The energy system decarbonization leads to a decentralization of generation and flexibility. Among the new concepts to integrate the distributed flexibility are local energy markets. While a broad range of research investigates local energy markets on a distribution grid level in detail, research on their system impact has been limited. Therefore, we develop a framework to investigate different configurations of local markets and operation of distributed energy resources and their interaction with the wholesale markets. This framework is applied to a pan-European case study with detailed bottom-up modeling of local energy markets in France and Germany. Additionally, transmission grid simulations are carried out to investigate the impact of local trading on grid congestions. Our results show that a direct coordination of distributed flexibility within the wholesale market is more efficient than a preference of local trading. Nonetheless, local energy markets can be used as an incentive for expansion of distributed flexibility as well as a way to break down the complexity of coordinating millions of distributed assets in a single market. Additionally, our results show that local trading relieves the transmission grid.

The solenoid has been used extensively in a wide range of electrical applications. In the present work, an analytical model is presented for estimating the 3-dimensional (3D) magneto quasi-static fields of the cylindrical solenoid with helix-shaped (helical) wire winding. The proposed integral formulation is based on the Schur (Hadamard) vector product of the current vector I and the helical tangent vector T, to express the magnetic vector potential A. The model is used to estimate the magnetic flux density vector B and the flux linkage Psi anywhere in 3D space, excluding the (source) regions of conducting wire. A constant scaling coefficient, based on measured terminal inductance, is proposed for calibration of the model’s magnetic field. Error analysis is presented on the numerical integration, based on truncated series expansion of complete elliptic integrals in terms of Chebychev polynomials. The model may be used to estimate all three cylindrical components of the magneto quasi-static fields of solenoids as a function of core diameter, wire radius, the number of wire turns per winding layer, the number of winding layers, and the complex permeability of frequency-dependent linear material. Several numerical examples are provided to validate the helical model against the superposition of circular loops, and an idealized circuit model.

Multi-Channel Man-in-the-Middle (MitM) attacks are special MitM attacks capable of manipulating encrypted wireless frames between two legitimate endpoints. Since its inception in 2014, attackers have been targeting Wi-Fi networks to perform different attacks, such as cipher downgrades, denial of service, key reinstallation attacks (KRACK) in 2017, and recently FragAttacks in 2021, which widely impacted millions of Wi-Fi devices, especially IoT devices. To the best of our knowledge, there are no studies in the literature that holistically review the different types of Multi-Channel MitM enabled attacks and analyze their potential impact. To this end, we evaluate the capabilities of Multi-Channel MitM and review every reported attack in the state of the art. We examine practical issues that hamper the total adoption of protection mechanisms, i.e., security patches and Protected Management Frames (PMF), and review available defense mechanisms in confronting the Multi-Channel MitM enabled attacks in the IoT context. Finally, we highlight the potential research problems and identify future research lines in this field.

This paper outlines the design and testing process of the hull of a deep small Autonomous Underwater Vehicle (AUV), rated at 2000m depth. Many existing AUV pressure housings use aluminum or other isotropic traditional metals, instead of composites due to the complexities of the design of composites at such big load. The research at hand explains the process of design starting from setting the geometrical constraints for the design to mass production. To the best of the authors’ knowledge, none of the previous studies has presented such detailed description of the work. Carbon fiber reinforced epoxy material was chosen thanks to its high strength-to-weight ratio and similarity of its compressibility to sea water. Material characterization was performed to obtain the material properties under loading conditions using a modified method of the Combined Loading Compression testing technique. A specific fixture was designed and manufactured to test filament-wound tubes. An analytical model was developed using MATLAB, a finite element model was created using ABAQUS, and the results of the two models were compared. A set of recommendations was introduced for the stacking sequence to provide the lowest possible stresses, regardless on the diving depth of the vehicle. Afterwards, a quality control set of tests was conducted, including seawater absorption under high pressure and void analysis using destructive and non-destructive tests. Pilot samples were manufactured and tested in a pressure vessel, where it was cycle-tested and inspected using visual and ultrasonic testing. Other samples were fail-tested and showed a failure at ∼93% of the expected failure load. Such range can be considered good to provide safe operation for the vehicle at the designated depth, given that the factor of safety included covers more than 7% of the failure load. The proposed design methodology has shown that CFRE can be safely used even at such high depths.

This paper reports on a novel and general solution using machine learning (ML) for stabilization of a many-input, many-output system, which can learn to correlate patterns while the system is unstable or in operation. We apply this to coherent laser combination systems, to stabilize against optical phase drift. In this application, the ML algorithm finds correspondence between output interference patterns and phase errors, in both temporal pulse combination and spatial beam combination, and can be scaled to large numbers (order 100) of beams or pulses. This is significant because our approach enables ML to learn and deterministically correct phase drift in a high-channel-count, low repetition rate system designed for high energy, where stochastic, single-output control algorithms (e.g. SPGD) become too slow. The advantage of our approach is that the amount of information scales with the number of channels, enabling high-bandwidth control of arbitrarily high channel count coherent combination, providing for lasers with high energy and power. The approach presented in this paper has been experimentally demonstrated in a diffractive, 8-beam combination system by our group, with less than one percent stability, Opt. Express 30, 12639-12653 (2022). This manuscript expands on that work by exploring the characteristics of the algorithm in simulations, analyzing its combining efficiency and stability, and generalizing the approach to different types of coherently combined lasers with different learned patterns. This manuscript covers the ongoing progress of using ML as a deterministic error detector, and contributes to current trends of advanced understanding and novel application in photonic systems

Millimeter-wave (mm-wave) frequency modulated continuous wave (FMCW) radars are increasingly being deployed for scenario perception in various applications. It is expected that the mutual interference between such radars will soon become a significant problem. Therefore, to maintain the reliability of the radar measurements, there must be procedures in place to mitigate this interference. This paper proposes a novel interference mitigation technique that utilizes the pulse compression principle for interference compression and mitigation. The interference in the received time-domain signal is compressed using an estimated matched filter. Afterward, the compressed interference is discarded, and the signal is repaired in the pulse-compressed domain using an autoregressive (AR) model. Since the interference spans fewer samples after compression, the signal can be restored more accurately in the compressed domain. Real outdoor measurements show that the interference is effectively suppressed down to the noise floor using the proposed scheme.

This paper addresses the transient stability of grid-forming (GFM) inverters when transitioning from the islanded to grid-connected mode. It is revealed that the reconnection of the GFM inverters to the main grid can be equivalent to a step change of the active power reference, whose impact is closely related with the local load, active power reference of GFM inverter, and short-circuit ratio (SCR) of the grid. Such equivalent disturbance may cause GFM inverters lose the synchronism with the grid. To avoid loss of synchronization, the existence of equilibria of GFM inverter after reconnecting it with the grid is examined, considering the varying SCR. Then, the parametric effects of power controllers on the transient stability are characterized by using phase portraits, which shed clear insights into the controller design for reliably reconnecting GFM inverters with grid. Lastly, all the theoretical findings are confirmed by experimental tests.

Simulating such events typically involves a system of differential equations representing the overall generation and load present at the time. The standard model based on the swing equation assumes unlimited capacity in aggregated resources, as well as the availability of these services for the duration of the frequency excursion. In simulating the effect of outages on the GB Grid frequency on 2019/8/9, the effect of limiting these services to the capacity of resources engaged during the event is examined. It is shown that by taking these refinements into account the timing and extent of the frequency nadir can be successfully estimated. Insight is gained into the responses of various characteristics of the grid and how they interact with unplanned generation imbalances. Using this adapted model, further events on the GB grid are examined to validate the influence of these features.

A document by Weixuan XIAO . Click on the document to view its contents.

A design for a highly reconfigurable intelligent surface (RIS) that can provide three-dimensional passive reflective beamforming suitable for sub-6 GHz frequency bands is proposed. The RIS is based on a novel double-layer structure consisting of independently controllable and highly reconfigurable elements on top of a ground plane. The novelty of the structure is the use of reconfigurable RIS elements, whose size is of the order of a wavelength, that are optimized to function over wide incident and reflected angles. In addition, the individual elements each have four RF switches, in a 5x5 sub-element structure, providing 16 different reflective phases, from 0 to 360 degrees relative to the incident waves. To quantify the performance of the RIS, the performance metric of phase entropy is defined and it is also utilized as an objective function for optimizing the design. An efficient analytical method for predicting the reflected waves from the RIS elements, as well as phase entropy, is also provided. For verification, a prototype of a 4x4-element RIS, with a total size of 0.32x0.34 m2, is fabricated and an experimental setup for measuring the scattered pattern is described. It is shown that waves incident from various angles can be passively beam steered to any angle within 0-360 degrees azimuth and 70 degrees elevation angles. The high reconfigurability to manipulate incident waves makes the proposed RIS a promising surface for use in future high-capacity communication systems.

