Soliton solutions of higher-order generalized derivative nonlinear Schrödinger equation

AbstractThe lax pair and Hirota’s bilinear form of higher-order generalized derivative nonlinear Schrödinger equation are given. The expression of N-soliton solutions are obtained through Hirota’s standard procedure

Deterministic and probabilistic analyses of offshore pile systems

The offshore pile system capacity and the pile capacity model biases are important aspects in the assessment of existing offshore platforms and in the performance reliability that is achieved using the state of practice. The objectives of this research are to improve understanding of the pile system behavior, to calibrate the pile system capacity model bias factors, and to evaluate the reliabilities of offshore pile systems. A simplified single pile failure surface in terms of three dimensional pile head loads is proposed based on the analytical lower and upper solutions, and is verified through finite element analyses. Numerical lower and upper bound models are then proposed for the ultimate capacity of a pile system, and are shown to be efficient and be effective in considering global torsion and out-of-plane failures. The evidence from the survival of offshore platforms indicates that (1) well conductors should be included in assessing the pile system ultimate capacity; (2) static p-y curves should be used which increases the pile system lateral capacity by 10 to 20%; (3) the mean value of the steel yield strength should be used; (4) jacket leg stubs should be included; and (5) site-specific geotechnical information is important. The model bias factors in the API load and resistance design recipe are calibrated through Bayes’ Theorem based on the predicted and observed performance of eighteen offshore platforms in recent Gulf of Mexico hurricanes. The API load and resistance design recipe is calibrated to be close to unbiased for predicting the jacket system performance; be slightly conservative for predicting a foundation overturning failure in clay; and be conservative for predicting a lateral failure in clay and a foundation overturning failure in sand. The reliability of a pile system is shown to be insensitive to water depths and locations in the Gulf of Mexico, but depends on the pile layout, number of piles, loading direction, and expected failure mode. The pile system redundancy (a measure of capacity beyond failure of the first element) and robustness (a measure of capacity when the system is damaged) depend on the failure mode, pile geometry and layout, and loading directions. In general, the 8-leg pile system is more redundant and more robust than the 3-leg and 4-leg pile systems. The complexity (a measure of the how well the most critically-loaded element represents all elements) depends on the pile layout, the expected failure mode of a single pile and the pile capacity uncertainty. The complexity is generally small, indicating that the failure probability of the most critically-loaded pile is representative of the failure probabilities for all piles.Curriculum and Instructio

VIGAN: Missing View Imputation with Generative Adversarial Networks

In an era when big data are becoming the norm, there is less concern with the quantity but more with the quality and completeness of the data. In many disciplines, data are collected from heterogeneous sources, resulting in multi-view or multi-modal datasets. The missing data problem has been challenging to address in multi-view data analysis. Especially, when certain samples miss an entire view of data, it creates the missing view problem. Classic multiple imputations or matrix completion methods are hardly effective here when no information can be based on in the specific view to impute data for such samples. The commonly-used simple method of removing samples with a missing view can dramatically reduce sample size, thus diminishing the statistical power of a subsequent analysis. In this paper, we propose a novel approach for view imputation via generative adversarial networks (GANs), which we name by VIGAN. This approach first treats each view as a separate domain and identifies domain-to-domain mappings via a GAN using randomly-sampled data from each view, and then employs a multi-modal denoising autoencoder (DAE) to reconstruct the missing view from the GAN outputs based on paired data across the views. Then, by optimizing the GAN and DAE jointly, our model enables the knowledge integration for domain mappings and view correspondences to effectively recover the missing view. Empirical results on benchmark datasets validate the VIGAN approach by comparing against the state of the art. The evaluation of VIGAN in a genetic study of substance use disorders further proves the effectiveness and usability of this approach in life science.Comment: 10 pages, 8 figures, conferenc

Practical Stabilization of Uncertain Nonholonomic Mobile Robots Based on Visual Servoing Model with Uncalibrated Camera Parameters

The practical stabilization problem is addressed for a class of uncertain nonholonomic mobile robots with uncalibrated visual parameters. Based on the visual servoing kinematic model, a new switching controller is presented in the presence of parametric uncertainties associated with the camera system. In comparison with existing methods, the new design method is directly used to control the original system without any state or input transformation, which is effective to avoid singularity. Under the proposed control law, it is rigorously proved that all the states of closed-loop system can be stabilized to a prescribed arbitrarily small neighborhood of the zero equilibrium point. Furthermore, this switching control technique can be applied to solve the practical stabilization problem of a kind of mobile robots with uncertain parameters (and angle measurement disturbance) which appeared in some literatures such as Morin et al. (1998), Hespanha et al. (1999), Jiang (2000), and Hong et al. (2005). Finally, the simulation results show the effectiveness of the proposed controller design approach

The eDAL Suite: Tools and Concepts for Primary Data Citation

Retrieval and citation of primary data is the important factor in the approaching age of “data science”. Digital data are easily shared, and just as easily wiped or lost. The problem of keeping on-line data accessible and
retrievable is especially difficult for SME like plant breeders plant biotech companies as well as research projects in this domain.
Intension of eDAL is the provisioning of an information retrieval and data citation infrastructure that meets the requirements of the “data science” age and implements a re-usable platform for data retrieval, data
citation, and data publication. Like a shopping cart, the idea is to combine a search engine and a data cart, which retrieves, rank and collect query relevant data from crop plant data centers

Formulation and Application of a New Critical State Model for Clays

Master'sMASTER OF ENGINEERIN

A 97 fJ/Conversion Neuron-ADC with Reconfigurable Sampling and Static Power Reduction

A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work. The Neuron-ADC leverages level-crossing sampling and a bio-inspired refractory circuit to compressively converts bio-signal to digital spikes and information-of-interest. The proposed design can not only avoid dissipating ADC energy on unnecessary data but also achieve reconfigurable sampling, making it appropriate for either low power operation or high accuracy conversion when dealing with various kinds of bio-signals. Moreover, the proposed dynamic comparator can reduce static power up to 41.1% when tested with a 10 kHz sinusoidal input. Simulation results of 40 nm CMOS process show that the Neuron-ADC achieves a maximum ENOB of 6.9 bits with a corresponding FoM of 97 fJ/conversion under 0.6 V supply voltage.Comment: Accepted to 2022 IEEE the 18th Asia Pacific Conference on Circuits and Systems (APCCAS

Image Encoding Using Multi-Level DNA Barcodes with Nanopore Readout.

Deoxyribonucleic acid (DNA) nanostructure-based data encoding is an emerging information storage mode, offering rewritable, editable, and secure data storage. Herein, a DNA nanostructure-based storage method established on a solid-state nanopore sensing platform to save and encrypt a 2D grayscale image is proposed. DNA multi-way junctions of different sizes are attached to a double strand of DNA carriers, resulting in distinct levels of current blockades when passing through a glass nanopore with diameters around 14 nm. The resulting quaternary encoding doubles the capacity relative to a classical binary system. Through toehold-mediated strand displacement reactions, the DNA nanostructures can be precisely added to and removed from the DNA carrier. By encoding the image into 16 DNA carriers using the quaternary barcodes and reading them in one simultaneous measurement, the image is successfully saved, encrypted, and recovered. Avoiding any proteins or enzymatic reactions, the authors thus realize a pure DNA storage system on a nanopore platform with increased capacity and programmability

Contactless Electrocardiogram Monitoring with Millimeter Wave Radar

The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring