Miracle-dz/UniFAR-FTUs huggingface dataset:
Fossil capital: the rise of steam power and the roots of global warming, by Andreas Malm, London and New York, Verso, 2016, 488 pp., £19.99 (paperback), ISBN 13: 978-1-78478-129-3

According to J. Donald Hughes, “the task of environmental history is the study of human relationships through time with the natural communities of which they are a part, in order to explain the processes of change that affect that relationship. ” Easier said than done, perhaps, but environmental history— quite likely better than any other subdiscipline or approach to history—is more perceptive of human interconnections in the world community, or of the interdependence of humans and other living beings on the planet; indeed, environmental history typically supplements and complicates traditional economic, social, and political forms of historical analysis.
Simultaneous Control of Frequency Fluctuation and Battery SOC in a Smart Grid using LFC and EV Controllers based on Optimal MIMO-MPC

This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective modelbased prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.
Research and Implementation of Vector Control Strategy for Centrifugal Actuator

The centrifugal actuator system, driven by the permanent magnet synchronous motor, is able to realize the control of the frequency, phase and amplitude of the actuator output force by adjusting the speed, phase and phase difference of two sets of actuator eccentric wheel drive motors. This paper constructs a control model of centrifugal actuator. In order to reduce the electromagnetic ripple and current ripple during the rotation of the motor, and to increase the torque output of the motor, a vector control strategy is adopted to control the stator current, realizing the control of torque. Modules such as PWM algorithm, PI adjustment algorithm and SVPWM algorithm are designed in FPGA. The system-level modeling tool is utilized to achieve the hybrid simulation. The simulation results show that the integrated vector control strategy developed in this paper accelerates the dynamic response of the motor torque and improves the anti-interference ability of the system.
Coordinated Control Strategy for Improving Frequency Stability of Offshore Wind Farms Connected to the Grid through MMC - HVDC Transmission

Aiming at the inertia support problem of offshore wind power connected to MMC-HVDC transmission, a virtual inertia control strategy based on DC capacitor energy and wind turbine rotor kinetic energy is proposed. By analogy with the inertia response of the synchronous machine, the energy of the DC capacitor is used to provide inertia support. For wind turbines with different wind speeds, a coordinated inertia support control based on differential rotor kinetic energy regulation is designed. The frequency fluctuation is reflected by the change rate of DC voltage to realize the inertia support of the wind farm to the system. Aiming at the primary frequency regulation problem of the receiving-end AC grid, by establishing the coupling relationship between DC voltage and AC grid frequency, a wind farm frequency regulation control strategy based on the measurement information of the DC voltage deviation is designed, and the variable speed control of the wind turbine is used to realize the wind farm actively participates in the primary frequency regulation. Finally, based on the RTLAB OP5600 real-time digital simulation platform, the simulation model of offshore wind farms connected to the grid through MMC-HVDC transmission is built to verify the effectiveness of the proposed control strategy.
Automated CAD System for Intermediate Uveitis Grading Using Optical Coherence Tomography Images

Intermediate uveitis is a major cause of vitritis and can be considered a leading cause of blindness. Clinical records show that accurate detection and hence grading of vitritis will result in a great reduction of blindness rate. This paper proposes an automatic vitritis grading computer aided diagnostic (CAD) system using optical coherence tomography images (OCT), which consists of two stages. The first is a U-net convolutional neural network (U-CNN), which is used to segment the vitreous. The vitreous is very difficult to segment directly from the original OCT due to the high similarity in visual appearance with background tissues. Instead, the U-CNN is based on processing of an input proposed fused image (FI) that integrates the original image, a distance map, and an adaptive appearance map. To assess the vitritis severity, the second stage utilizes the cumulative distribution function of the vitreous intensity as a discriminatory feature for a two-level machine learning classifier with 4 classes (grades 0 – 3). System performance is evaluated on a 200 images dataset. Segmentation stage performance is evidenced by both Dice similarity coefficient of 98.8% and Hausdorff distance of 0.3 µm. Second stage performance is evidenced by the classifier accuracy of 90.5% for the first level and 81% for the second level. These results support using the proposed CAD as an aid to early diagnosis of uveitis.
Distributed Error Estimation for Quasi-Synchronization of Heterogeneous Dynamical Networks

This article proposes a distributed error estimation approach to investigate the quasi-synchronization problem of heterogeneous dynamical networks with static and adaptive coupling laws under a directed communication topology. By introducing a pining control strategy, a novel class of distributed error estimation algorithms is proposed for solving the quasi-synchronization problem of heterogeneous dynamical networks with a static coupling law by exploring the local information among the neighboring nodes. By removing the assumption that the smallest real part of the nonzero eigenvalues of the Laplacian matrix associated with the communication graph must be known, we also address the case of the quasi-synchronization of heterogeneous dynamical networks with nonidentical nonlinear dynamics and an adaptive coupling law. It is proved, in the sense of Lyapunov function, that the distributed error estimation for the quasi-synchronization problem of heterogeneous dynamical networks with static and adaptive coupling laws will be achieved with a bounded synchronization error level. Finally, two examples are presented to demonstrate the effectiveness of the proposed approaches.
Controlled Islanding with Special Consideration of Parallel Power System Restoration Constraints

Intentional Controlled Islanding (ICI) can prevent blackouts by splitting the system into islands following a severe disturbance. Post-islanding events, however, might lead to instabilities that can result into blackouts within one or more islands. Although it is critical to ensure that the islands can be restored in the case of local blackouts, this has not been addressed in the literature. To fill this gap, this paper proposes an ICI method that considers not only the typical ICI constraints, but also Parallel Power System Restoration (PPSR) constraints. The traditional islanding problem for minimal power-flow disruption ensuring the typical generator coherency constraint is extended to include at least one blackstart unit within each island and to exclude various branches from possible solutions. To understand the extent to which each island can be restored, the method quantifies the active and reactive power generation available within each island to determine the maximum load that can be picked up. By applying the proposed ICI method, the restoration process can be facilitated and speeded up. Simulation studies on two IEEE test systems are used to demonstrate the effectiveness of the method in determining an islanding solution that considers PPSR constraints with different network topologies and sizes.
Customized 5G and Beyond Private Networks with Integrated URLLC, eMBB, mMTC, and Positioning for Industrial Verticals

The fifth generation (5G) and beyond networks promote new business opportunities not only for mobile network operators but also for industrial verticals. However, most currently deployed 5G networks are public mobile networks developed for generalized purposes, limiting their adoption for various industrial fields demanding specialized quality of service. The new trend in applying 5G and beyond networks to industrial fields requires a new design paradigm shift calling for customized network structure, network resources, and network management. In customized 5G and beyond private networks (C5GBPN), network structure can be designed to be standalone or integrated; network resources can be designated to be exclusive or shared; and network management, including access control, data control, and governance, needs to be revisited. The flexibility of C5GBPN is of paramount importance for various application scenarios of industrial verticals, such as ultra-reliable low-latency communications, enhanced mobile broadband, massive machine-type communications, and positioning. In this regard, we first present the background and motivation of developing C5GBPN in this article. Then we provide three types of C5GBPN and discuss their pros and cons. Also, we provide feasible solutions to deal with these foreseeable design challenges of C5GBPN.
Lung Cancer Identification via Deep Learning: A Multi-Stage Workflow

Lung cancer diagnosis involves different screening exams concluding with a biopsy. Although it is among the most diagnosed, lung cancer is characterized by a very high mortality rate caused by its aggressive nature. Though a swift identification is essential, the current procedure requires multiple physicians to visually inspect many images, leading to a lengthy analysis time. In this context, to support the radiologists and automate such repetitive processes, Deep Learning (DL) techniques have found their way as helpful diagnosis support tools. With this work, we propose an end-to-end multi-step framework for lung cancer localization within routinely acquired Computed Tomography images. The framework is composed of a first step of lung segmentation, followed by a patch classification model, and ends with a mass segmentation module. Lung segmentation reaches an accuracy of 99.6% even when considerable damages are present, while the patch classifier achieves a sensitivity of 85.48% in identifying patches containing masses. Finally, we evaluate the end-to-end framework for mass segmentation, which proves to be the most challenging task reaching a mean Dice coefficient of 68.56%.
Optimal DC Electric Spring Planning Based on Intelligent Algorithm

The penetration of distributed generations (DGs) and the proportion of DC load promote the development of direct current (DC) distribution network to a certain extent. However, the stability of DC distribution network is influenced due to the intermittence and randomness of DGs. As a demand-side management method, DC electric spring (DCES) can assist energy storage system (ESS) to optimize the operation of DC distribution network. In this paper, a two-layer planning method based on the latest DCES topology is proposed to determine the location and capacity of DCESs, adopting particle swarm optimization (PSO) to solve the issue. The stability and economy of DC distribution network as well as the penetration of DGs are considered comprehensively. The upper level aims to improve the stability of the network while the lower level serves to reduce operational cost and improve allowable penetration of DGs. The effectiveness of the planning method has been verified on IEEE 14-bus system.
Riemannian Metric Learning for Progression Modeling of Longitudinal Datasets

Explicit descriptions of the progression of biomarkers across time usually involve priors on the shapes of the trajectories. To circumvent this limitation, we propose a geometric framework to learn a manifold representation of longitudinal data. Namely, we introduce a family of Riemannian metrics that span a set of curves defined as parallel variations around a main geodesic, and apply that framework to disease progression modeling with a mixed-effects model, where the main geodesic represents the average progression of biomarkers and parallel curves describe the individual trajectories. Learning the metric from the data allows to fit the model to longitudinal datasets and provides few interpretable parameters that characterize both the group-average trajectory and individual progression profiles. Our method outperforms the 56 methods benchmarked in the TADPOLE challenge for cognitive scores prediction.
Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis

Malaria is a major health threat caused by Plasmod-ium parasites that infect the red blood cells. Two predominant types of Plasmodium parasites are Plasmodium vivax (P. vivax) and Plasmodium falciparum (P. falciparum). Diagnosis of malaria typically involves visual microscopy examination of blood smears for malaria parasites. This is a tedious, error-prone visual inspection task requiring microscopy expertise which is often lacking in resource-poor settings. To address these problems, attempts have been made in recent years to automate malaria diagnosis using machine learning approaches. Several challenges need to be met for a machine learning approach to be successful in malaria diagnosis. Microscopy images acquired at different sites often vary in color, contrast, and consistency caused by different smear preparation and staining methods. Moreover, touching and overlapping cells complicate the red blood cell detection process, which can lead to inaccurate blood cell counts and thus incorrect parasitemia calculations. In this work, we propose a red blood cell detection and extraction framework to enable processing and analysis of single cells for follow-up processes like counting infected cells or identifying parasite species in thin blood smears. This framework consists of two modules: a cell detection module and a cell extraction module. The cell detection module trains a modified Channel-wise Feature Pyramid Network for Medicine (CFPNet-M) deep learning network that takes the green channel of the image and the color-deconvolution processed image as inputs, and learns a truncated distance transform image of cell annotations. CFPNet-M is chosen due to its low resource requirements, while the distance transform allows achieving more accurate cell counts for dense cells. Once the cells are detected by the network, the cell extraction module is used to extract single cells from the original image and count the number of cells. Our preliminary results based on 193 patients (including 148 P. Falciparum infected patients, and 45 uninfected patients) show that our framework achieves cell count accuracy of 92.2%.
Early Prediction of Blastocyst Development via Time-Lapse Video Analysis

Advances in assisted reproductive technology allow more and more infertile patients to benefit from in vitro fertilization (IVF) treatment. For an IVF treatment, selecting embryos that have developed into the blastocyst stage is a crucial step for the subsequent embryo transfer. In the clinic, it requires embryologists to keep observing the developmental status of embryos, which not only highly depends on the professional level of reproductive experts but greatly increases patients’ economic cost. To address this problem, we propose a novel task termed early blastocyst development prediction, which aims at predicting the potential that an embryo can develop into the blastocyst stage by partially observing its early development status so as to assist embryologists with early embryo selection. To achieve this goal, we collect a new IVF dataset with 2,898 time-lapse videos of embryo development. Based on this, we also propose a benchmark solution named Attentive Multi-focus Selection Network (AMSNet). Specifically, AMSNet is a deep learning-based time-lapse video analysis method that includes a separate attention mechanism to exploit the features of embryoscope images captured at multiple focal planes and a temporal feature channel shift operation to obtain memory capability over time-lapse videos. Experimental results demonstrate the effectiveness and clinical significance of our proposed AMSNet on our IVF dataset.
Multi-objective Optimization for Optimal Placement and Sizing of DG in Distribution System

