Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced-order Modeling and Stability Analysis of MTDC System Under Droop Control in DC Voltage Control Timescale

Dynamic stability of voltage-mode and current-mode multi-terminal DC systems under droop control in DC voltage control timescale has been investigated in this paper, and their corresponding reduced-order models have been proposed in the form of parallel RLC, which is convenient to demonstrate the physical meaning of key control parameters. Employing this model, second-order characteristic equations have been further derived, which makes it easy to study system dynamic stability in an analytical way. The impact of droop gain and DC control loop (DC voltage control or DC current control) parameters on system damping and oscillation frequency has been analyzed in detail. The effectiveness of the proposed reduced-order models has been verified by simulations based on PSCAD/EMTDC.
What Is the Value of Alternative Methods for Estimating Ramping Needs?

Power system operators procure and deploy flexibility reserves or ramping products to address balancing needs caused by uncertainty and variability of load and generation. Existing methods estimate ramping needs using calendar information and historical forecast errors. Novel methods investigate if real-time weather information could inform ramping and other balancing requirements. This paper compares estimation methods for ramping requirements in theory and practice. The theoretical framework indicates when an alternative method could yield improved economic or reliability performance than existing methods by requiring lower or higher levels of ramping products. Preliminary simulations on a 118-bus test system for 4 days in May 2019 illustrate how system performance improves or deteriorates when ramping requirements are weather-informed (alternative) instead of calendar-based (baseline). Preliminary results suggest high variability in change of performance and underline the impact of additional factors, such as system conditions, on the realized performance change.
Online Assessment of Transient Stability of Grid Connected PV Generator With DC Link Voltage and Reactive Power Control

The proliferation of Photovoltaic (PV) generation has brought several changes in the transient stability analysis of power systems. The rotor angle dynamics based stability analysis used for synchronous generators may not be applicable for PV generators. In the existing literature, the transient stability of a grid connected PV generator with dc link voltage and reactive power control (<inline-formula> <tex-math notation="LaTeX">$V_{dc}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> control) is analyzed from the coupled nonlinear functions corresponding to PV control loops, Phase Locked Loop (PLL) and network dynamics. The existing method may not be suitable for online transient stability assessment because it is computationally heavy. This paper proposes an online transient stability criterion for a PV generator (with <inline-formula> <tex-math notation="LaTeX">$V_{dc}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> control) connected to grid modelled as infinite bus. The PV generator is considered to have Low Voltage Ride Through (LVRT) capability, but does not provide voltage support. In this study, the relation between the ac and dc side dynamics is explored. A stability criterion is proposed to assess the transient stability by monitoring the swing in the energy of the dc link capacitor. The proposed criterion requires the dc link voltage trajectory only till the first swing of the dc link voltage. As a result, the proposed assessment criterion is computationally less demanding and less prone to measurement errors in comparison to existing methods. Thus, the proposed criterion is suitable for online transient stability assessment. The effectiveness of the proposed criterion is verified for single-stage as well as two-stage converter configurations of PV generator. The proposed criterion is also verified on a modified IEEE 39 bus system. Time-domain simulations are carried out to validate the proposed criterion. The proposed approach is compared with an existing transient stability criterion for <inline-formula> <tex-math notation="LaTeX">$V_{dc}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> control based grid connected PV generator. The advantages of the proposed method as compared to the existing method are discussed.
Mechanism and solution of passive interference of shared tower to adjacent radio station

The shared tower integrates power and communication equipment, and its new base station antenna makes the passive interference mechanism and level of transmission line to the radio station adjacent to the line change. Therefore, the passive interference mechanism of the shared tower to the station is analyzed. Compared with the traditional transmission line, the shared tower is the combination of the scatterers with discontinuous electricity, and the second-order coupling scattering will occur among the scatterers. The compound scattering field model of the shared tower under the coupling effect is established, and the scattering field under the coupling background is solved by using the equivalent principle and reciprocity theorem, so as to realize the solution of passive interference level. Taking the tower type, incident angle and antenna power of the shared tower as variables, the passive interference levels of stations with different frequencies under three factors are solved respectively, and the change rule of passive interference of the shared tower is obtained. It is suggested that the shared tower should take some passive interference protection measures for the stations adjacent to the base station antenna frequency.
Diabetic Retinopathy Grading Using ResNet Convolutional Neural Network

Designing and developing automated systems to detect and grade Diabetic Retinopathy (DR) is one of the recent research areas in the world of medical image applications since it is considered one of the main causes of total blindness for people who have diabetes in the mid-age. In this paper, a complete pipeline for retinal fundus images processing and analysis has been described, implemented and evaluated. This pipeline has three main stages: (i) image pre-processing, (ii) features extraction and (iii) classification. In the first stage, the image has been pre-processed using different transformations to standardize the images and to enhance the images quality. It has been proven that Gaussian filtering is quite effective in this context to enhance the images contrast. In the second and third stage, the convolution neural network (CNN), one of the best neural network architecture for image analysis applications, has been used. The concept of transfer learning and fine tuning have been advocated in this paper and applied for ResNet18 using the publicly available Kaggle dataset. The problem of DR diagnosis has been handled as a multi-class classification problem where there are five levels of the disease severity (0 – No DR, 1 – Mild, 2 – Moderate, 3 – Severe, 4 – Proliferative DR). The final model has achieved accuracy of 70 %, recall of 50% and specificity of 88% outperforming other models built from scratch with less training time and proving the efficiency of transfer learning in this context. The training process has considered the problem of imbalanced dataset using two different ways and it has been discovered that using imbalanced dataset sampler is a very efficient solution. The final model developed in this research could be used as the main unit for a computer aided system to be hosted online for DR detection and diagnosis.
Mobile Apps Meet the Smart Energy Grid: A Survey on Consumer Engagement and Machine Learning Applications

Consumers lie at the epicenter of smart grids, since their activities account for a large portion of the total energy demand. Therefore, utility companies, governmental agencies, and various other entities with environmental concerns aim at lowering and shaping energy consumption patterns to achieve peak load reduction, load smoothing, and hence carbon emission curtailment. In this survey paper, we present an overview of approaches for engaging smart grid consumers and for providing them with information, motivation, and recommendations for energy efficiency through mobile apps. Our focus is to bring machine learning approaches closer to smart grid mobile apps so as to optimally manage consumer flexibility and enhance energy savings through detailed consumer profiling and modeling, since an increasing amount of energy consumer data is becoming available. A novel survey and analysis of prior work in the area is conducted in order to identify gaps from this perspective. We consider both recent research project outputs and commercial products and we discuss various aspects of the designs, such as state-of-the-art technologies, extrinsic and intrinsic motivation techniques, gamification, consumer profiling, and the role of machine learning and recommender systems in this context. Furthermore, different mobile apps are presented and compared based on the most important features that affect consumer energy efficiency and sustainability, such as data visualization, gamification, flexibility, consumer profiling methods, feedback mechanisms, recommendations, social media, and machine learning integration. The main goal of this work is to identify how mobile apps incorporate these features to engage energy consumers in energy-efficient behavior, assess the current state-of-the-art in the area, and highlight future research directions.
Proximity Law Modelling for Quantifying the Visual Perception by Marked Point Process

The human visual system receives sensory inputs from the environment and converts them into the perception of real objects such as desks, buildings, and cars. This phenomenon is known as visual perception. It is an effortless process which makes sense of the visual information. Recently, the ultimate goal for machine vision researches is to imitate the human visual perception and to understand the intricate data processing done by the human brain. In this paper, we propose a methodology inspired by Gestalt theory of perception to quantify the human visual perception. Our goal is to add a quantitative aspect for the visual perception in order to be easily integrated into the image processing tasks. Tests on synthetic images show good performance on images with Gaussian noise proving its efficiency to detect perceptual groups.
A Improved List Heuristic Scheduling Algorithm for Heterogeneous Computing Systems

When the traditional heterogeneous multi-core scheduling algorithm performs tasks with high resource density, a large amount of idle time often occurs on the processor core. Therefore, based on the environment of heterogeneous multi-core processors, this paper studies the static heuristic table scheduling algorithm, and proposes an optimization approach for the problem of single priority assignment and too simple task assignment. We design optimization in the static heuristic scheduling algorithm list generation phase and task allocation phase, and propose a hybrid task allocation method with three strategies to improve the standby time utilization of processor core. Then, DVFS technology is used to optimize the scheduling results, so that the task can run with lower energy consumption without increasing makespan. Finally, the new algorithm is compared with three traditional scheduling algorithms through design experiments, and it is proved that the new algorithm has better performance when executing more tasks.
Cluster-Boosted Multi-Task Learning Framework for Survival Analysis

Accurately predicting the time to an event of interest is an important problem in a wide range of real-world applications. However, prediction is often difficult because many medical datasets have a large number of unlabeled (“censored”) instances because labeling is costly and time consuming. Survival analysis focuses on labeled data to predict the time to an event of interest, such as time of death, or conversion to a different stage in a progressive disease. Grouping structure, which naturally exists in medical datasets, can be exploited to improve generalization performance by learning multiple related survival prediction tasks for subgroups collaboratively. Thus a multi-task learning framework can connect multiple survival prediction tasks (for different subgroups) and learn them simultaneously. In order to take into account both censored information, as well as discover the grouping structure, we propose a novel cluster-boosted multitask learning framework for survival analysis that boosts survival prediction performance. We develop an efficient algorithm and demonstrate the performance of the proposed cluster-boosted multi-task survival analysis method on The Cancer Genome Atlas (TCGA) dataset. Our results show that the proposed approach can significantly improve prediction performance in survival analysis while also identifying different subgroups of cancer patients.
Data augmentation for multi-organ detection in medical images

We propose a deep learning solution to the problem of object detection in 3D medical images, i.e. the localization and classification of multiple structures. Supervised learning methods require large annotated datasets that are usually difficult to acquire. We thus develop a Cycle Generative Adversarial Network (CycleGAN) and You Only Look Once (YOLO) combined method for data augmentation from one modality to another via CycleGAN and organ detection from generated images via YOLO. This results in a fast and accurate detection with a mean average distance of 7.95 mm for CT modality and 16.18 mm for MRI modality, which is significantly better than detection without data augmentation. We show that the approach compares favorably to state-of-the-art detection methods for medical images on CT data.
Application of digital holographic and fluorescence microscopy for investigation of live cells response to photodynamic treatment using Radachlorin photosensitizer

In this paper we present analysis of living cells response to photodynamic treatment. Intracellular generation of reactive oxygen species was performed by means of Radachlorin photosensitizer. Radachlorin accumulation in cells was controlled using fluorescence microscopy. Digital holographic microscopy was applied for monitoring cells response to photodynamic treatment at different doses. The results obtained can be used for estimation of cells resistivity to intracellularly generated reactive oxygen species.
Multispectral Image Compression Based on HEVC Using Pel-Recursive Inter-Band Prediction

Recent developments in optical sensors enable a wide range of applications for multispectral imaging, e.g., in surveillance, optical sorting, and life-science instrumentation. Increasing spatial and spectral resolution allows creating higher quality products, however, it poses challenges in handling such large amounts of data. Consequently, specialized compression techniques for multispectral images are required. High Efficiency Video Coding (HEVC) is known to be the state of the art in efficiency for both video coding and still image coding. In this paper, we propose a cross-spectral compression scheme for efficiently coding multispectral data based on HEVC. Extending intra picture prediction by a novel inter-band predictor, spectral as well as spatial redundancies can be effectively exploited. Dependencies among the current band and further spectral references are considered jointly by adaptive linear regression modeling. The proposed backward prediction scheme does not require additional side information for decoding. We show that our novel approach is able to outperform state-of-the-art lossy compression techniques in terms of rate-distortion performance. On different data sets, average Bjøntegaard delta rate savings of 82 % and 55 % compared to HEVC and a reference method from literature are achieved, respectively.
Interchangeability of Knowledge and Opinion Integration Strategies in Collective Models

Knowledge diffusion and opinion formation are important research topics in the area of communication in social networks. We present our generic model with interchangeable variants for processing messages from other members of the group. We describe several generic aspects of communication, like learning, persuasion, or polarization, and apply them in our model. We then show how this simple change leads to different results for the communication process of the whole group. We show how the model with interchangeable integration strategies is applicable for modeling various aspects of online social network and real life communication.
Transient Stability Analysis of Offshore Wind With O&G Platforms and an Energy Storage System

The integration of offshore floating wind turbine generation (WTG) with offshore O&G platforms in the off-grid configuration which is a business model that is in the process of developing in the North Sea. As such, an integrated system consisting of an offshore floating WTG and O&G production platforms with onboard battery energy storage system (BESS) is proposed in this paper. In simulation, there are 4 different scenarios and results show that conventional system has high transient stability which do not meet the IEC standards for O&G platforms. The simulation has shown proposed system 2 which has incorporated with BESS 1 and BESS 2, has a reduction in transient deviation that can meets the IEC standards. In addition, capital expenditure (CapEx) and operational expenditure (OpEx) of proposed system 2 is presented in this paper.
Metal removal with two biochars made from municipal organic waste: adsorptive characterization and surface complexation modeling

