Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Satellite Image Change Detection Via Transformer-Based Siamese Network

Optical satellite image change detection is essential to monitor the use of Earth's resources. Convolutional neural networks(CNN)-based methods exhibit excellent performance on change detection. As Transformers became the de-facto standard in the field of natural language processing(NLP), there were more and more methods based on it are proposed in computer vision, such as image classification, object detection, semantic segmentation and so on. Many proposed models based on vision Transformer(ViT) have surpassed the performance of CNN and show effectiveness and superiority. With the emergence of more and more applications of ViT in the field of image processing, it's advantages are gradually being explored. In terms of change detection, the CNN-based models have already shown great advantages over traditional methods. In view of current achievements of Transformer, we decided to apply Transformer to change detection in optical satellite image. Change detection of bitemporal images, we need to take two images as inputs. So we proposed a Siamese extensions of ViT networks which achieve the best results in tests on two open change detection datasets. Experimental results on real datasets show the effectiveness and the superiority of the proposed network.
A Hierarchical Classification Head based Convolutional Gated Deep Neural Network for Automatic Modulation Classification

Automatic modulation classification (AMC) identifies a received signal’s modulation scheme without prior knowledge of the intercepted signal, which enables significant applications in both the military and civilian domains. Inspired by the great success of deep learning (DL), lots of neural networks are introduced into AMC. To further improve classification performance, various complementary cues including in-phase/quadrature (I/Q), amplitude/phase (A/P), constellation, and other formats are used together to enhance the discrimination of the DL model, where only outputs of the last layer are used. In this paper, we find that different layers’ outputs in the DL model are also complementary to each other. As a result, a hierarchical classification head based convolutional gated deep neural network (HCGDNN) is proposed by utilizing different layers’ output, which only uses the I/Q cue. The proposed HCGDNN consists of three groups of convolutional neural networks (CNN) blocks, two groups of bidirectional gated recurrent units (BiGRU), and a hierarchical classification head. Compared to the long short-term memory (LSTM), the BiGRU has a smaller computational complexity and also releases the gradient dispersion and explosion in the training phase. With the help of the hierarchical classification head, three groups of modulation predictions are made for a received I/Q signal. After that, a novel nonlinear optimization fusion method is derived to generate fusion weights to fuse different groups, then a final classification decision is made. Compared to AMC methods using various cues, the proposed HCGDNN only uses I/Q cue and has low computational overhead. Numerical results suggest that the newly developed HCGDNN achieves superior performance on the public benchmark1.
RI-DC: Rotation-Invariant Detection and Classification for Wheat Head Detection

We propose a novel automatic detection method of wheat heads from images taken from above wheat fields. The automatic detection of heads is useful for predicting yields. For this purpose, deep learning based methods has proved to be effective recently, but they are not robust against the variations of head directions. To tackle this problem, we utilize a two-step approach which first carries out object detection for augmented test images rotated in many directions, and then classifies the detected objects by using a classifier trained with many rotated images. It was evaluated by using GWHD dataset and proved to be effective.
Multi-Channel Residual Learning for Marine Mammal Vocalization Classification

Marine mammal vocalizations provide a reliable method of identifying most species. These vocalizations may be effectively captured by the passive acoustic monitoring of underwater acoustic signals, but this approach generates prohibitively large amounts of data, making a machine learning approach desirable. In this study, we demonstrate that residual learning networks may be highly effective at classifying vocalizations by species and investigate the utility of multi-channel spectrograms in training these networks. We find that classification performance can be improved by effectively preprocessing the acoustic data. A series of tests are used to assess optimal parameters for noise-removal, spectrographic window functions, preprocessing augmentations, and multi-channel spectrogram generation. While we find that our networks do learn additional information from multi-channel spectrograms, we demonstrate that single-channel spectrograms still offer superior classification performance in 32-class classification tasks.
Applying the Chirp Scaling Algorithm for Efficient Beamforming of Ultrasound Images

To produce high-resolution ultrasound images, synthetic aper-ture acquisitions are used, which contain a large amount of data that is difficult to beamform in real time with traditional methods like delay and sum (DAS). Although frequency do-main beamforming methods have been adapted to ultrasound, the existing adaptations require interpolation. The chirp scaling algorithm (CSA), which was originally developed for radar remote sensing applications, avoids interpolation by using the Fourier transform shift property, so modifying CSA for ultrasound could expedite computation while avoiding interpolation error, improving rapid image formation. This work adapts CSA theory for typical monostatic ultrasound acquisitions and tests the algorithm on point target and le-sion simulations to analyze image quality and computational speed. While CSA achieves similar image quality as related frequency domain beamformers (such as the range-Doppler algorithm - RDA), its runtime is lower than both DAS and RDA. With further adaptations, the algorithm could benefit real-time, high-resolution clinical imaging.
An Unfolded Deep Network for SAR Imaging Based on General Regularization and S-TLS Model