Under the background of promoting the development of low-carbon power system, distributed generation (DG) has developed rapidly. In this paper, the DG planning mathematical model considering active power losses, voltage profile and DG cost, is established. Then, manta ray foraging optimization (MRFO) algorithm is applied to obtain the optimal Pareto set. In order to avoid the subjective influence of artificial weighting coefficient, the improved ideal point method is used to make the decision of Pareto optimal solution set and select a more objective compromise solution. Finally, the simulation analysis based on IEEE 33, 69-bus distribution network shows that MRFO can obtain a more widely distributed and uniform Pareto frontier compared with the traditional multi-objective intelligent optimization algorithm.
Grid-Forming Converter in High Penetration of Converter-Interfaced Generation Large-Scale Power System: A Review of Synchronization Stability

The high penetration of electronic and converter-based generation (CBG) is the trend of the future power system. The working mode of converters in realistic large-scale gird nowadays are gird-following mode which needs a synchronous machine to build the reference voltage and the renewable resource feeding the grid. With increasing renewable energy resources, it is important to make the converters work as a voltage source to form the grid which is called the grid-forming control frame work. Although there are some applications of the grid-forming converter in Microgrids, its implementation in a large-scale system is still an issue that is more complicated thus should be discussed. The stability issue of converter connected to the traditional power grid is the key problem that limits the development of renewable energy. This paper reviews this issueandreportstheresearchonthisdecades.
Quantitative Assessment of Degree of Polymerization of Insulating Paper by Optimized Bayesian-SVR Algorithm

Insulation paper is the main insulating material for oil-immersed power (OIP) equipment and the diagnosis of its aging condition is important to maintain the health of OIP equipment. In recent years, researchers have proposed the use of near-infrared spectroscopy (NIRS) as an alternative to the viscosity method for detecting the degree of polymerization (DP) of insulating paper, but the complex mapping relationship between the spectra and DP has led to poor performance of the traditional algorithm. In this paper, an optimized support vector regression (SVR) algorithm is proposed for the first time to model the quantitative analysis of NIRS and DP of insulating paper. We firstly obtained the insulating paper spectral dataset in the laboratory and carried out noise reduction and abnormal sample rejection, then analyzed the effect of the SVR algorithm hyperparameters $\varepsilon,\ \sigma$; C on the model performance, and used the Bayesian optimization algorithm to optimize the SVR hyperparameters, and finally used the SVR algorithm to build an evaluation model and compared it with the partial least squares (PLS) and BP neural network (BPNN) models. The results show that the Bayesian-SVR model performs better on both the training and test sets. The algorithm proposed in this paper significantly improved the current algorithms.
AMD Classification Based on Adversarial Domain Adaptation with Center Loss

In this paper, we present a deep learning approach for automatic categorization of age-related macular degeneration (AMD). Faced with the deficiency of training data, we propose a solution to combine additional data to effectively assist the classification task. During training process, the retinal fundus images from two datasets are mapped into a common feature space with adversarial domain adaptation to reduce domain discrepancy. Moreover, we introduce center loss to increase the intra-class compactness of the extracted features to further improve the classification performance. Experiments are conducted on three public fundus image datasets: STARE, ODIR and iCHALLENGE-AMD (hereinafter referred to as iAMD). Our method outperforms three state-of-the-art classification models as well as other augmentation approaches. The proposed approach provides a general framework to handle the issue of training samples with domain discrepancy.
LONDN-MRI: Adaptive Local Neighborhood-Based Networks for MR Image Reconstruction from Undersampled Data

There has been much interest in machine learning based methods for MR image reconstruction from undersampled k-space data. This paper presents a method for MR image reconstruction based on rapidly fitting neural networks to adaptively estimated neighbor-hoods within a larger training set. The weights of the network are learned only on training examples that are specified in the ‘vicinity’ or local neighborhood of a test reconstruction. The algorithm (dubbed LONDN-MRI) alternates between estimating the neighbors of the reconstructed test image and performing (local) network training and updating the test reconstruction. Rather than attempting to fit a model once to the entire dataset, our proposed method allows for learning models that are more tailored to the input test data, and therefore more flexible to the choice of undersampling patterns or anatomy. It also easily accommodates modifications to training sets. We used the recent MoDL (deep unrolled) network and the FastMRI dataset for testing our approach. We present reconstruction results for fourfold and eightfold undersampling of multi-coil data using 1D variable-density random phase-encode sampling masks. When trained locally, our method yields reconstructions of better quality compared to models learned globally on larger datasets.