Low Carbon Energy Policy Research

AbstractCase study of Korea, Low carbon energy efficiency labeling schemes (Energy Efficiency Label and Standard Program, High efficiency Appliance Certification Program, e-Standby Program) play a key role in carrying out the energy efficiency improvement policy in the appliances and equipment sector in Korea. Korea operates these Programs in an effort to improve energy efficiency in appliances and equipments. Mandatory energy efficiency standard which bans production and sales of low energy efficiency products which fall below the minimum energy performance standard. Ministry of Knowledge of Economy (MKE) and Korea Energy Management Corporation (KEMCO) is the key organizations in implementing energy efficiency standards and labeling. National energy efficiency efforts can be realized through energy efficiency improvements with the successful implementation of an energy efficient appliances dissemination policy and the phase out of low efficiency appliances. Through the implementation of the Energy Efficiency Label and Standard Program (1992), High-efficiency Appliance Certification Program (1996) and e-Standby Program (1999), significant energy efficiency improvements have been achieved, and 1.37 billion USD worth of energy savings

Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification.

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straight forward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification

Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution

Due to the limitations of hyperspectral imaging systems, hyperspectral imagery (HSI) often suffers from poor spatial resolution, thus hampering many applications of the imagery. Hyperspectral super-resolution refers to fusing HSI and MSI to generate an image with both high spatial and high spectral resolutions. Recently, several new methods have been proposed to solve this fusion problem, and most of these methods assume that the prior information of the Point Spread Function (PSF) and Spectral Response Function (SRF) are known. However, in practice, this information is often limited or unavailable. In this work, an unsupervised deep learning-based fusion method - HyCoNet - that can solve the problems in HSI-MSI fusion without the prior PSF and SRF information is proposed. HyCoNet consists of three coupled autoencoder nets in which the HSI and MSI are unmixed into endmembers and abundances based on the linear unmixing model. Two special convolutional layers are designed to act as a bridge that coordinates with the three autoencoder nets, and the PSF and SRF parameters are learned adaptively in the two convolution layers during the training process. Furthermore, driven by the joint loss function, the proposed method is straightforward and easily implemented in an end-to-end training manner. The experiments performed in the study demonstrate that the proposed method performs well and produces robust results for different datasets and arbitrary PSFs and SRFs

Single-crystalline gold nanodisks on WS2_2 mono- and multilayers: Strong coupling at room temperature

Engineering light-matter interactions up to the strong-coupling regime at room temperature is one of the cornerstones of modern nanophotonics. Achieving this goal will enable new platforms for potential applications such as quantum information processing, quantum light sources and even quantum metrology. Materials like transition metal dichalcogenides (TMDC) and in particular tungsten disulfide (WS$_2$) possess large transition dipole moments comparable to semiconductor-based quantum dots, and strong exciton binding energies allowing the detailed exploration of light-matter interactions at room temperature. Additionally, recent works have shown that coupling TMDCs to plasmonic nanocavities with light tightly focused on the nanometer scale can reach the strong-coupling regime at ambient conditions. Here, we use ultra-thin single-crystalline gold nanodisks featuring large in-plane electromagnetic dipole moments aligned with the exciton transition-dipole moments located in monolayer WS$_2$. Through scattering and reflection spectroscopy we demonstrate strong coupling at room temperature with a Rabi splitting of $\sim$108 meV. In order to go further into the strong-coupling regime and inspired by recent experimental work by St\"uhrenberg et al., we couple these nanodisks to multilayer WS$_2$. Due to an increase in the number of excitons coupled to our nanodisks, we achieve a Rabi splitting of $\sim$175 meV, a major increase of 62%. To our knowledge, this is the highest Rabi splitting reported for TMDCs coupled to open plasmonic cavities. Our results suggest that ultra-thin single-crystalline gold nanodisks coupled to WS$_2$ represent an exquisite platform to explore light-matter interactions

Comparison among Reconstruction Algorithms for Quantitative Analysis of 11

Objective. Kinetic modeling of dynamic 11C-acetate PET imaging provides quantitative information for myocardium assessment. The quality and quantitation of PET images are known to be dependent on PET reconstruction methods. This study aims to investigate the impacts of reconstruction algorithms on the quantitative analysis of dynamic 11C-acetate cardiac PET imaging. Methods. Suspected alcoholic cardiomyopathy patients (N=24) underwent 11C-acetate dynamic PET imaging after low dose CT scan. PET images were reconstructed using four algorithms: filtered backprojection (FBP), ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), and OSEM with both time-of-flight and point-spread-function (TPSF). Standardized uptake values (SUVs) at different time points were compared among images reconstructed using the four algorithms. Time-activity curves (TACs) in myocardium and blood pools of ventricles were generated from the dynamic image series. Kinetic parameters K1 and k2 were derived using a 1-tissue-compartment model for kinetic modeling of cardiac flow from 11C-acetate PET images. Results. Significant image quality improvement was found in the images reconstructed using iterative OSEM-type algorithms (OSME, TOF, and TPSF) compared with FBP. However, no statistical differences in SUVs were observed among the four reconstruction methods at the selected time points. Kinetic parameters K1 and k2 also exhibited no statistical difference among the four reconstruction algorithms in terms of mean value and standard deviation. However, for the correlation analysis, OSEM reconstruction presented relatively higher residual in correlation with FBP reconstruction compared with TOF and TPSF reconstruction, and TOF and TPSF reconstruction were highly correlated with each other. Conclusion. All the tested reconstruction algorithms performed similarly for quantitative analysis of 11C-acetate cardiac PET imaging. TOF and TPSF yielded highly consistent kinetic parameter results with superior image quality compared with FBP. OSEM was relatively less reliable. Both TOF and TPSF were recommended for cardiac 11C-acetate kinetic analysis

Calculation of Residual Surface Subsidence Above Abandoned Longwall Coal Mining

Exhausted or abandoned underground longwall mining may lead to long-term residual subsidence on surface land, which can cause some problems when the mined-out land is used for construction, land reclamation and ecological reconstruction. Thus, it is important to assess the stability and suitability of the land with a consideration of residual surface subsidence. Assuming a linear monotonic decrease in the annual residual surface subsidence, the limit of the sum of the annual residual subsidence factor, and continuity between surface subsidence in the last year of the weakening period and the residual surface subsidence in the first year, we establish a model to calculate the duration of residual subsidence and the annual residual surface subsidence factor caused by abandoned longwall coal mining. The duration of residual surface subsidence increases with the increase in mining thickness as well as the factor of extreme residual subsidence. The proposed method can quantitatively calculate the annual residual subsidence, the accumulative residual subsidence, and the potential future accumulative residual subsidence. This approach can be used to reasonably evaluate the stability and suitability of old mining subsidence areas and will be beneficial for the design of mining subsidence land reclamation and ecological reconstruction

Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straightforward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification

An Approach to Generate Spatial Voronoi Treemaps for Points, Lines, and Polygons

As a space-filling method, Voronoi Treemaps are used for showcasing hierarchies. Previously presented algorithms are limited to visualize nonspatial data. The approach of spatial Voronoi Treemaps is proposed in this paper to eliminate these problems by enabling the subdivisions for points, lines, and polygons with spatial coordinates and references. The digital distance transformation is recursively used to generate nested raster Voronoi polygons while the raster to vector conversion is used to create a vector-based Treemap visualization in a GIS (geographic information system) environment. The objective is to establish a spatial data model to better visualize and understand the hierarchies in the geographic field