A Compressed Sensing Approach to Uncertainty Propagation for Approximately Additive Functions

Computational models for numerically simulating physical systems are increasingly being used to support decision-making processes in engineering. Processes such as design decisions, policy level analyses, and experimental design settings are often guided by information gained from computational modeling capabilities. To ensure effective application of results obtained through numerical simulation of computational models, uncertainty in model inputs must be propagated to uncertainty in model outputs. For expensive computational models, the many thousands of model evaluations required for traditional Monte Carlo based techniques for uncertainty propagation can be prohibitive. This paper presents a novel methodology for constructing surrogate representations of computational models via compressed sensing. Our approach exploits the approximate additivity inherent in many engineering computational modeling capabilities. We demonstrate our methodology on some analytical functions, with comparison to the Gaussian process regression, and a cooled gas turbine blade application. We also provide some possible methods to build uncertainty information for our approach. The results of these applications reveal substantial computational savings over traditional Monte Carlo simulation with negligible loss of accuracy

Evaluation and Establishing Strategies of Blended Learning

Blended learning is a new emerging learning method which integrates online learning and face-to- face learning. This paper aims at discussing the advantages and disadvantages of blended learning and propose approaches to fix some existing problems. Moreover, this paper also gives attention to Chinese blended learning experimental examples. The ultimate goal is to improve hybrid learning and benefit students and teachers

Changer: Feature Interaction is What You Need for Change Detection

Change detection is an important tool for long-term earth observation missions. It takes bi-temporal images as input and predicts "where" the change has occurred. Different from other dense prediction tasks, a meaningful consideration for change detection is the interaction between bi-temporal features. With this motivation, in this paper we propose a novel general change detection architecture, MetaChanger, which includes a series of alternative interaction layers in the feature extractor. To verify the effectiveness of MetaChanger, we propose two derived models, ChangerAD and ChangerEx with simple interaction strategies: Aggregation-Distribution (AD) and "exchange". AD is abstracted from some complex interaction methods, and "exchange" is a completely parameter\&computation-free operation by exchanging bi-temporal features. In addition, for better alignment of bi-temporal features, we propose a flow dual-alignment fusion (FDAF) module which allows interactive alignment and feature fusion. Crucially, we observe Changer series models achieve competitive performance on different scale change detection datasets. Further, our proposed ChangerAD and ChangerEx could serve as a starting baseline for future MetaChanger design.Comment: 11 pages, 5 figure

A Compressed Sensing Approach to Uncertainty Propagation for Approximately Additive Functions

Computational models for numerically simulating physical systems are increasingly being used to support decision-making processes in engineering. Processes such as design decisions, policy level analyses, and experimental design settings are often guided by information gained from computational modeling capabilities. To ensure effective application of results obtained through numerical simulation of computational models, uncertainty in model inputs must be propagated to uncertainty in model outputs. For expensive computational models, the many thousands of model evaluations required for traditional Monte Carlo based techniques for uncertainty propagation can be prohibitive. This paper presents a novel methodology for constructing surrogate representations of computational models via compressed sensing. Our approach exploits the approximate additivity inherent in many engineering computational modeling capabilities. We demonstrate our methodology on some analytical functions, with comparison to the Gaussian process regression, and a cooled gas turbine blade application. We also provide some possible methods to build uncertainty information for our approach. The results of these applications reveal substantial computational savings over traditional Monte Carlo simulation with negligible loss of accuracy

Economic Operation and Planning of Distribution System Sources

This thesis presents the findings of some research carried out pertaining to economic operation and planning distribution systems. An optimal capacitor switching algorithm is developed for distribution system based on backward-forward sweep algorithm, which can assist in real-time applications. Thereafter, an optimal reconfiguration algorithm is proposed for distribution networks that seek to minimize losses by reducing the number of spanning trees in the network. The proposed algorithm provides a faster solution method and is useful for practical applications. Finally, the issues of short-term operating and long term planning of distribution networks in the presence of distributed generators is examined. An optimization framework is developed to determine the optimal locations of these distributed generator units

Prediction of solubility of amino acids based on COSMO calculation

In order to maximize the concentration of amino acids in the culture, we need to obtain solubility of amino acid as a function of concentration of other components in the solution. This function can be obtained by calculating the activity coefficient along with solubility model. The activity coefficient of the amino acid can be calculated by UNIFAC. Due to the wide range of applications of UNIFAC, the prediction of the activity coefficient of amino acids is not very accurate. So we want to fit the parameters specific to amino acids based on the UNIFAC framework and existing solubility data. Due to the lack of solubility of amino acids in the multi-system, some interaction parameters are not available. COSMO is a widely used way to describe pairwise interactions in the solutions in the chemical industry. After suitable assumptions COSMO can calculate the pairwise interactions in the solutions, and largely reduce the complexion of quantum chemical calculation. In this paper, a method combining quantum chemistry and COSMO calculation is designed to accurately predict the solubility of amino acids in multi-component solutions in the absence of parameters, as a supplement to experimental data

Universal linear optical operations on discrete phase-coherent spatial modes

Linear optical operations are fundamental and significant for both quantum mechanics and classical technologies. We demonstrate a non-cascaded approach to perform arbitrary unitary and non-unitary linear operations for N-dimensional phase-coherent spatial modes with meticulously designed phase gratings. As implemented on spatial light modulators (SLMs), the unitary transformation matrix has been realized with dimensionalities ranging from 7 to 24 and the corresponding fidelities are from 95.1% to 82.1%. For the non-unitary operators, a matrix is presented for the tomography of a 4-level quantum system with a fidelity of 94.9%. Thus, the linear operator has been successfully implemented with much higher dimensionality than that in previous reports. It should be mentioned that our method is not limited to SLMs and can be easily applied on other devices. Thus we believe that our proposal provides another option to perform linear operation with a simple, fixed, error-tolerant and scalable scheme

Programmable coherent linear quantum operations with high-dimensional optical spatial modes

A simple and flexible scheme for high-dimensional linear quantum operations on optical transverse spatial modes is demonstrated. The quantum Fourier transformation (QFT) and quantum state tomography (QST) via symmetric informationally complete positive operator-valued measures (SIC POVMs) are implemented with dimensionality of 15. The matrix fidelity of QFT is 0.85, while the statistical fidelity of SIC POVMs and fidelity of QST are ~0.97 and up to 0.853, respectively. We believe that our device has the potential for further exploration of high-dimensional spatial entanglement provided by spontaneous parametric down conversion in nonlinear crystals