Despite the advancement in deep learning-based semantic segmentation methods, which have achieved accuracy levels of field experts in many computer vision applications, the same general approaches may frequently fail in 3D medical image segmentation due to complex tissue structures, noisy acquisition, disease-related pathologies, as well as the lack of sufficiently large datasets with associated annotations. For expeditious diagnosis and quantitative image analysis in large-scale clinical trials, there is a compelling need to predict segmentation quality without ground truth. In this paper, we propose a deep learning framework to locate erroneous regions on the boundary surfaces of segmented objects for quality control and assessment of segmentation. A Convolutional Neural Network (CNN) is explored to learn the boundary related image features of multi-objects that can be used to identify location-specific inaccurate segmentation. The predicted error locations can facilitate efficient user interaction for interactive image segmentation (IIS). We evaluated the proposed method on two data sets: Osteoarthritis Initiative (OAI) 3D knee MRI and 3D calf muscle MRI. The average sensitivity scores of $0.96\pm0.00$ and $0.96\pm0.02$, and the average positive predictive values of $0.87\pm0.01$ and $0.93\pm0.03$ were achieved, respectively, for erroneous surface region detection of knee cartilage segmentation and calf muscle segmentation. Our experiment demonstrated promising performance of the proposed method for segmentation quality assessment by automated detection of erroneous surface regions in medical images.

Satellite-aerial-ground integrated network (SAGIN) has been widely envisioned as a promising network architecture for 6G. In the SAGINs, high altitude platform (HAP)-aided relaying satellite systems using hybrid free-space optics (FSO)/radio-frequency (RF) communications have recently attracted research efforts worldwide. Nevertheless, the main drawback of hybrid FSO/RF systems is the restricted bandwidth of the RF connection, especially when the FSO one is blocked by cloud coverage. This paper explores a novel solution for the hybrid FSO/RF HAP-based SAGIN under the impact of weather and atmospheric conditions. Specifically, an additional unmanned aerial vehicle (UAV) is deployed to diverse the FSO link from the HAP-to-ground station to avoid cloud blockage while maintaining a high-speed connection of the FSO link. A mirror array constructed by re-configurable intelligent surface (RIS), an emerging technology, is mounted on the UAV to reflect the signals from the HAP. The channel model of RIS-UAV takes into account both atmospheric turbulence and hovering-induced pointing errors. Furthermore, we present a novel link switching design with a multi-rate adaptation scheme for the proposed network under different weather and turbulence conditions. Numerical results quantitatively confirm the effectiveness of our proposal. Additionally, we provide insightful discussions that can be helpful for the practical system design of RIS-UAV-assisted HAP-based SAGIN using hybrid FSO/RF links. Monte Carlo simulations are also performed to validate the accuracy of theoretical derivations.

Deep learning techniques are very prominent in processing remotely sensed synthetic aperture radar (SAR) images for real-time, high-impact applications, such as image classification, object detection, and semantic segmentation. The accuracy of deep learning models, such as convolutional neural networks (CNNs), depends on the quality of the input data. Compared to the model-centric approach, where the model parameters are optimized during training, the data-centric approach can enhance the performance accuracy as data quality is improved before training the models. Improving the data quality of SAR images is challenging as SAR image properties are different from optical (OPT) images. Image fusion techniques proved to enhance the quality of SAR images when combined with OPT images. Many fusion techniques exist for combining SAR and OPT images in the classical domain. This paper proposes a novel approach to using quantum computing for the image fusion of SAR and OPT images. Eight different quantum processing techniques are used for the fusion of the images. We designed and created a dataset for land-use classification by collecting data using the Google Earth Engine. The quality metric measurements show that the quality of SAR images has improved by using the proposed quantum processing techniques. In addition, performance evaluation of the deep learning CNNs on the dataset was carried out for all quantum processing techniques. Our approach improved the classification accuracy from 82.64%, with only SAR images for training, to 95.36% using the proposed image fusion techniques.

Wheezes are adventitious respiratory sounds commonly present in patients with respiratory conditions. The presence of wheezes and their time location are relevant for clinical reasons, such as understanding the degree of bronchial obstruction. Conventional auscultation is usually employed to analyze wheezes, but remote monitoring has become a pressing need during recent years. Automatic respiratory sound analysis is required to reliably perform remote auscultation. In this work we propose a method for wheeze segmentation. Our method starts by decomposing a given audio excerpt into intrinsic mode frequencies using empirical mode decomposition. Then, we apply harmonic-percussive source separation to the resulting audio tracks and get harmonic-enhanced spectrograms, which are processed to obtain harmonic masks. Subsequently, a series of empirically derived rules are applied to find wheeze candidates. Finally, the candidates stemming from the different audio tracks are merged and median filtered. In the evaluation stage, we compare our method to three baselines on the ICBHI 2017 Respiratory Sound Database, a challenging dataset containing various noise sources and background sounds. Using the full dataset, our method outperforms the baselines, achieving an F1 of 41.9%. Our method’s performance is also better than the baselines across several stratified results focusing on five variables: recording equipment, age, sex, body-mass index, and diagnosis. We conclude that, contrary to what has been reported in the literature, wheeze segmentation has not been solved for real life scenario applications. Adaptation of existing systems to demographic characteristics might be a promising step in the direction of algorithm personalization, which would make automatic wheeze segmentation methods clinically viable.

From the early stage of robotic applications to current autonomous driving technologies, environment modeling has been acting as the middleware of connecting perception and decision layers. In robotic applications, space-oriented models (e.g., grid map, drivable area) are widely applied to faithfully reflect the space occupation. With the development of autonomous driving, highly dynamic and complex road environment brings rising need of understanding the type and motion status of objects, thus element list became the mainstream of environment model. However, along comes the reliablity problem caused by missed detection and irregular objects, which is still inevitable despite the detection accuracy improvement. In view of this, we provide a new view of driving environment with the proposed unified state-extended boundary (USEB), aiming to improve the reliablity of element-oriented model. For driving decision requirements, different types of elements are consistently converted into driving constraints. Semantics and dynamics are expressed as status of drivable area boundary, making it possible to merge space occupation to improve reliability against missed detection and irregular objects. Evaluation of USEB is carried out on nuScenes dataset. Comparative results show that the proposed USEB could cover the required information of driving decision, whereas achieving higher reliability than commonly applied element-oriented model.

A document by Athanasios Iossifides . Click on the document to view its contents.

Driving energy consumption plays an important role in the navigation of autonomous mobile robots in off-road scenarios. However, the accuracy of the driving energy predictions is often affected by a high degree of uncertainty due to unknown and constantly varying terrain properties, and the complex wheel-terrain interaction in unstructured terrains. In this paper, we propose a probabilistic deep meta-learning approach to model the existing uncertainty in the driving energy consumption and efficiently adapt the probabilistic predictions based on a small number of local measurements. Our method expands upon an existing deterministic deep-meta learning model that, in contrast, only provided single-point energy estimates. The performance of our method is compared against the deterministic approach in a 3D-body dynamic simulator over several typologies of deformable terrains and unstructured geometries. In this way, we provide evidence of the benefit of the proposed method to enhance the predictions with informative probabilistic considerations, which can be crucial to the safety of mobile robots traversing challenging, unstructured environments.

Autonomous vehicle requires a general planner for all possible scenarios. Existing researches design such a planner by a unified scenario description. However, it may significantly increase the planner complexity even in some simple tasks, e.g., car following, further resulting in unsatisfactory driving performance. This work aims to design a general planner which can 1) drive in all possible scenarios and 2) have lower complexity in some common scenarios. To this end, this work proposes a pertinent boundary for multi-scenario driving planning. The total approach is named as Pertinent Boundary-based Unified Decision system. Based on the original drivable area, the pertinent boundary can further support motion status and semantics of the traffic elements, which provides the potential of pertinent performance for given scenarios. The pertinent boundary can support unified driving with the drivable area, in the meantime, can be pertinently modified to support the pertinent driving decisions for identified driving scenarios (e.g., car-following, junction left turning). It will further avoid the bump between the connections of the scenarios due to the continuity of space boundary. Thus, the planner is suitable for the fully autonomous driving. The proposed method is validated in different classical driving decision scenarios. Results show that the proposed method can support pertinent driving decisions in identified scenarios, in the meantime, assure generalized cross-scenario planning when no scenario information is available. Such a method shed light on fully autonomous driving by pertinence improvement of multi-scenario decision in the complex real world.