Generalization of Subpixel Analysis for Hyperspectral Data With Flexibility in Spectral Similarity Measures

Several spectral unmixing techniques have been developed for subpixel mapping using hyperspectral data in the past two decades, among which the fully constrained least squares method based on the linear spectral mixture model (LSMM) has been widely accepted. However, the shortage of this method is that the Euclidean spectral distance measure is used, and therefore, it is sensitive to the magnitude of the spectra. While other spectral matching criteria are available, such as spectral angle mapping (SAM) and spectral information divergence (SID), the current unmixing algorithm is unable to be extended to these measures. In this paper, we propose a unified subpixel mapping framework that models the unmixing process as a best match of the unknown pixel\u27s spectrum to a weighted sum of the endmembers\u27 spectra. We introduce sequential quadratic programming to solve the nonlinear optimization problem encountered in the implementation of this framework. The main feature of this proposed method is that it is not restricted to any particular similarity measures. Experiments were conducted with both simulated and Hyperion data. The tests demonstrated the proposed framework\u27s advantage in accommodating various spectral similarity measures and provided performance comparisons of the Euclidean distance measure with other spectral matching criteria including SAM, spectral correlation measure, and SID

Sliding Mode Robustness Control Strategy for Shearer Height Adjusting System

This paper firstly established mathematical model of height adjusting hydro cylinder of the shearer, as well as the state space equation of the shearer height adjusting system. Secondly we designed a shearer automatic height adjusting controller adopting the sliding mode robustness control strategy. The height adjusting controller includes the sliding mode surface switching function based on Ackermann formula, as well as sliding mode control function with the improved butterworth filter. Then simulation of the height adjustment controller shows that the sliding mode robustness control solves buffeting of typical controller, and achieves automatic control for the rolling drum of the shearer. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.373

Influence of diabetes on cardiac resynchronization therapy in heart failure patients: a meta-analysis

BACKGROUND: Diabetes mellitus is an independent risk factor of increased morbidity and mortality in patients with heart failure. Cardiac resynchronization therapy (CRT), a pacemaker-based therapy for dyssynchronous heart failure, improves cardiac performance and quality of life, but its effect on mortality in patients with diabetes is uncertain. METHODS: We performed a meta-analysis of results from randomized controlled trials (RCTs) of the long-term outcome of cardiac resynchronization therapy for heart failure in diabetic and non-diabetic patients. Literature search of MEDLINE via Pubmed for reports of randomized controlled trials of Cardiac resynchronization for chronic symptomatic left-ventricular dysfunction in patients with and without diabetes mellitus, with death as the outcome. Relevant data were analyzed by use of a random-effects model. Reports published from 1994 to 2011 that described RCTs of CRT for treating chronic symptomatic left ventricular dysfunction in patients with and without diabetes, with all-cause mortality as an outcome. RESULTS: A total of 5 randomized controlled trials met the inclusion criteria, for 2,923 patients. The quality of studies was good to moderate. Cardiac resynchronization significantly reduced the mortality for heart failure patients with or without diabetes mellitus. Mortality was 24.3% for diabetic patients with heart failure and 20.4 % for non-diabetics (odds ratio 1.28, 95% confidence interval 1.06–1.55; P = 0.010). CONCLUSIONS: Cardiac resynchronization therapy (CRT) may reduce mortality from progressive heart failure in patients with or without diabetes mellitus, but mortality may be higher for patients with than without diabetes after CRT for heart failure

Asymmetrical Siamese Network for Point Clouds Normal Estimation

In recent years, deep learning-based point cloud normal estimation has made great progress. However, existing methods mainly rely on the PCPNet dataset, leading to overfitting. In addition, the correlation between point clouds with different noise scales remains unexplored, resulting in poor performance in cross-domain scenarios. In this paper, we explore the consistency of intrinsic features learned from clean and noisy point clouds using an Asymmetric Siamese Network architecture. By applying reasonable constraints between features extracted from different branches, we enhance the quality of normal estimation. Moreover, we introduce a novel multi-view normal estimation dataset that includes a larger variety of shapes with different noise levels. Evaluation of existing methods on this new dataset reveals their inability to adapt to different types of shapes, indicating a degree of overfitting. Extensive experiments show that the proposed dataset poses significant challenges for point cloud normal estimation and that our feature constraint mechanism effectively improves upon existing methods and reduces overfitting in current architectures

ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis

We propose a novel Text-to-Image Generation Network, Adaptive Layout Refinement Generative Adversarial Network (ALR-GAN), to adaptively refine the layout of synthesized images without any auxiliary information. The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout Visual Refinement (LVR) loss. The ALR module aligns the layout structure (which refers to locations of objects and background) of a synthesized image with that of its corresponding real image. In ALR module, we proposed an Adaptive Layout Refinement (ALR) loss to balance the matching of hard and easy features, for more efficient layout structure matching. Based on the refined layout structure, the LVR loss further refines the visual representation within the layout area. Experimental results on two widely-used datasets show that ALR-GAN performs competitively at the Text-to-Image generation task.Comment: Accepted by TM

Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model

This paper focuses on the recently popular task of point cloud completion guided by multimodal information. Although existing methods have achieved excellent performance by fusing auxiliary images, there are still some deficiencies, including the poor generalization ability of the model and insufficient fine-grained semantic information for extracted features. In this work, we propose a novel multimodal fusion network for point cloud completion, which can simultaneously fuse visual and textual information to predict the semantic and geometric characteristics of incomplete shapes effectively. Specifically, to overcome the lack of prior information caused by the small-scale dataset, we employ a pre-trained vision-language model that is trained with a large amount of image-text pairs. Therefore, the textual and visual encoders of this large-scale model have stronger generalization ability. Then, we propose a multi-stage feature fusion strategy to fuse the textual and visual features into the backbone network progressively. Meanwhile, to further explore the effectiveness of fine-grained text descriptions for point cloud completion, we also build a text corpus with fine-grained descriptions, which can provide richer geometric details for 3D shapes. The rich text descriptions can be used for training and evaluating our network. Extensive quantitative and qualitative experiments demonstrate the superior performance of our method compared to state-of-the-art point cloud completion networks

Study on friction mechanism and performance of disc brakes for mining motor vehicle

According to the convexity theory, the working principle of the disc brake of mining motor vehicle is analyzed. The dry friction force between brake disc and brake block is mainly composed of three mechanisms, such as meshing effect, adhesion effect and furrow effect. The Link NVH 3900 device is used to simulate the long downhill braking condition with bench test method, which can obtain friction performance with high credibility in different brake conditions. The average friction coefficient and friction stability coefficient are chosen as the evaluation parameter for friction performance judgment. Through the research results, it can be known that the average friction coefficient of the brake decreases linearly with brake pressure increases, the friction stability coefficient under different braking pressure showed numerically larger and smaller fluctuations. When the temperature of disc surface exceeds 200 °C, the resin lubricating film will be produced in the brake block, which will decrease the friction coefficient obviously. The actual braking torque does not increase linearly with the increase of braking pressure, especially when the brake speed is high, the friction coefficient will decrease obviously

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts