Miracle-dz/UniFAR-FTUs huggingface dataset:
Rotor Fault Diagnosis in a Hydrogenerator based on the Stator Vibration and the Variational Autoencoder

A non-intrusive method for the detection of the Rotor Inter-Turn Short Circuit and Static Eccentricity (SE) of a large hydrogenerator (74 MVA, 76 poles), by the means of real vibratory stator data, Finite Element Model (FEM) and Deep Learning technique, is presented in this paper. The Variational AutoEncoder is implemented for fault diagnosis: it is trained and validated on healthy vibratory signals. The signature of both faults is obtained from FEM, injected into another set of healthy real vibratory signals, and tested in the VAE. The classification in the latent space is investigated. The results prove the capability of the method in fault classification in a user friendly space, where each colored zone corresponds to a different case.
Design of a Dual DC-Bus Homopolar Generator Implementing a Direct-Cooled Excitation Coil

The drivetrains of large transportation systems, such as aircraft or ships, are evolving towards hybrid turbo-electric solutions with DC primary power distribution, aiming to reduce the related greenhouse gas emissions. Synchronous Homopolar Generators (SHGs) equipped with front-end diode rectifiers are a promising technology for on-board generation units. Following the work presented in previous publications, the design of a 2 kW@6000 rpm concept demonstrator is here refined by means of 3D-FE analysis, considering both no-load and load operation. A direct-cooled solenoid excitation coil provides the full voltage-control capability of two independent 400 V DC-buses supplied via the diode rectifiers. The electromagnetic design choices are focused on the minimisation of the DC-bus voltage ripple. The multi-physics design of the field winding considers both electromagnetic and thermal aspects, which are validated through CFD analysis.
Deep Composite Kernels ELM Based on Spatial Feature Extraction for Hyperspectral Vegetation Image Classification

Vegetation classification has a pivotal role in forest management and ecological research. It is a specific application problem in hyperspectral image classification. However, the existing classification models do not make sufficient use of the spatial features of vegetation, and cannot extract deep feature information. To address these issues, we propose a deep composite kernel extreme learning machine based on spatial feature extraction (DCKELM-SPATIAL) to classify vegetation. Especially, we use the Gabor filter and super-pixel density peak clustering method to obtain a new set of spatial composite kernels. Experiments are carried out on two sets of real hyperspectral vegetation datasets. The results show that this method is superior to some classical and advanced methods in classification accuracy, and satisfactory results are obtained.
Integrative Literature Review: Motivation to Transfer Training: An Integrative Literature Review

Motivation to transfer is essential for the transfer of training. Without motivation, newly acquired knowledge and skills will not be applied at work. The purpose of this integrative literature review is to summarize, critique, and synthesize past transfer motivation research and to offer directions for future investigations. First, seven contributions of past research are presented in an attempt to understand antecedents, correlates, and consequences of motivation to transfer. Second, an alternative view that complements and extends current approaches is discussed, and its implications for future studies investigating employees’ motivation for training application on the job are outlined.
Analysis of fault traps

Unfaulted four-way-dip closures are the simplest and most attractive hydrocarbon traps. A single sealing lithology can provide both top and lateral seals. Seal risks are minimized with unfaulted four-way-dip closures. Unfortunately, most large four-way-drip closures have already been drilled, particularly in mature areas such as the US Gulf Coast. Exceptions may exist in deep water, at great depth, or in areas with significant lateral velocity variations, but for the most part, explorationists working mature areas are looking for either fault traps or stratigraphic traps. This article focuses on extensional fault systems, although many of the observations are applicable to compressional and strike-slip faulting. The following topics are discussed: mapping faults including discussions on the aliasing problem, fault shape, 3-D data, en echelon faults and the coherence cube; a general discussion of fault traps; juxtaposition traps and the use of ``Allan sections;`` and fault-sealing traps and the three mechanisms that cause fault-zone capillary properties to differ from unfaulted rock -- clay smear, grain crushing and diagenesis.
In Situ and Crowd-Sensing Techniques for Monitoring Flows in Ephemeral Streams

Flow monitoring during extreme hydrological events is essential for calibration and validation of hydrological models and for an efficient management of water resources. However, flows are hard to monitor and are therefore hardly measured in some places, especially those most affected by climate change, where floods are increasingly severe, causing great damage to facilities and the general population. In particular, in ephemeral streams, typical of semi-arid climates, flash floods may occur as they have a rapid response to a rainfall event and floods are of a sudden nature. In this article, we introduce an in situ and Mobile crowdsourcing (MCS) monitoring system for efficient real-time flow measurement in ephemeral streams. Our system is deployed in the Mar Menor watershed (Murcia, SE Spain), the first ecosystem in Europe with a legal entity, promoted by the citizens in the face of the environmental crisis that is besetting it.
How to Create the Largest In-Vivo Endoscopic Dataset

In this work, we present a novel approach to generate large amounts of training data for supervised machine learning algorithms. Traditionally, labeling a high quantity of data requires many hours of repetitive manual labor, which can be achieved for instance by crowdsourcing [2]. Recently, there have been efforts to leverage advancements in machine learning in the process of medical image annotation [3]. Our method is a scalable, semi-automated way to generate ground truth annotations for image data. Using the output of Convolutional Neural Networks (CNNs), with U-Net [4] style architectures, trained on a small, manually labeled subset of the data, we generate meaningful annotation proposals that human annotators can verify and adjust in a n-step quality assurance process. This process is visualized in Fig. 1. On average, it is faster to correct the proposals than to annotate the image from scratch, which allows a speed-up by several orders of magnitude compared to the naive approach of pure manual labor. The algorithm’s output can be compared to a first step, coarse annotation by a human that is refined and verified by different annotators (human) in the next steps. Because of this relation, the method does not introduce a new bias, as in every n-step quality assurance process the (i+1)th reviewer is biased by the i-th reviewer. We plan to evaluate an incorporation of the algorithm’s uncertainty maps to guide the annotators’ attention to areas that need special attention. This could reduce the error introduced by a potential bias even compared to full human annotations as a human is not able to express their own uncertainty per pixel. While similar ideas have been investigated before, we found that no significant amount of time has been spent on building the right tooling to fully leverage the potential of algorithmic proposals. Currently, we are implementing tools to refine the proposals in an easier way than before. Annotating images using a polygon approach takes significantly longer Fig. 1. An overview of our approach: 1. Training a CNN on a small amount of manually labeled data. 2. Inference of annotation proposals for the remaining chunk of the data. 3. Verification and correction of the proposals by humans. than it should, because the same outlines have to be traced multiple times and the user has to interact with individual anchor points. We believe that a graph based annotation tool combined with a polygon augmentation tool that partly abstracts the individual polygon anchor points away from the annotator can fix this issue. In general, this approach transfers well to different requirements and tasks. To demonstrate and verify our approach we are currently creating the largest in-vivo endoscopic dataset. The dataset provides 10000 highquality, pixel-wise semantic annotations of endoscopic instruments. The data is based on the MICCAI EndoVis Challenge 2017 [1]. It is intended for researchers in Computer Aided Surgery (CAS) and related fields. For ethical considerations, please refer to the EndoVis Challenge [1].
Meta-learning Accelerated Bi-LSTM for Fiber Nonlinearity Compensation

Fiber nonlinearity is one of the main limitations for long-reach optical fiber transmissions. We propose a meta-learning accelerated Bi-LSTM algorithm for fiber nonlinearity equalization by saving the training time with different launch powers. The proposed algorithm obtains 0.7 dB Q-factor gain compared to DBP and 81.25% complexity reduction.
Markov Chain Monte Carlo Based MIMO Detection with Reduced Complexity

This paper presents a design and implementation of a complexity-efficient MIMO detector based on the Markov Chain Monte Carlo (MCMC) algorithm. First, to simplify the computation of each iteration step in the Gibbs Sampler (GS), we propose a recursive method for bit Log-Likelihood Ratios (LLR) calculation by symbol decoupling based on QR decomposition. Second, we modify the GS for support of specific subset search to complement the indispensable symbols for bit LLRs calculation. Furthermore, we develop an improved MCMC detector with the stalling problem mitigation, which outperforms the traditional MCMC detector in both accuracy and complexity. Experiments in various cases demonstrate the efficiency of the proposed detector with significant complexity reduction.
Deep Reinforcement Learning Based Resource Allocation for Renewable-Powered 5G Base Station

In recent years, mobile edge computing (also called fog computing) has become an increasingly popular way to process delay-sensitive applications on mobile devices. In addition, due to the high costs associated with grid power supply, renewable energy is increasingly being used as a major power supply for mobile edge computing devices. On the other hand, renewable energy's high intermittency and unpredictability makes it very challenging to deliver high quality services to users under the demands of low latency. Accordingly, this paper describes the co-deployment of edge servers at 5G base stations with hybrid energy storage. Moreover, taking into account both the energy and information domains, we design a total service cost metric. It is calculated by weighted sum of grid energy costs, battery depreciation costs and service delay costs. In order to reduce the long-term expenses and to meet renewable portfolio standards, we propose an allocation algorithm based on deep reinforcement learning that allocates resources efficiently. In terms of energy savings and offloading, our strategy have been shown to be effective for 5G base stations, based on numerical results.
Geophysical Model Research and Results

Geophysical models constitute an important component of calibration for nuclear explosion monitoring. We will focus on four major topics: (1) a priori geophysical models, (2) surface wave models, (3) receiver function derived profiles, and (4) stochastic geophysical models. The first, a priori models, can be used to predict a host of geophysical measurements, such as body wave travel times, and can be derived from direct regional studies or even by geophysical analogy. Use of these models is particularly important in aseismic regions or regions without seismic stations, where data of direct measurements might not exist. Lawrence Livermore National Laboratory (LLNL) has developed the Western Eurasia and North Africa (WENA) model which has been evaluated using a number of data sets, including travel times, surface waves, receiver functions, and waveform analysis (Pasyanos et al., 2004). We have joined this model with our Yellow Sea - Korean Peninsula (YSKP) model and the Los Alamos National Laboratory (LANL) East Asia model to construct a model for all of Eurasia and North Africa. Secondly, we continue to improve upon our surface wave model by adding more paths. This has allowed us to expand the region to all of Eurasia and into Africa, increase themore » resolution of our model, and extend results to even shorter periods (7 sec). High-resolution models exist for the Middle East and the YSKP region. The surface wave results can be inverted either alone, or in conjunction with other data, to derive models of the crust and upper mantle structure. We are also using receiver functions, in joint inversions with the surface waves, to produce profiles directly under seismic stations throughout the region. In a collaborative project with Ammon, et al., they have been focusing on stations throughout western Eurasia and North Africa, while we have been focusing on LLNL deployments in the Middle East, including Kuwait, Jordan, and the United Arab Emirates. Finally, we have been exploring methodologies such as Markov Chain Monte Carlo (MCMC) to generate data-driven stochastic models. We have applied this technique to the YSKP region using surface wave dispersion data, body wave travel time data, and receiver functions. « less
Prediction methods and databases within chemoinformatics: emphasis on drugs and drug candidates

MOTIVATION To gather information about available databases and chemoinformatics methods for prediction of properties relevant to the drug discovery and optimization process. RESULTS We present an overview of the most important databases with 2-dimensional and 3-dimensional structural information about drugs and drug candidates, and of databases with relevant properties. Access to experimental data and numerical methods for selecting and utilizing these data is crucial for developing accurate predictive in silico models. Many interesting predictive methods for classifying the suitability of chemical compounds as potential drugs, as well as for predicting their physico-chemical and ADMET properties have been proposed in recent years. These methods are discussed, and some possible future directions in this rapidly developing field are described.
Differentially activated B cells develop regulatory phenotype and show varying immunosuppressive features: a comparative study

Regulatory B lymphocytes (Bregs) are B cells with well-pronounced immunosuppressive properties, allowing them to suppress the activity of effector cells. A broad repertoire of immunosuppressive mechanisms makes Bregs an attractive tool for adoptive cell therapy for diseases associated with excessive activation of immune reactions. Such therapy implies Breg extraction from the patient’s peripheral blood, ex vivo activation and expansion, and further infusion into the patient. At the same time, the utility of Bregs for therapeutic approaches is limited by their small numbers and extremely low survival rate, which is typical for all primary B cell cultures. Therefore, extracting CD19+ cells from the patient’s peripheral blood and specifically activating them ex vivo to make B cells acquire a suppressive phenotype seems to be far more productive. It will allow a much larger number of B cells to be obtained initially, which may significantly increase the likelihood of successful immunosuppression after adoptive Breg transfer. This comparative study focuses on finding ways to efficiently manipulate B cells in vitro to differentiate them into Bregs. We used CD40L, CpG, IL4, IL21, PMA, and ionomycin in various combinations to generate immunosuppressive phenotype in B cells and performed functional assays to test their regulatory capacity. This work shows that treatment of primary B cells using CD40L + CpG + IL21 mix was most effective in terms of induction of functionally active regulatory B lymphocytes with high immunosuppressive capacity ex vivo.
Edge Caching Based on Deep Reinforcement Learning

The use of mobile apps has caused a lot of data to be transmitted repeatedly, which has put a strain on the networks. One solution to this problem is caching, which stores data closer to the user to reduce data transfer and delay. Typically, when designing a caching policy, only a limited set of file features, such as freshness and popularity, are taken into account in the majority of prior studies. Nevertheless, it is crucial to incorporate factors like importance and size since highly valuable files may hold more significance compared to freely accessible ones. In this paper, we modeled caching problem using Semi-Markov Decision Process that considers file features such as popularity, lifetime, size, and importance. Then, we used a Reinforcement Learning algorithm called Double Deep Q-Learning to solve the Semi-Markov Decision Process. Simulation results show that the proposed method outperforms an existing caching method in terms of cache hit rate and total utility in various settings. This method is the first to comprehensively consider all file features, making it more practical for real-world scenarios.
The Method of Increasing the Energy Saving of a Constellation of Small Satellites of a Satellite Network with a Distributed Architecture

The work solves a scientific and practical task - the development of models for the integration of wireless information and energy transmission technology into the infrastructure of a satellite network based on small satellites using algorithms and artificial intelligence tools in conditions of high dynamics of changes in the probabilistic-temporal characteristics of information flows and conflicting requirements for quality of service. A conceptual model of the integration of the technology of wireless transmission of information and energy into the infrastructure of a satellite network based on small satellites is proposed, which provides the necessary infrastructure to support the developed intelligent algorithms for managing information and communication flows. Models and tools have been developed for the analysis and management of resources in a low-orbit satellite system based on a distributed satellite for the transmission of information and energy.
FMCW Radar-Based Human Sitting Posture Detection

Sitting posture is closely related to our health. Poor sitting posture can cause various diseases and harm our physical health. Current methods to detect sitting posture include machine vision, wearable sensors, and pressure sensors. However, these methods have problems with respect to privacy, inconvenience, and cost. In this work, we proposed the use of frequency-modulated continuous wave radar (FMCW) for detecting human sitting posture, which employs wireless signal transmission to enable non-contact detection, protect privacy, and reduce costs. First, the range fast Fourier transform (FFT) and Doppler FFT of the radar’s intermediate frequency (IF) signals are performed to obtain range and Doppler feature information for different sitting postures. Second, to overcome the problem of range FFT bin offset, a single target angle measurement method is proposed to obtain angle features. Subsequently, we constructed various combinations of features to explore the influence of different combinations of features on the detection of posture while sitting. And we used five machine learning algorithms to perform sitting posture detection experiments. Finally, we conducted sedentary experiments in an office setting and provided sitting history records. The experimental results demonstrate that the method we proposed can identify five distinct sitting postures with an average accuracy of 98.07%.
Application of polydopamine coating in polyetheretherketone modification

: Poly-ether-ether-ketone (PEEK), a new type of bone implant, as a substitute for titanium and other traditional bone implants, its clinical application is affected by its surface hydrophobicity and biological inertia. The research on the improvement of osteogenesis and antibacterial activity of PEEK has attracted more and more attention. Polydopamine (PDA) coating, a widely used surface modification coating, is used to modify PEEK, which can not only solve the problem of low surface activity of PEEK, but also maintain the good mechanical properties of PEEK. In this paper, the articles of surface modification of PEEK with polydopamine coating are summarized and reviewed.
Dynamic networks of cortico-muscular interactions in sleep and neurodegenerative disorders

The brain plays central role in regulating physiological systems, including the skeleto-muscular and locomotor system. Studies of cortico-muscular coordination have primarily focused on associations between movement tasks and dynamics of specific brain waves. However, the brain-muscle functional networks of synchronous coordination among brain waves and muscle activity rhythms that underlie locomotor control remain unknown. Here we address the following fundamental questions: what are the structure and dynamics of cortico-muscular networks; whether specific brain waves are main network mediators in locomotor control; how the hierarchical network organization relates to distinct physiological states under autonomic regulation such as wake, sleep, sleep stages; and how network dynamics are altered with neurodegenerative disorders. We study the interactions between all physiologically relevant brain waves across cortical locations with distinct rhythms in leg and chin muscle activity in healthy and Parkinson’s disease (PD) subjects. Utilizing Network Physiology framework and time delay stability approach, we find that 1) each physiological state is characterized by a unique network of cortico-muscular interactions with specific hierarchical organization and profile of links strength; 2) particular brain waves play role as main mediators in cortico-muscular interactions during each state; 3) PD leads to muscle-specific breakdown of cortico-muscular networks, altering the sleep-stage stratification pattern in network connectivity and links strength. In healthy subjects cortico-muscular networks exhibit a pronounced stratification with stronger links during wake and light sleep, and weaker links during REM and deep sleep. In contrast, network interactions reorganize in PD with decline in connectivity and links strength during wake and non-REM sleep, and increase during REM, leading to markedly different stratification with gradual decline in network links strength from wake to REM, light and deep sleep. Further, we find that wake and sleep stages are characterized by specific links strength profiles, which are altered with PD, indicating disruption in the synchronous activity and network communication among brain waves and muscle rhythms. Our findings demonstrate the presence of previously unrecognized functional networks and basic principles of brain control of locomotion, with potential clinical implications for novel network-based biomarkers for early detection of Parkinson’s and neurodegenerative disorders, movement, and sleep disorders.