Significant wave height forecasting based on the hybrid EMD-SVM method

1957-1962Prediction of significant wave height (SWH) is considered an effective method in marine engineering and prevention of marine disasters. Support vector machine (SVM) model has limitations in processing nonlinear and non-stationary SWH time series. Fortunately, empirical mode decomposition (EMD) can effectively deal with the complicated series. So, the SWH prediction method based on EMD and SVM is proposed by combining the advantages of both methods. A statistical analysis was carried out to compare the results of two models i.e., between the hybrid EMD-SVM and SVM. In addition, two models are used for forecasting SWH with 3, 6, 12 and 24 hours lead times, respectively. A high R value of different prediction times for the hybrid model. Results indicate that SWH prediction of the hybrid EMD-SVM model is superior to the SVM model

Regularized Two Level Algorithms for Model Problems

In previous work, we experimented a variant of the two-level ideal algorithm for parametric shape optimization that was proposed by Desideri, and two of three approaches were shown superior. In this report, we try to look them in a more classic way -- in terms of singular value decomposition (SVD) and regularizations, for model problems, i.e., we combine these regularization tools with these two-level ideal algorithms. Numerical results show that regularized two level algorithms are more robust

Planetary gearbox remaining useful life estimation based on state space model

As planetary gearboxes are widely used in various kinds of engineering, the fault diagnosis and prognosis of planetary gearbox is very important. This paper proposes a remaining useful life estimation method based on state space model. The degradation process is assumed to be Gamma distribution. And experience maximization method and particle filter is used to estimate the parameters of state space model. A planetary gearbox life-cycle experiment is done to obtain the degradation data and verify the effectiveness of the proposed method

Role of Fiber Orientation in Atrial Arrythmogenesis

Electrical wave-front propagation in the atria is determined largely by local fiber orientation. Recent study suggests that atrial fibrillation (AF) progresses with enhanced anisotropy. In this work, a 3D rabbit atrial anatomical model at 20 × 20 × 20 μm3 resolution with realistic fiber orientation was constructed based on the novel contrast-enhanced micro-CT imaging. The Fenton-Karma cellular activation model was adapted to reproduce rabbit atrial action potential period of 80 ms. Diffusivities were estimated for longitudinal and transverse directions of the fiber orientation respectively. Pacing was conducted in the 3D anisotropic atrial model with a reducing S2 interval to facilitate initiation of atrial arrhythmia. Multiple simulations were conducted with varying values of diffusion anisotropy and stimulus locations to evaluate the role of anisotropy in initiating AF. Under physiological anisotropy conditions, a rapid right atrial activation was followed by the left atrial activation. Excitation waves reached the atrio-ventricular border where they terminated. Upon reduction of conduction heterogeneity, re-entry was initiated by the rapid pacing and the activation of both atrial chambers was almost simultaneous. Myofiber orientation is an effective mechanism for regulating atrial activation. Modification of myoarchitecture is proarrhythmic

Harnessing Context for Budget-Limited Crowdsensing with Massive Uncertain Workers

Crowdsensing is an emerging paradigm of ubiquitous sensing, through which a crowd of workers are recruited to perform sensing tasks collaboratively. Although it has stimulated many applications, an open fundamental problem is how to select among a massive number of workers to perform a given sensing task under a limited budget. Nevertheless, due to the proliferation of smart devices equipped with various sensors, it is very difficult to profile the workers in terms of sensing ability. Although the uncertainties of the workers can be addressed by standard Combinatorial Multi-Armed Bandit (CMAB) framework through a trade-off between exploration and exploitation, we do not have sufficient allowance to directly explore and exploit the workers under the limited budget. Furthermore, since the sensor devices usually have quite limited resources, the workers may have bounded capabilities to perform the sensing task for only few times, which further restricts our opportunities to learn the uncertainty. To address the above issues, we propose a Context-Aware Worker Selection (CAWS) algorithm in this paper. By leveraging the correlation between the context information of the workers and their sensing abilities, CAWS aims at maximizing the expected total sensing revenue efficiently with both budget constraint and capacity constraints respected, even when the number of the uncertain workers are massive. The efficacy of CAWS can be verified by rigorous theoretical analysis and extensive experiments

Regularized Two Level Algorithms for Model Problems

In previous work, we experimented a variant of the two-level ideal algorithm for parametric shape optimization that was proposed by Desideri, and two of three approaches were shown superior. In this report, we try to look them in a more classic way -- in terms of singular value decomposition (SVD) and regularizations, for model problems, i.e., we combine these regularization tools with these two-level ideal algorithms. Numerical results show that regularized two level algorithms are more robust

A critical review on recent progress of solution-processed monolayer assembly of nanomaterials and applications

The rapid development in nanotechnology has necessitated accurate and efficient assembly strategies for nanomaterials. Monolayer assembly of nanomaterials (MAN) represents an extreme challenge in manufacturing and is critical in understanding interactions among nanomaterials, solvents, and substrates. MAN enables highly tunable performance in electronic and photonic devices. This review summarizes the recent progress on the methods to achieve MAN and discusses important control factors. Moreover, the importance of MAN is elaborated by a broad range of applications in electronics and photonics. In the end, we outlook the opportunities as well as challenges in manufacturing and new applications

Research on the Stability of Pickering Emulsion and Its Application in Food Field

Pickering emulsions is a new emulsions system formed by replacing traditional emulsifiers with solid particles, which has some advantages such as strong stability, environmentally-friendly, high safety and so on. It has been highly favored in the fields of food, cosmetics, chemical materials and biomedicine. Based on the stability mechanism of Pickering emulsions, this review mainly discusses relevant factors affecting its stability from six aspects, including the type of solid particles, shape of solid particles, concentration of solid particles, surface charge of aqueous phase, volume fraction of oil-water phase and the wettability. Meanwhile, the achievements of domestic and overseas on Pickering emulsions are also summarized, including preparing the intelligent food films, preventing the lipid oxidation, delivering the bioactive substances, synthesizing the molecularly imprinted polymers, achieving biphasic catalysis, and constructing 4D printed food raw materials in recent years. This paper aims to provide theoretical basis and technical support to a certain extent for the diversified development of food industry and other related fields