Synthetic aperture radar (SAR) can obtain two-dimensional images of the illuminated area, which is an important means for earth remote sensing and monitoring. However, due to the loss of azimuth data and system errors during the processing of data sampling, it is necessary to study the method for high-quality SAR image reconstruction from down-sampled data in the condition of measurement inaccuracy. Considering these factors, this paper proposes a sparsity-driven SAR imaging method based on general regularization and the sparse total least-squares (S-TLS) model and implements the method by an unfolded deep network. In the proposed method, general regularization can solve the problem of sparse sampling, and the S-TLS model is adopted to deal with measurement inaccuracy. Moreover, through the deep network implementation, the proposed is more time-efficient and can exploit more effective scene prior knowledge, making the proposed method suitable in practical applications. Experiments verify the effectiveness of the proposed method.
Full-Spectrum Spectral Super-Resolution Method Based on LSMM

Full-spectrum remote sensing images can simultaneously provide reflectance and emission information about objects, which has great application value. Hyperspectral imaging can record hundreds of spectral bands, but due to technical and space limitations, full-spectrum hyperspectral images (HSI) are difficult to obtain. Recently, we proposed a spectral super-resolution method based on the Linear Spectral Mixing Model (LSMM), which can generate full-spectrum hyperspectral images (HSI) from multispectral images (MSI). After the spectral-unmixing of MSI, we transform MS endmembers into full-spectrum HS endmembers by spectral library. Since the abundance of MSI and HSI with the same spatial resolution is consistent, we linearly mixed the abundance and HS endmember to obtain the full spectrum HSI. In this work, we use Sentinel-2 dataset and EO-1 ALI/Hyperion images to verify the accuracy and applicability. Compared with other works, our method can simulate full-spectrum HSI of large-area scenes without real HSI, which has a certain accuracy and provides more comprehensive information for applications.
Photovoltaic Installations Change Detection from Remote Sensing Images Using Deep Learning

The development and monitoring of Photovoltaic (PV) installations is of great interests for the Chinese energy management agency in recent years. The traditional land change detection of PV installations has issues pertaining to low efficiency and high missed detection rates. Therefore, this paper explores an efficient and high accurate detection method of PV installations land using changes from remote sensing images in order to help relevant stakeholders to better manage and monitor urban energy and environment. In this paper, Full Convolutional Network (FCN) and classical segmentation convolutional network (U-Net) based deep learning algorithms are used to build change detection models. To evaluate the model performance, we have built the change detection dataset from Northeast Petroleum University - Photovoltaic Remote Sensing Dataset (NEPU-PRSD) of PV installations in Western China. The experimental results show that both models can achieve good accuracy in change detection regarding PV installations.
Land Cover Classification from Street-Level Photos

Land cover classification mapping is a process to depict terrestrial surfaces by pre-defined thematic classes across space and is often implemented by a supervised classification approach with satellite and/or aero images. However, due to labor-intensive and time-consuming tasks to identify land cover class from those images manually, it is challenging to build richquantity and highquality reference data sets for the mapping. To capture land covers on the ground, we developed a deep learning model to estimate land covers from street-level photos. A transfer learning from the pre-trained DenseNet was applied, and this model yields an overall accuracy of 0.91. This model contributes to the subsequent study of building a semi-automatic reference database from geo-tagged street-level photos.
Wet Tropospheric Delay Correction of Wide-Swath Altimeter based on the Mixing of Nadir Radiometer and Gridded Data Product

Wet tropospheric delay (WPD) is a critical error source of altimeters. The current radiometers have been able to obtain ideal along-track correction values. However, the small-scale spatial variability of the water vapor is a huge challenge for correcting the wide-swath altimeter. Even though the total residual error can meet the error budget by a two-beam radiometer, the correction effect at the edge of the swath is still difficult to control. This paper proposes a WPD correction method based on mixing nadir radiometer and gridded data product by spatial correlation function. The corrected residual root mean square error (RMSE) and cross-track distribution characteristics of different methods are compared through the simulation data. The results show that the total RMSE of the mixed method is similar to the two-beam method but has better cross-track stability.
A methodology to improve the predictability of wind energy generation with confirmatory evidence from Germany

