Miracle-dz/UniFAR-FTUs huggingface dataset:
A distributed lightning fast maritime anomaly detection service

For applications such as navigation and others, timeliness is a top priority; making the right decision steering a vessel away from danger, is only useful if it is a decision made in due time. Effectiveness of such time critical computing systems is dependent not only on the accuracy of the result produced, but also on the time in which it was computed. In this paper, we present a distributed architecture capable of detecting possible collisions, groundings, unexpected communication gaps and travel pattern deviations of ships fitted with Automatic Identification System transponders. Our results demonstrate that the proposed system can achieve good accuracy and sub second latency in real world conditions.
Varying Speed Rate Controller for Human–Robot Teleoperation Based on Muscle Electrical Signals

With the emergence of industrial robots in recent decades, advancing the technology that drives human-robot interaction has become a research hotspot. However, the control of human-robot interaction using human gestures has presented some challenges when applied to the industrial environment. For example, when a robot is controlled with a hand motion, the hand movement space may be smaller than the robot’s working space, making it difficult to meet the robot’s space and accuracy requirements. The research herein was centered on building interactive scenarios through unit software, and gesture recognition sensors were used to collect spatial information about the hand. A position mapping algorithm was used to map the position of the hand to the robot end-effector and a virtual robot space constraint was added to collectively allow users to control the robot’s movements. In the experiment, volunteers were asked to control the robot by using constant speed ratio (Ratio of robot speed to input value) or changing speed ratio. The speed ratio affected the stability of the experiment, adding electromyography can improve the stability. The first innovation was the use of human muscle information as input information, adding it to the interaction based on unity. The second innovation was using an algorithm to change the mapping speed ratio according to the electromyography information provided. This was the first time that electromyography was used to change the mapping speed ratio of human-robot interactions, which may enable further advancements in human-robot interaction.
A Hybrid Engine for Clinical Information Extraction from Radiology Reports

Clinical researches and practitioners require data extracted from CT scan reports but most of them are in unstructured data format, which are not ready to analysis. Furthermore, a lag of annotated data makes data extraction more difficult to apply natural language processing techniques to convert unstructured data to be structured data. This study is therefore conducted to apply an automated engine employing topic modeling combined with lexicon and syntactic rule-based approach to extract clinical information from CT scan reports. This prototype shows promising results for constructing clinical datasets for further clinical researches.
High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies

As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids, and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks. In this tutorial paper, we review the various application scenarios, fundamental performance limits, and potential technical solutions for high-reliability and low-latency (HRLL) wireless IoT networks. We discuss physical, MAC (medium access control), and network layers of wireless IoT networks, which all have significant impacts on latency and reliability. For the physical layer, we discuss the fundamental information-theoretic limits for HRLL communications, and then we also introduce a frame structure and preamble design for HRLL communications. Then practical channel codes with finite block length are reviewed. For the MAC layer, we first discuss optimized spectrum and power resource management schemes and then recently proposed grant-free schemes are discussed. For the network layer, we discuss the optimized network structure (traffic dispersion and network densification), the optimal traffic allocation schemes and network coding schemes to minimize latency.
Development of an intelligent and distributed low-cost platform for marine observations

In this work a novel intelligent and distributed low-cost platform for marine observation is introduced. The observatory consists of an autonomous surface vehicle (ASV) and an autonomous underwater vehicle (AUV). This paper is focusing on potential search strategies for such an observatory. A novel search strategy based on Braitenberg search is introduced and the performance of this strategy is compared with the performance of inertia Levy-flight developed in recent work. Different settings of search algorithms are tested and their performance is evaluated using a computer simulation. It is shown that the combination of an ASV performing a pre-programmed search and an AUV performing inertia Levy-flight outperform a single AUV using inertia Levy flight for approximately 95 % of the chosen SGD positions. Both settings clearly outperform the introduced Braitenberg search.
Characterizing Dynamic Load Balancing in Cloud Environments Using Virtual Machine Deployment Models

The ever growing computational demands of users call for efficient cloud resource management to avoid service-level agreement (SLA) violation. Virtualization co-locates multiple virtual machines (VMs) on a single physical server to share the underlying resources for efficient resource management. However, the decision about “what” and “where” to place workloads significantly impacts performance of hosted workloads. Existing cloud schedulers consider a single resource (RAM) to co-locate workloads that as a result lead to SLA violation due to non-optimal VM placement. To handle this issue, current study has updated nova scheduler to propose a multi-resource based VM placement approach to improve application performance in terms of central processing unit (CPU) utilization and execution time. Experimentally we have shown that our proposed method has lessened application execution time by 50% when compared with one of the well-known technique.
Estimation of Doubly Spread Underwater Acoustic Channel via Gram-Schmidt Matching Pursuit

The underwater acoustic channel (UAC) exhibits strongly time delay and Doppler (DD) spread especially when the UAC is rapidly time-varying. These dynamic factors result to a serious impact on communication performance such as Inter-Symbol Interference (ISI). Hence, estimation of complex amplitude, time delay and the Dopplers of the UAC becomes the key part in underwater acoustic communication and is hopeful for improving the performance of equalization. However, the estimation is challenged by multiple factors to be estimated in delay and Doppler dimensions. This study exploits the sparsity of the UAC and develops an estimator via using Gram-Schmidt to find orthogonal bases, which leads to the fast and orthogonal way to select the supports of the dictionaries. The support list of the dictionaries constructed by probe signal can be used for estimating the DD functions from a noisy received signal. Matching Pursuit (MP) and Least Square (LS) methods are used for comparisons. The effectiveness of the proposed method is verified by the experimental data.
Modeling of Multiterminal HVDC Offshore Grids with Renewable Energy and Storage Integration by Opensource Tools

With increasing offshore wind penetration levels, more secure and flexible offshore electrical transmission systems are needed to ensure security of supply to onshore users. When considering long distances of electrical power transmission, HVDC grids based on the modular multilevel converter (MMC) are a solution for present and future large scale offshore wind integration. As HVDC grids are developing into large and complex systems, dynamic analyses are useful to gain knowledge on the interactions between the different components in the grids. This paper uses the open source tool OpenModelica as modeling environment to demonstrate the potential of the tool in modeling such HVDC systems. A three-terminal HVDC system with offshore wind and energy storage integration is implemented in OpenModelica, and the dynamics of the system are investigated through four simulation cases. The main focus of this paper is dynamics and operation related to the HVDC system. The simulation results show that the MMCs can improve the most rapid power fluctuations using its internal storage capabilities, while an external energy storage system provides a more constant power flow over more extensive periods, in addition to improving the operation of the HVDC system. All models used in this paper are made publicly available to anyone for any purpose, including future studies of larger HVDC grids.
A Deconvolution Approach for Degradation Modeling With Measurement Error

Degradation trajectories over time provide information that is important for the life estimation of products and systems. However, most of the time the degradation measurements are disturbed by different conditions that cause uncertainty. This is an important problem in the area of reliability assessment based on degradation data, because the multiple observed measurements characterize the degradation path, which ends defining a failure time. Thus, in the presence of measurement error the observed failure time may be different from the true failure time. As the measurement error is inherent to the degradation testing, it results important to establish models that allow to obtain the true degradation from the observed degradation and some measurement error. In this article, a modeling approach to assess reliability under measurement error is proposed. It is considered that the true degradation is obtained by deconvoluting the observed degradation and the measurement error. We considered the inverse Gaussian and Wiener processes to describe the observed degradation of a particular case study. Then, the obtaining of the true degradation is performed by developing the proposed deconvolution method which considers that the measurement error follows a Gaussian distribution. An illustrative example is presented to implement the proposed modeling, and some important insights are provided about the reliability assessment.
State-of-the-Art of Standalone Accurate AUV Positioning - Application to High Resolution Bathymetric Surveys

Autonomous Underwater Vehicles (AUVs) represent a very promising tool for high resolution bathymetric surveys. In this context, this article presents a state-of-the-art of precise underwater navigation. While classical acoustic underwater positioning systems (USBL, LBL) provide a sufficient level of precision, they suffer from deployment difficulties: beacons immersion, need for support vessel, etc. Therefore these methods are not further developed in this article and a conventional underwater navigation method consists in inertial navigation. However, position accuracy of inertial systems drifts over time, making it incompatible with bathymetric survey quality requirements for long missions. To limit this drift, external speed sensors (a Doppler Velocity Log -DVL- most of the time) are used: this is called Aided Inertial Navigation System (AINS). AINS performances depend on sensor performances, data fusion algorithms and mission pattern followed by the AUV. These considerations are presented in this paper and illustrated by two sea trials conducted in the bay of Brest (France). Finally, Terrain Based Navigation (TBN), Simultaneous Localization And Mapping (SLAM) and Synthetic Aperture Sonar (SAS) micronavigation are presented as emerging methods that could help to reduce or bound the position uncertainty drift.
Dietary and Reproductive Risk Factors for Breast Cancer in the Nurses' Health Studies

Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer death among US women. Several hormonal, anthropometric, lifestyle, and genetic factors are known to be associated with breast cancer, though these associations may differ by menopausal status and molecular subtype. Studying risk factors in relation to these subtypes can help enhance our understanding of breast cancer etiology. Here, we aim to further explore the mechanisms through which several established and suspected risk factors may influence risk of breast cancer, with an emphasis on modifiable exposures, which may have direct implications for prevention strategies, particularly for premenopausal and non-luminal breast cancers. In Chapter 1, we evaluated the cross-sectional relationship between intakes of caffeine, coffee, decaffeinated coffee, and tea, and comprehensive profiles of urinary estrogens and estrogen metabolites in premenopausal women. We found significant associations between coffee intake and metabolites in the 2-hydroxylation pathway, suggesting a possible mechanism through which coffee may affect breast cancer risk. In Chapter 2, we examined associations between premenopausal plasma carotenoid levels and markers of oxidative stress and subsequent breast cancer risk. In contrast to previously published analyses, which largely focused on postmenopausal carotenoid exposure, we did not find significant inverse associations between circulating carotenoids and risk; additionally we did not observe positive associations between fluorescent oxidation products and risk. However, we did find some evidence that the effects of carotenoids on risk may be modified by single nucleotide polymorphisms in genes related to
Using Sentinel-2 Image Time Series to map the State of Victoria, Australia

Sentinel-2 satellites are now acquiring images of the entire Earth every five days from 10 to 60 m spatial resolution. The supervised classification of this new optical image time series allows the operational production of accurate land cover maps over large areas. In this paper, we investigate the use of one year of Sentinel-2 data to map the state of Victoria in Australia. In particular, we produce two land cover maps using the most established and advanced algorithms in time series classification: Random Forest (RF) and Temporal Convolutional Neural Network (TempCNN). To our knowledge, these are the first land cover maps at 10 m spatial resolution for an Australian state.
Influence of Ambient Pressure on Performance of a Deep-sea Hydraulic Manipulator

Deep-sea hydraulic manipulator is the most commonly used operation equipment for executing subsea operations in different applications. The increasing demands on underwater operational ease require the implementation of manipulator system which can maintain similar dynamic performance in different working conditions Considering the variations of seawater properties at different ocean depths, especially high hydrostatic pressure in deep-sea environment will make the kinematic viscosity of oil obviously increased, which has a significant influence on the performance of deep-sea hydraulic manipulator. The viscosity-pressure characteristics of working medium is tested. A detailed nonlinear mathematical model and related simulations considering the significant slender pipelines between valves and actuators due to the increased oil viscosity are conducted to analyze the ambient pressures against varying depths affecting the manipulator performance. The 115MPa online pressure experimental results indicate the joint response characteristics at different ambient pressures, which provides basis for response consistency control of deep-sea hydraulic manipulator.
Sealing performance analysis of deep-sea sediment sampler

A portable sediment pressure sampler mounted on ROV was designed for 11,000m sediment sampling with a pressure retention of 110 MPa. Moreover, seal is the key to maintain sampler pressure. This paper compares the sealing performance of the seals used in the deep-sea sediment pressure-holding sampler, and used the finite element analysis method to simulate the sealing performance of four different sealing ring types. The pre-tightening performance of four types of sealing rings under different radial compression ratios was discussed. The sealing contact stress, friction force and Von-Mises stress of four types of sealing rings under 11,000m superhigh pressure were analyzed.
Application-Based Discussion of Verified Simulations of Interval Enclosure Techniques

Dynamical systems are often subject to uncertainties, whether it be parameter uncertainties or the interpretation of state dependencies in quasi-linear state-space representations as some kind of time-varying uncertain parameters. The first group of uncertainties arises from mathematical model simplifications, manufacturing tolerances, and imperfect measurements. Here, uncertainties can be represented in different forms like probability distributions in the stochastic case or interval representations in a bounded error framework. On the one hand, there exist numerous techniques to handle stochastic uncertainty, for example Monte-Carlo methods, but those do not allow for the computation of worst-case bounds of the sets of reachable states. On the other hand, approaches based on interval analysis are capable of the latter aspect. This paper deals with those methods from the perspective of an application scenario in the form of a guaranteed robust stabilization of an inverted pendulum by using constant controller gains. Here, we assume an interval representation for the bounded influence of state dependencies in the system matrices by a polytopic uncertainty model. When dealing with interval uncertainty, the rigorous computation of guaranteed state enclosures is a difficult task. Due to conservatism and/or the wrapping effect, overestimation is a common problem. This paper discusses different approaches to perform a verified reachability analysis by means of interval enclosures to be interfaced with the controller parameterization and gives guidelines on which available technique to use in a real-life robust control task.
Robust $\mathscr{H}_{2}$ observer design for actuator degradation: Application to suspension system

