Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval

Deformation and failure analysis of pinch-torsion based thermal runaway risk evaluation method of Li-ion cells

A new pinch-torsion test is developed for safety of Li-ion batteries that shows the stable capability of making small internal short-circuit spots effectively. The further deformation and failure analysis is conducted by finite element analysis and experiments. Two different loading conditions, pure pinch and pinch-torsion, are evaluated and compared which demonstrates that the addition of the torsion component significantly increased the maximum principal strain, and thus the internal short circuit induction. In addition, the vertical load in the pinch-torsion test is significantly less than it in the pinch test to generate the failure inside the battery, thus dramatically improving the applicability of the pinch test. Finally, an analytical stick-slip model rationalizes deformation mechanisms and the conclusion is made that the additional torsion only facilitates the failure of separator at the early stage which is typically a few degrees of rotation. The systematic investigation of the Li-ion cell deformation and failure provides insight for the optimization of the future battery safety experiment design

A method for the suppression of fluctuations in the neutral-point potential of a three-level NPC inverter with a capacitor-voltage loop

This paper investigates the problem of fluctuation of the neutral-point potential (NPP) in a three-level NPC inverter with a capacitor-voltage loop. The phase pulse width duty cycle disturbance PWM method is proposed to suppress the NPP fluctuation efficiently. Based on the basic carrier-based Phase Disposition (PD) PWM method, the average pulse neutral-point current model is established. Then the frequency, amplitude and equivalent initial phase of the NPP fluctuation are analyzed based on the current model. According to the alternating error of the DC-link capacitor voltages, a capacitor-voltage loop with a quasi PR (proportional resonant) controller is presented. The control variable, which varies with the modulation index, phase current, load power factor, etc, can be obtained from the quasi PR controller. Finally, an experimental three-level NPC inverter is described and the validity and feasibility of the proposed method are verified by experimental results

Intelligent model for offshore China sea fog forecasting

Accurate and timely prediction of sea fog is very important for effectively managing maritime and coastal economic activities. Given the intricate nature and inherent variability of sea fog, traditional numerical and statistical forecasting methods are often proven inadequate. This study aims to develop an advanced sea fog forecasting method embedded in a numerical weather prediction model using the Yangtze River Estuary (YRE) coastal area as a case study. Prior to training our machine learning model, we employ a time-lagged correlation analysis technique to identify key predictors and decipher the underlying mechanisms driving sea fog occurrence. In addition, we implement ensemble learning and a focal loss function to address the issue of imbalanced data, thereby enhancing the predictive ability of our model. To verify the accuracy of our method, we evaluate its performance using a comprehensive dataset spanning one year, which encompasses both weather station observations and historical forecasts. Remarkably, our machine learning-based approach surpasses the predictive performance of two conventional methods, the weather research and forecasting nonhydrostatic mesoscale model (WRF-NMM) and the algorithm developed by the National Oceanic and Atmospheric Administration (NOAA) Forecast Systems Laboratory (FSL). Specifically, in regard to predicting sea fog with a visibility of less than or equal to 1 km with a lead time of 60 hours, our methodology achieves superior results by increasing the probability of detection (POD) while simultaneously reducing the false alarm ratio (FAR).Comment: 19 pages, 9 figure

The Reform of Elastic-plastic Mechanics Teaching for Petroleum Engineering Related Majors

“Elastic-plastic Mechanics” is an important course for undergraduates and postgraduates of general engineering majors. However, the theoretical derivation of equations is complex, and the connection with engineering practice is inadequate. Therefore, teaching becomes difficult and boring for a number of students. Firstly, this paper introduces the importance of Elastic-plastic Mechanics for petroleum engineering related majors. Based on the recent teaching experience, the teaching reform of Elastic-plastic Mechanics course is carried out focusing on teaching method and learning content, and a discussion teaching mode based on students’ independent discussion and engineering cases is formed. Remarkable results have been achieved in improving students’ learning efficiency and classroom teaching effect. Furthermore, students’ comprehensive ability of independent innovation and practice is enhanced

Numerical Analysis and Strength Evaluation of an Exposed River Crossing Pipeline with Casing Under Flood Load