Using data from the 50Hertz electric power system in Germany, this paper presents a methodology to improve the short-run predictability of wind energy generation. The analysis proceeds by investigating the nature of the intraday forecast errors. It is observed that the current intraday wind energy forecasts do not fully reflect expected meteorological conditions. Following this finding, the modeling approach predicts wind energy generation based on forecasted/simulated levels of wind energy generation and forecasted/simulated meteorological conditions. The model was estimated using 15-minute data from Jan 2, 2016, through Dec 31, 2018. The model is evaluated using out-of-sample data from Jan 1, 2019 – Dec 31, 2019. The out-of-sample period-ahead predictions have a weighted-mean-absolute-percentage-error (WMAPE) of about 1.78%, which is substantially less than the 8.0% WMAPE associated with the intraday wind energy forecasts reported by the system operator over the same period.
Book Review: The Mathematics of the Heavens and the Earth: The Early History of Trigonometry

Glen Van Brummelen: The Mathematics of the Heavens and the Earth is published by Princeton University Press and copyrighted, © 2009, by Princeton University Press. All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher, except for reading and browsing via the World Wide Web. Users are not permitted to mount this file on any network servers.
Dynamic power flow-based resonance source location method for the large-scale power electronics-dominated power systems

To mitigate the resonance risk of power electronics-dominated power systems with multi-vendor converters, it is essential to obtain deep insight into the source of the resonances rather than just the system stability conditions. This paper proposed a dynamic power flow-based resonance source location method, which can identify the resonance source preventively using the black-box impedance/admittance models. The definition of the resonance source is firstly presented based on the concept of the dynamic power flow. Then the analytical calculation method of dynamic power flows for critical resonance modes is derived based on the impedance/admittance models of the system. As the required impedance models can be measured by frequency scanning, the proposed resonance source location method can be readily applied to the large-scale power electronics dominated power system, which provides a visualization tool to identify the root causes of various resonances. Case studies from an 800MW offshore wind energy system with a real-life complexity confirm the effectiveness of the proposed source location method.
The Host Canceled My Reservation! Impact of Host Cancelations on Occupancy Rate in the P2P Context: A Signaling Theory Perspective

The business of hosts in peer-to-peer (P2P) accommodation sharing has become an important source of revenue for individuals in many economies. However, there is a dearth of studies on hosts, specifically on the factors that affect host performance (i.e., occupancy rates). Drawing on the signaling theory and the source credibility theory and using a dataset of 41 610 reviews of 7004 Airbnb listings, we investigated the impact of cancelation rate—that conspicuously signals how many times a host has canceled a pre-existing reservation—on the host occupancy rate. Furthermore, we investigate the role of source credibility signals in reducing the impact of host cancelations. The results show that host signals of reputation, responsiveness, and expertise minimize the negative effect of cancelations on the occupancy rate. Theoretically, we advance the academic literature on credibility signals in P2P platforms and their moderating role on host performance. Managerially, the study helps P2P hosts in understanding the role of signals on occupancy rate.
Modal analysis of gravitational instabilities in nearly Keplerian, counter-rotating collisionless discs

We present a modal analysis of instabilities of counter-rotating, self-gravitating collisionless stellar discs, using the recently introduced modified WKB formulation of spiral density waves for collisionless systems by Gulati & Saini. The discs are assumed to be axisymmetric and in coplanar orbits around a massive object at the common centre of the discs. The mass in both discs is assumed to be much smaller than the mass of the central object. For each disc, the disc particles are assumed to be in near circular orbits. The two discs are coupled to each other gravitationally. The perturbed dynamics of the discs evolves on the order of the precession time-scale of the discs, which is much longer than the Keplerian time-scale. We present results for the azimuthal wavenumber m = 1 and 2, for the full range of disc mass ratio between the prograde and retrograde discs. The eigenspectra are in general complex, therefore all eigenmodes are unstable. Eigenfunctions are radially more compact for m = 1 as compared to m = 2. Pattern speed of eigenmodes is always prograde with respect to the more massive disc. The growth rate of unstable modes increases with increasing mass fraction in the retrograde disc, and decreases with m; therefore, m = 1 instability is likely to play the dominant role in the dynamics of such systems.
A NOVEL APPROACH USING A MACHINE LEARNING CLASSIFICATION FRAMEWORK TO IDENTIFY CRITICAL EVENTS IN AN INCREASING AMOUNT OF CONTROL ROOM ALARM MESSAGES

The increasingly more dynamic behaviour of the grid, for example due to the proliferation of Distributed Energy Resources (DER) and Electric Vehicles (EV), necessitates the need to monitor the distribution part of the grid (near) realtime. Therefore, progressively more sensors can be found in that part of the grid. Consequently, much more data, and hence alarm messages will be sent to the DNO/DSO's control centres. Detection of critical events from a vast stream of alarm massages is a time-consuming task when done manually. This may increase the CAIDI metric number as the power outage duration may increase as well. Therefore, a framework was proposed that uses a machine learning supervised classification method to handle the detection of critical events automatically and in less time. Besides detection, the framework itself consist of more parts including data pre-processing and data communication. This framework was tested in a pilot at one of the Dutch DNOs, Stedin, and has demonstrated good performance in terms of precision and recall. This resulted in a system implementation that is currently operationally used. The case study demonstrated the potential of this framework and provided valuable insights in how to make this framework more generic and flexible.
Machine Learning Based Evaluation of Dynamic Events in Medium Voltage Grid Components

