Multiple Localized Modes Research Articles

Simultaneously enhanced magnomechanical cooling and entanglement assisted by an auxiliary microwave cavity.

We propose a mechanism to simultaneously enhance quantum cooling and entanglement via coupling an auxiliary microwave cavity to a magnomechanical cavity. The auxiliary cavity acts as a dissipative cold reservoir that can efficiently cool multiple localized modes in the primary system via beam-splitter interactions, which enables us to obtain strong quantum cooling and entanglement. We analyze the stability of the system and determine the optimal parameter regime for cooling and entanglement under the auxiliary-microwave-cavity-assisted (AMCA) scheme. The maximum cooling enhancement rate of the magnon mode can reach 98.53%, which clearly reveals that the magnomechanical cooling is significantly improved in the presence of the AMCA. More importantly, the dual-mode entanglement of the system can also be significantly enhanced by AMCA in the full parameter region, where the initial magnon-phonon entanglement can be maximally enhanced by a factor of about 11. Another important result of the AMCA is that it also increases the robustness of the entanglement against temperature. Our approach provides a promising platform for the experimental realization of entanglement and quantum information processing based on cavity magnomechanics.

Bound states in Bose-Einstein condensates with radially-periodic spin-orbit coupling

We consider Bose-Einstein condensate (BEC) subject to the action of spin-orbit-coupling (SOC) periodically modulated in the radial direction. In contrast to the commonly known principle that periodic potentials do not create bound states, the binary BEC maintains multiple localized modes in the linear limit, with their chemical potential falling into spectral gaps of the (numerically found) radial band structure induced by the spatial modulation of the SOC. In the presence of the repulsive nonlinearity, the SOC modulation supports fundamental gap solitons of the semi-vortex types, as well as higher-order vortex gap solitons. The localization degree and stability of the gap solitons strongly depend on the location of their chemical potential in the gap. Stability intervals for vortex gap solitons in a broad range of the intrinsic vorticity, from −2 to 3, are identified. Thus, the analysis reveals the previously unexplored mechanism of linear and nonlinear localization provided by the spatially periodic modulation of SOC, which may be extended to other settings, such as those for optical beams and polaritons. Unlike the commonly known quartets of eigenvalues for small perturbations, in the present system the instability is accounted for by shifted complex eigenvalue pairs.

Post-hazard condition assessment of nuclear piping-equipment systems: Novel approach to feature extraction and deep learning

Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. In this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.

Universal method to describe multiple localized modes in photonic crystal heterojunction

A general method is proposed to describe the energy levels of the interface states in one-dimensional photonic crystal (PC) heterojunction [Formula: see text] containing dispersive or non-dispersion materials. We found that the finite energy levels of the interface states for the finite configuration can be described totally by the dispersion relation of the PC with a periodic unit [Formula: see text]. It is further found that this method is also applicable for the case of defect modes. We believe our method can be used to guide the practical application.

Multiple-band absorber enabled by new type of metamaterial resonator formed by metallic split ring embedded with rectangle patch

Multiple-band perfect light absorber using new type of metamaterial resonator consisted of a metallic split ring embedded with a rectangle patch is designed in this paper. Four terahertz absorption peaks with narrow line-widths, large absorption coefficients, and discrete resonance frequencies are realized. The combined effect of multiple localized modes of the designed metamaterial resonator results in the quad-band near-unity absorption. Further results reveal that parameter changes of the metamaterial resonator provide the ability to tune the number of absorption peaks. Both the device design and its physical mechanism are different from the traditional design strategies of the multiple-band absorption devices, and thus the suggested scheme can offer an alternative design idea for multiple-band light absorbers and could have large application prospects, including the selective thermal radiation, thermal detection, imaging, spectrum enhancement, stealth technology, solar energy, sensing, etc.

Bayesian estimation of ordinary differential equation models when the likelihood has multiple local modes

Abstract Ordinary differential equations (ODEs) are popularly used to model complex dynamic systems by scientists; however, the parameters in ODE models are often unknown and have to be inferred from noisy measurements of the dynamic system. One conventional method is to maximize the likelihood function, but the likelihood function often has many local modes due to the complexity of ODEs, which makes the optimizing algorithm be vulnerable to trap in local modes. In this paper, we solve the global optimization issue of ODE parameters with the help of the Stochastic Approximation Monte Carlo (SAMC) algorithm which is shown to be self-adjusted and escape efficiently from the “local-trapping” problem. Our simulation studies indicate that the SAMC method is a powerful tool to estimate ODE parameters globally. The efficiency of SAMC method is demonstrated by estimating a predator-prey ODEs model from real experimental data.

Statistical Analysis of Unlabeled Point Sets: Comparing Molecules in Chemoinformatics

We consider Bayesian methodology for comparing two or more unlabeled point sets. Application of the technique to a set of steroid molecules illustrates its potential utility involving the comparison of molecules in chemoinformatics and bioinformatics. We initially match a pair of molecules, where one molecule is regarded as random and the other fixed. A type of mixture model is proposed for the point set coordinates, and the parameters of the distribution are a labeling matrix (indicating which pairs of points match) and a concentration parameter. An important property of the likelihood is that it is invariant under rotations and translations of the data. Bayesian inference for the parameters is carried out using Markov chain Monte Carlo simulation, and it is demonstrated that the procedure works well on the steroid data. The posterior distribution is difficult to simulate from, due to multiple local modes, and we also use additional data (partial charges on atoms) to help with this task. An approximation is considered for speeding up the simulation algorithm, and the approximating fast algorithm leads to essentially identical inference to that under the exact method for our data. Extensions to multiple molecule alignment are also introduced, and an algorithm is described which also works well on the steroid data set. After all the steroid molecules have been matched, exploratory data analysis is carried out to examine which molecules are similar. Also, further Bayesian inference for the multiple alignment problem is considered.

Packet-Dispersion Techniques and a Capacity-Estimation Methodology

The packet-pair technique aims to estimate the capacity of a path (bottleneck bandwidth) from the dispersion of two equal-sized probing packets sent back to back. It has been also argued that the dispersion of longer packet bursts (packet trains) can estimate the available bandwidth of a path. This paper examines such packet-pair and packet-train dispersion techniques in depth. We first demonstrate that, in general, packet-pair bandwidth measurements follow a multimodal distribution and explain the causes of multiple local modes. The path capacity is a local mode, often different than the global mode of this distribution. We illustrate the effects of network load, cross-traffic packet-size variability, and probing packet size on the bandwidth distribution of packet pairs. We then switch to the dispersion of long packet trains. The mean of the packet-train dispersion distribution corresponds to a bandwidth metric that we refer to as average dispersion rate (ADR). We show that the ADR is a lower bound of the capacity and an upper bound of the available bandwidth of a path. Putting all of the pieces together, we present a capacity-estimation methodology that has been implemented in a tool called pathrate. We report on our experiences with pathrate after having measured hundreds of Internet paths over the last three years.