Identification and U-control of a state-space system with time-delay

This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.     

Applications of sketches in network traffic measurement: A survey

Accurate and timely network traffic measurement is essential for network status monitoring, network fault analysis, network intrusion detection, and network security management. With the rapid development of the network, massive network traffic brings severe challenges to network traffic measurement. However, existing measurement methods suffer from many limitations for effectively recording and accurately analyzing big-volume traffic. Recently, sketches, a family of probabilistic data structures that employ hashing technology for summarizing traffic data, have been widely used to solve these problems. However, current literature still lacks a thorough review on sketch-based traffic measurement methods to offer a comprehensive insight on how to apply sketches for fulfilling various traffic measurement tasks. In this paper, we provide a detailed and comprehensive review on the applications of sketches in network traffic measurement. To this end, we classify the network traffic measurement tasks into four categories based on the target of traffic measurement, namely cardinality estimation, flow size estimation, change anomaly detection, and persistent spreader identification. First, we briefly introduce these four types of traffic measurement tasks and discuss the advantages of applying sketches. Then, we propose a series of requirements with regard to the applications of sketches in network traffic measurement. After that, we perform a fine-grained classification for each sketch-based measurement category according to the technologies applied on sketches. During the review, we evaluate the performance, advantages and disadvantages of current sketch-based traffic measurement methods based on the proposed requirements. Through the thorough review, we gain a number of valuable implications that can guide us to choose and design proper traffic measurement methods based on sketches. We also review a number of general sketches that are highly expected in modern network systems to simultaneously perform multiple traffic measurement tasks and discuss their performance based on the proposed requirements. Finally, through our serious review, we summarize a number of open issues and identify several promising research directions.     

Re-evaluation of Radiation-Energy Transfer to an Extraction Solvent in a Minor-Actinide-Separation Process Based on Consideration of Radiation Permeability

ABSTRACT

Absorbed-dose estimation is essential for evaluation of the radiation tolerance of minor-actinide-separation processes. We propose a dose-evaluation method based on radiation permeability, with comparisons of heterogeneous structures seen in the solvent-extraction process, such as emulsions forming in the mixture of the organic and aqueous phases. A demonstration of radiation-energy-transfer simulation is performed with a focus on the minor-actinide-recovery process from high-level liquid waste with the aid of the Monte Carlo radiation-transport code PHITS. The simulation results indicate that the dose absorbed by the extraction solvent from alpha radiation depends upon the emulsion structure, and that from beta and gamma radiation depends upon the mixer-settler-apparatus size. Non-negligible contributions of well-permeable gamma rays were indicated in terms of the plant operation of the minor-actinide-separation process.     

Targeting S1PRs as a Therapeutic Strategy for Inflammatory Bone Loss Diseases—Beyond Regulating S1P Signaling

As G protein coupled receptors, sphingosine-1-phosphate receptors (S1PRs) have recently gained attention for their role in modulating inflammatory bone loss diseases. Notably, in murine studies inhibiting S1PR2 by its specific inhibitor, JTE013, alleviated osteoporosis induced by RANKL and attenuated periodontal alveolar bone loss induced by oral bacterial inflammation. Treatment with a multiple S1PRs modulator, FTY720, also suppressed ovariectomy-induced osteoporosis, collagen or adjuvant-induced arthritis, and apical periodontitis in mice. However, most previous studies and reviews have focused mainly on how S1PRs manipulate S1P signaling pathways, subsequently affecting various diseases. In this review, we summarize the underlying mechanisms associated with JTE013 and FTY720 in modulating inflammatory cytokine release, cell chemotaxis, and osteoclastogenesis, subsequently influencing inflammatory bone loss diseases. Studies from our group and from other labs indicate that S1PRs not only control S1P signaling, they also regulate signaling pathways induced by other stimuli, including bacteria, lipopolysaccharide (LPS), bile acid, receptor activator of nuclear factor κB ligand (RANKL), IL-6, and vitamin D. JTE013 and FTY720 alleviate inflammatory bone loss by decreasing the production of inflammatory cytokines and chemokines, reducing chemotaxis of inflammatory cells from blood circulation to bone and soft tissues, and suppressing RANKL-induced osteoclast formation.     

