture to enhance modeling accuracy and availability, and the activation function selection, weights, and biases of the selected network are optimized by differential evolution algorithm. Different experimental training and validation samples are used for network training. The prediction capability of the optimal NARX model is verified by new measured test data. The test and comparative results show that the optimal NARX network model can satisfactorily emulate the dynamic behavior of MR damper and effectively capture its distortion behavior occurred with the increased current. The developed inverse NARX network model can effectively estimate the required current and track desired damping force. Moreover, the effects of different noise disturbance on the NARX network model performance are analyzed, and the model error varies slightly with a small noise disturbance. The accuracy of the results supports the use of this modeling technique for identifying irregular non-linear models of MR damper and similar devices

A Two-stage Multiband Radar Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference

Multiband fusion is an important technique for radar sensing, which jointly utilizes measurements from multiple non-contiguous frequency bands to improve the sensing performance. In the multi-band radar sensing signal model, there are many local optimums in the associated likelihood function due to the existence of high frequency component, which makes it difficult to obtain high-accuracy parameter estimation. To cope with this challenge, we divide the radar target parameter estimation into two stages equipped with different but equivalent signal models, where the first-stage coarse estimation is used to narrow down the search range for the next stage, and the second-stage refined estimation is based on the Bayesian approach to avoid the convergence to a bad local optimum of the likelihood function. Specifically, in the coarse estimation stage, we employ a weighted root MUSIC algorithm to achieve initial estimation. Then, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm for the Bayesian estimation of the radar target parameters in the refined stage. Unlike the conventional particle-based VBI (PVBI) in which only the probability of each particle is optimized and the per-iteration computational complexity increases exponentially with the number of particles, the proposed SPVBI optimizes both the position and probability of each particle, and it adopts the block SSCA to significantly improve the sampling efficiency by averaging over iterations. As such, it is shown that the proposed SPVBI can achieve a better performance than the conventional PVBI with a much smaller number of particles and per-iteration complexity. Finally, extensive simulations verify the advantage of the proposed algorithm over various baseline algorithms

Thermal transport properties of IrSbSe

We report a thermal transport study of IrSbSe, which crystallizes in a noncentrosymmetric cubic structure with the $P2_13$ space group and shows a narrow-gap semiconducting behavior. The large discrepancy between the activation energy for conductivity [$E_\rho$ = 128(2) meV] and for thermopower [$E_S$ = 17.7(9) meV] from 200 to 300 K indicates the polaronic transport mechanism. Electrical resistivity varies as $exp(T_0/T)^{1/4}$ and thermopower varies as $T^{1/2}$ at low temperatures, indicating that it evolves into the Mott's variable-range hopping dominant conduction with decreasing temperature. IrSbSe shows relatively low value of thermal conductivity ($\sim$ 1.65 W/K$\cdot$m) and thermopower of about 0.24 mV/K around 100 K, yet poor electrical conductivity. On the other hand, high vacancy defect concentration on both Ir and Sb atomic sites of up to 15\%, suggests high defect tolerance and points to possibility of future improvement of carrier density by chemical substitution or defect optimization

Artificial Coal: Facile and Green Production Method via Low-Temperature Hydrothermal Carbonization of Lignocellulose

A new concept is proposed for the production of artificial coal under HTC conditions using Mg(NO3)2 as an oxidant in a short time, which is found to enhance the coalification degree of hydrochar from lignocellulosic materials. Pressure promotes decarboxylation reactions of lignocellulose to form hollow smooth-faced regular spherical particles, avoiding the agglomeration of hydrochar particles. In parallel, oxidation can break down the biopolymer structure to form low-molecular-weight compounds, which is found to be a key step during artificial coal formation. The artificial coal synthesized has a high degree of coalification

A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS) operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011

Deca : a garbage collection optimizer for in-memory data processing

In-memory caching of intermediate data and active combining of data in shuffle buffers have been shown to be very effective in minimizing the recomputation and I/O cost in big data processing systems such as Spark and Flink. However, it has also been widely reported that these techniques would create a large amount of long-living data objects in the heap. These generated objects may quickly saturate the garbage collector, especially when handling a large dataset, and hence, limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which, by automatically analyzing the user-defined functions and data types, obtains the expected lifetime of the data objects and then allocates and releases memory space accordingly to minimize the garbage collection overhead. In particular, we present Deca,1 a concrete implementation of our proposal on top of Spark, which transparently decomposes and groups objects with similar lifetimes into byte arrays and releases their space altogether when their lifetimes come to an end. When systems are processing very large data, Deca also provides field-oriented memory pages to ensure high compression efficiency. Extensive experimental studies using both synthetic and real datasets show that, in comparing to Spark, Deca is able to (1) reduce the garbage collection time by up to 99.9%, (2) reduce the memory consumption by up to 46.6% and the storage space by 23.4%, (3) achieve 1.2× to 22.7× speedup in terms of execution time in cases without data spilling and 16× to 41.6× speedup in cases with data spilling, and (4) provide similar performance compared to domain-specific systems