Pipelines in service always experience complicated loadings induced by operational and environmental conditions. Flood is one of the common natural hazard threats for buried steel pipelines. One exposed river crossing X70 gas pipeline induced by flood erosion was used as a prototype for this study. A mechanical model was established considering the field loading conditions. Morison equations were adopted to calculate distributional hydrodynamic loads on spanning pipe caused by flood flow. Nonlinear soil constraint on pipe was considered using discrete nonlinear soil springs. An explicit solution of bending stiffness for pipe segment with casing was derived and applied to the numerical model. The von Mises yield criterion was used as failure criteria of the X70 pipe. Stress behavior of the pipe were analyzed by a rigorous finite element model established by the general-purpose Finite-Element package ABAQUS, with 3D pipe elements and pipe-soil interaction elements simulating pipe and soil constraints on pipe, respectively. Results show that, the pipe is safe at present, as the maximum von Mises stress in pipe with the field parameters is 185.57 MPa. The critical flow velocity of the pipe is 5.8 m/s with the present spanning length. The critical spanning length of the pipe is 467 m with the present flow velocity. The failure pipe sections locate at the connection point of the bare pipe and the pipe with casing or the supporting point of the bare pipe on riverbed

Crossover between Weak Antilocalization and Weak Localization of Bulk States in Ultrathin Bi2Se3 Films

We report transport studies on the 5 nm thick Bi2Se3 topological insulator films which are grown via molecular beam epitaxy technique. The angle-resolved photoemission spectroscopy data show that the Fermi level of the system lies in the bulk conduction band above the Dirac point, suggesting important contribution of bulk states to the transport results. In particular, the crossover from weak antilocalization to weak localization in the bulk states is observed in the parallel magnetic field measurements up to 50 Tesla. The measured magneto-resistance exhibits interesting anisotropy with respect to the orientation of B// and I, signifying intrinsic spin-orbit coupling in the Bi2Se3 films. Our work directly shows the crossover of quantum interference effect in the bulk states from weak antilocalization to weak localization. It presents an important step toward a better understanding of the existing three-dimensional topological insulators and the potential applications of nano-scale topological insulator devices

Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis

<p>Abstract</p> <p>Background</p> <p>Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public.</p> <p>Results</p> <p>In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM). Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile.</p> <p>Conclusion</p> <p>Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases. Moreover, with its open source architecture, interested researchers can further develop and extend this software based on the existing infrastructure.</p

High temperature superconducting FeSe films on SrTiO3 substrates

Interface enhanced superconductivity at two dimensional limit has become one of most intriguing research directions in condensed matter physics. Here, we report the superconducting properties of ultra-thin FeSe films with the thickness of one unit cell (1-UC) grown on conductive and insulating SrTiO3 (STO) substrates. For the 1-UC FeSe on conductive STO substrate (Nb-STO), the magnetization versus temperature (M-T) measurement shows a diamagnetic signal at 85 K, suggesting the possibility of superconductivity appears at this high temperature. For the FeSe films on insulating STO substrate, systematic transport measurements were carried out and the sheet resistance of FeSe films exhibits Arrhenius TAFF behavior with a crossover from a single-vortex pinning region to a collective creep region. More intriguing, sign reversal of Hall resistance with temperature is observed, demonstrating a crossover from hole conduction to electron conduction above Tc in 1-UC FeSe films

LncRNAs: the bridge linking RNA and colorectal cancer.

Long noncoding RNAs (lncRNAs) are transcribed by genomic regions (exceeding 200 nucleotides in length) that do not encode proteins. While the exquisite regulation of lncRNA transcription can provide signals of malignant transformation, lncRNAs control pleiotropic cancer phenotypes through interactions with other cellular molecules including DNA, protein, and RNA. Recent studies have demonstrated that dysregulation of lncRNAs is influential in proliferation, angiogenesis, metastasis, invasion, apoptosis, stemness, and genome instability in colorectal cancer (CRC), with consequent clinical implications. In this review, we explicate the roles of different lncRNAs in CRC, and the potential implications for their clinical application