Theoretical estimation of dielectric loss of oxide glasses using nonequilibrium molecular dynamics simulations

To theoretically explore amorphous materials with a sufficiently low dielectric loss, which are essential for next-generation communication devices, the applicability of a nonequilibrium molecular dynamics simulation employing an external alternating electric field was examined using alkaline silicate glass models. In this method, the dielectric loss is directly evaluated as the phase shift of the dipole moment from the applied electric field. This method enabled us to evaluate the dielectric loss in a wide frequency range from 1 GHz to 10 THz. It was observed that the dielectric loss reaches its maximum at a few THz. The simulation method was found to qualitatively reproduce the effects of alkaline content and alkaline type on the dielectric loss. Furthermore, it reasonably reproduced the effect of mixed alkalines on the dielectric loss, which was observed in our experiments on sodium and/or potassium silicate glasses. Alkaline mixing was thus found to reduce the dielectric loss.     

Optimized microwave absorption properties by tailoring the morphology of carbon coated TiC nanoparticles by N2 pressure

Increasing the dielectric loss capacity plays an important role in enhancing the electromagnetic absorption performance of materials. It remains a challenge to simultaneously introduce multiple types of dielectric losses in the material. In this work, we show that the atomic and interfacial dipole polarizations can be simultaneously enhanced by substituting N species into both carbon coating layers and bulk TiC lattices of a core-shell TiC@C material. Additionally, substitution of N species results more exposed TiC(111) facets and refines the TiC grain sizes in the bulk material, which is beneficial for enhancing the scattering of the external electromagnetic waves. The maximum reflection loss of the N substituted TiC@C material is measured as ?47.1 dB with an effective absorbing bandwidth of 4.83 GHz at 1.9 mm, which illustrates a valuable way to further tuning the electromagnetic absorption performance of this type of materials.     

The influence of FeNi nanoparticles on the microstructures and soft magnetic properties of FeSi soft magnetic composites

In this work, a new type of FeSi/FeNi soft magnetic powder core (SMPC) was successfully fabricated by coating FeNi nanoparticles on the surface of FeSi micrometer powder. The effects of different contents of FeNi nanoparticles on the micromorphology, internal structures, and soft magnetic properties of SMPCs were studied. The results show that FeNi nanoparticles adhere to the surface of FeSi powder, which can effectively fill the air gap between FeSi powder and is beneficial to the compaction of the powder cores during the pressing process. Thus, the density of the SMPCs is increased. Compared to FeSi SMPCs, the comprehensive soft magnetic properties of FeSi/FeNi SMPCs have been greatly improved. When adding 15 wt% FeNi nanoparticles, the SMPCs exhibit excellent magnetic properties with high effective permeability (increased by 43.8 %) and low core loss (decreased by 22.1 %). The high performance FeSi/FeNi SMPCs prepared in this work are expected to be widely used in power choke coils, uninterruptible power supplies, and boosts and inverter inductors.     

Effect of heat loss on the syngas production by fuel-rich combustion in a divergent two-layer burner

The effect of heat loss on the syngas production from partial combustion of fuel-rich in a divergent two-layer burner is numerically studied using two-dimensional model with detailed kinetics GRI-Mech 1.2. Both the radiation and wall heat losses to the surrounding are considered in the computations. It is shown that two types heat losses have different effects on the syngas production. The radiation heat loss has significant effect on the syngas temperature and the syngas temperature is dropped as radiation heat loss is increased, but it has neglected effect on the reforming efficiency and methane conversion efficiency. The wall heat loss has a comprehensive effect on the syngas production. The wall heat loss not only reduces the conversion efficiency, but also significantly decreases the syngas temperature. The effect of wall heat loss becomes weak as the equivalence is increased. The reforming efficiency drops from 0.440 to 0.424 for equivalence ratio of 2 and mixture velocity of 0.17 m/s for the predictions between adiabatic wall and non-adiabatic conditions.     

Maximum likelihood-based adaptive differential evolution identification algorithm for multivariable systems in the state-space form

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.