The main contribution of this paper is a robust $\mathscr{H}_{2}$ observer design to estimate the actuator degradation under the influence of disturbances and parametric uncertainty. In this design, the observer conception, based on the $\mathscr{H}_{2}$ frequency-shaping filter and theorem of Parseval, allows the estimation of degradation in polynomial form while coping with system uncertainty. Moreover, the usage of the proposed method also relaxes the conditions for unknown input observer (UIO) or the min-max optimization in linear matrix inequality (LMI) for the disturbance attenuation, which facilitates the robust observer design by engineers. Finally, an application to suspension system is presented to illustrate the performance of the proposed method.
Towards real-time underwater photogrammetry for subsea metrology applications

High accuracy underwater inspections are getting more and more important in the underwater industry where time and cost optimization represent nowadays the main innovation drivers. The subsea industry is undergoing a digital transformation process and for this reason, methods that can provide real-time accurate 3D digital measurements are increasingly demanded. This paper provides a short review of the main techniques currently used in subsea metrology to then present an experimental study carried out to evaluate the accuracy potential of three vision-based techniques well-known in photogrammetry, namely visual odometry with and without windowed bundle adjustment, and keyframe based simultaneous localization and mapping (SLAM). The accuracy evaluation is done using an ORUS 3D® subsea photogrammetry system using a certified 3D underwater reference test-field available at COMEX facilities, whose spatial coordinates are known with sub-millimetre accuracy. A critical assessment of results is presented against currently set tolerances for the subsea metrology industry.
Plans for Planar: Phase-Noise Reduction Techniques in Voltage-Controlled Oscillators

To ensure spectral purity in modern RF signal sources, microwave oscillators and voltage-controlled oscillators (VCOs) must be designed to perform with low phase noise. Despite the growth of monolithic microwave integrated circuits (MMICs), microstrip planar technology is still used for developing low-phase-noise VCOs due to its design simplicity and low manufacturing cost. This article reviews the attributes of low-phase-noise VCOs and some recent techniques applied for phase-noise reduction in microstrip planar technology. The major challenge in designing VCOs in microstrip planar technology lies in improving the quality factor (Q) of resonators. Different methods to improve Q are presented here for techniques incorporated with microstrip lines and substrate integrated waveguides (SIWs). The tradeoff involved with the tuning range (TR) of VCOs is also emphasized to introduce the design selectivity for wideband applications.
A New Approach for Supervised Power Disaggregation by Using a Denoising Autoencoder and Recurrent LSTM Network

Non-intrusive load monitoring (NILM) is a task of estimating the contribution of individual appliance to the overall power consumption by using a set of electrical signals measured by a smart meter. In this paper, we propose a comprehensive and extensible framework based on DNNs. We employ denoising autoencoder (dAE) to reconstruct the power signal of individual appliance from aggregated power consumption, and we use long short term memory (LSTM) network to make sure which appliance the power signal belongs to. We select 5 appliances to validate our method, and the results have shown the advantages of the proposed framework in some aspects compared to hidden Markov models (HMMs) and premier dAE.
Fault tolerance analysis of a multirotor with 6DOF

Several works have addressed the issue of obtaining a six degree of freedom (6DOF) capable multirotor. However, the capability of keeping full control in the case of a total failure in one of the rotors has not yet been analyzed. In this work, it is proven that eight is the minimum number of rotors necessary to achive 6DOF control of a multirotor, in the case of a total failure in one of them. Also, an octorotor configuration with 6DOF control capabilities in a failure situation is proposed and analyzed through numerical simulations, comparing its flight performance in a nominal and a failure case.
Weld Segmentation and Defect Detection in Radiographic Images of Pipe Welds

This article dwells upon the process of the automated weld defect detection after segmentation of the weld area in radiographic images. The current software lacks automatic image processing. This paper proposes the algorithm for fully automatic weld segmentation and defect detection. The deep convolutional neural network FgSegNet is applied to weld segmentation while the neural network, consisting of 3 convolutional layers, is applied to defect detection. The experiments show that the prediction accuracy of FgSegNet is 0.9929 and the prediction accuracy of the convolutional neural network for defect detection is 0.8429 by the binary accuracy metric. The next step of automatic system development will be the improvement of accuracy of the convolutional neural network for defect detection and convolutional neural network design for defect classification.