Maximizing the utilization of grid components in operation is a major challenge for distribution system operators. In order to achieve this, assessment of the technical conditions of the equipment is required. The dielectric performance of insulation systems can be evaluated by the interpretation of Partial Discharge (PD) signals. Evaluation of the characteristics of these time-resolved signals is of highest importance. Conventional methods have several drawbacks, such as the complexity of the test setup and the settings as well as costs of the systems. Available PD measuring systems are not designed to face future challenges arising from Direct Current (DC)-infrastructure for energy distribution. To apply machine learning (ML) algorithms, typically the complete time signals have to be provided in order to extract features. In the present study, to allow a real-time processing at high signal resolutions, an automatically initializing algorithm was developed to separate noise and deterministic signals from each other. The PD or interference signals are available for extended signal processing such as clustering or classification. The implementation of this technology for Alternating Current (AC) and DC voltage applications was evaluated under practical conditions. Finally, an outlook on future applications and options using algorithms of ML in combination with the presented technology is given.
Accelerating Lifelong Reinforcement Learning via Reshaping Rewards*

The reinforcement learning (RL) problem is typically formalized as the Markov Decision Process (MDP), where an agent interacts with the environment to maximize the long-term expected reward. As an important branch of RL, Lifelong RL requires the agent to consecutively solve a series of tasks modeled as MDPs, each of which is drawn from some distribution. A crucial issue in Lifelong RL is how best to utilize the knowledge from the previous tasks for improving the performance in the current task. As a pioneering work in this field, MaxQInit takes the maximum over action-values learned from previous tasks’ environmental rewards as the initial action-value of the current task. In this way, MaxQInit improves the initial performance in the current task and reduces the sample complexity of learning. However, the rewards obtained in the learning process are usually delayed and sparse, dramatically decreasing the learning efficiency. In this paper, we propose a new method, Shaping Rewards for Lifelong RL (SR-LLRL), to speed up the lifelong learning process by shaping timely and informative rewards for each task. Critically, we construct the Lifetime Reward Shaping (LRS) function based on the knowledge of optimal trajectories collected in previous tasks, providing additional reward information for the current task. Compared with MaxQInit, our method exhibits higher learning efficiency and superior performance in Lifelong RL experiments.
Evolutionary Tensor Train Decomposition for Hyper-Spectral Remote Sensing Images

Hyper-spectral images are widely used for mapping and remote sensing of the Earth's surface. Different tensor decomposition methods have been applied for hyper-spectral image decomposition. In this study we present an evolutionary tensor train (ETT) decomposition. The ETT technique defines a combinatorial optimization model to find an optimal shape for the tensor train (TT) decomposition. The optimization model maximizes the compression ratio of the TT decomposition given an error bound. A genetic algorithm (GA) linked with the TT-SVD algorithm is applied to find the optimal shape. We adopt the ETT for the decomposition of hyper-spectral images and study the performance of the ETT with respect to the error bound. The results demonstrate the effectiveness of the proposed evolutionary tensor shape search for the the decomposition of the hyper-spectral images.
Variability of the Particulate Matter Concentration in the Northern Parts of India Using Low-Cost Sensors

The Indo-Gangetic plains (IGP) suffers from poor air quality and high atmospheric pollution over the years. Various natural and anthropogenic sources are the causes of the poor air quality, affecting the population in the IGP and surrounding areas. The local and long-range transport of dust affects the air quality of the IGP region and sometimes depending upon the favorable meteorological conditions dust reaches the Himalayan foothills. region. We have carried out the analysis of aerosol properties and air quality using a Microtop Sun photometer and low-cost air quality sensors at the IIT Mandi campus (high altitude site) and Dhampur (rural site). The observed $\text{PM}_{2.5}$ and aerosol optical depth (AOD) at IIT Mandi show a strong influence of dust, however, the $\text{PM}_{2.5}$ and AOD are found to be lower compared to the values observed at Dhampur (located 200 km northeast of Delhi). Local industries, dust, and crop residue burning are the main sources of pollution in the rural areas of the northern parts of India. The low-cost sensors provide good quality data from IIT Mandi and Dhampur, our results show the need for a dense network in rural places to understand the dynamics of pollutants, especially in the IGP, one of the polluted regions in India.