The adsorptive characteristics of biochar produced from garden green waste (S-char) and a mixture of food waste and garden green waste (FS-char) were investigated. Adsorption of Cu2+, Zn2+, and Mn2+ onto the two biochars reached equilibrium within 48 hours. The metal adsorption was effectively described by the pseudo-second-order kinetic and Freundlich isotherm models which suggest heterogeneous chemisorption. The initial solution pH influenced adsorption of Zn2+ and Mn2+ but not of Cu2+. Simulation via a surface complexation model showed that the fraction of XOCu+ adsorbed onto biochar was increased with increasing pH until it reached the adsorption maximum at pH 8.5, while the endpoint for the maximum of XOMn+ was higher than pH 12.
Toward an End-to-End Solution to Identification of Handheld Pharmaceutical Blister Packages*

Verification of dispensed pharmaceutical packages is of paramount importance to prescription dispensing. Due to lack of identification peripherals like bar codes or RFID tags on blister packages, image-based solutions have been utilized. Earlier two-stage solutions require more resources for implementation and training, in addition to more computational time. In contrast, this paper presents an end-to-end trained solution, called Fast Rotated Occluded Rectangular (Fast ROR) pattern recognition architecture, composed of modules of: rotational rectangular detection, affine transformation, and image recognition. In particular, the features used to localize the package and the features used for identification are the same and extracted by a common feature extractor. As a result, the overall architecture is more compact with only one training set needed and more efficient computation time. Comparison experiments have been conducted on the pro-posed end-to-end FOR and on a representative two-stage HBIN [1] solution: Targeting a pool of 230 types of pharmaceutical packages, 30 paired front and back handheld images for each type were taken and randomly partitioned with 4:1 for training and testing, FOR (vs. HBIN) uses 41.79M (120.32M) network parameters, with a training time of 17 hours (119 hours), and a testing speed of 22.2 fps (10fps). The identification results in terms of F1-score by FOR (vs. HBIN) is 100% (98.67%) in familiar environment, whereas 94.30% (91.27%) when the identification is conducted in new environment.
User-Aided Global Registration Method using Geospatial 3D Data for Large-Scale Mobile Outdoor Augmented Reality

Accurate global camera registration is a key requirement for precise AR visualizations in large-scale outdoor AR applications. Existing approaches mostly use complex image-based registration methods requiring large pre-registered databases of geo-referenced images or point clouds that are hardly applicable to large-scale areas. In this paper, we present a simple yet effective user-aided registration method that utilizes common geospatial 3D data to globally register mobile devices. For this purpose, text-based 3D geospatial data including digital 3D terrain and city models is processed into small-scale 3D meshes and displayed in a live AR view. Via two common mobile touch gestures the generated virtual models can be aligned manually to match the actual perception of the real-world environment. Experimental results show that - combined with a robust local visual-inertial tracking system - this approach enables an efficient and accurate global registration of mobile devices in various environments determining the camera attitude with less than one degree deviation while achieving a high degree of immersion through realistic occlusion behavior.
Permanent Magnet Torque Motor with Unequal Teeth Mix Material

In order to increase the torque density, the permanent magnet (PM) torque motor with unequal teeth mix material is proposed in this paper. It combines the unequal teeth technology with two kinds of silicon steel sheet, where the armature teeth with windings are made by oriented silicon steel sheet, while the left parts of the stator are composed of non-oriented silicon steel sheet. The effects of unequal armature teeth width on winding coefficient and the effect of changing the armature teeth material are analyzed by the analytical method. The finite element simulation results are used to compare the average output torque and overload torque of three types of motors. The results show that the unequal teeth structure mix material motor can increase the torque density and improve overload ability.
Multi-Layered Flat Metamaterial Lenses: Design, Prototyping and Measurements

Multi-Layered flat metamaterial lenses have been proposed as lightweight and thin alternatives to conventional homogeneous dielectric lenses. In this work, we first present a methodology for the refractive index synthesis of flat meta-lens antennas based on a linkage between Geometrical Optics (GO) and Particle Swarm Optimization (PSO). The applied numerical synthesis algorithm, which was previously used to synthesize the inhomogeneity of on-axis fed lenses, is now used to synthesize both on-axis and off-axis fed flat lenses with circular symmetry thus providing conically scanned beams for the off-axis designs. Then, metamaterial elements of variable sizes distributed on planar dielectric substrates are used to form a multi-layered flat metamaterial lens and satisfy the required refractive index distribution. Simulation and near-field/far-field measured results of a proof-of-concept prototype of a multilayered flat lens operating at 13.4 GHz are also presented. Additionally, microwave holographic approach is used to evaluate the goodness of the exit aperture phase.