Phase identification is the problem of determining what phase(s) that a load is connected to in a power distribution system. However, real-world sensor measurements used for phase identification have some level of noise that can hamper the ability to identify phase connections using data-driven methods. Knowing the phase connections is important to keep the distribution system balanced so that parts of the system are not overloaded, which can lead to inefficient operations, accelerated component degradation, and system destruction at worst. We present a feature engineering pipeline that combines Singular Value Decomposition (SVD) with an Interquartile Range (IQR)-based nonlinear filter to denoise matrices of voltage magnitude measurements. We empirically show that this pipeline is effective in denoising data that contains either additive Gaussian, Laplacian, Cauchy, or mixed Gaussian/Cauchy noise. This approach reduces Frobenius error and increases the average phase identification accuracy over a year of non-stationary time series data. K-medoids clustering is used on the denoised voltage magnitude measurements to perform phase identification.

With the advent of intelligent systems, we are still facing a high number of fatal traffic accidents. Driver assistance systems can significantly reduce this rate. For example, when a driver uses a turn signal, driver assistance systems alert the object’s presence in blind spot areas. Camera-based driver assistance systems for blind spots usually alert by detecting objects, including vehicles, in image frames. Based on a more dynamic dangerous situation classification for lane changing and turning to the sides, we propose an efficient blind-spot warning system that works with a single camera sensor for each side. Our contribution consists of two sections. First, we take a deeper look at classifying dangerous and safe situations in a dynamic environment with moving objects. Second, to distinguish dangerous situations from safe conditions, we install a pre-trained SOTA object detector to track vehicles in consecutive frames and then estimate the distances of tracked cars by a 6% mean percentage error rate. In addition, to detect objects in blind spots, the proposed system uses cars’ relative velocity to warn dangerous situations. This classification process is not real-time. So, in the second section, we propose a tiny model as a driver assistance system for the blind spot that works in real-time. This tiny model feeds optical flow into CNN layers. This vision-based system uses self-supervised learning without the necessity of the labeled data. It shows 97% accuracy and can detect dangerous situations as a real-time system.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. A major revision is done, compared to the previous version. Abstract: Neural networks (NNs) have been applied to predict complex ship motions for stable ship maneuvering and operations at sea, but the coupling effects of different DoFs are still not effectively modeled in black-box NNs. To this end, we propose a novel deep NN framework that explicitly models the coupling effects through factorization machines, and significantly improves the prediction accuracy without introducing much extra complexity. The proposed framework is also lightweight and frequency-aware, which serves real-time and high-frequency prediction with high accuracy. We experimentally demonstrated that the framework is not only friendly to real-time prediction and online learning, but also interpretable and with great potential to be complemented by common machine learning tricks and go deeper.

Due to recent advances in laser satellite communications technology, free-space optical (FSO) links are presented as an ideal alternative to the conventional radio frequency (RF) feeder links of the geostationary satellite for next generation very high throughput satellite (VHTS) systems. In this paper, we investigate the performance of multibeam VHTS systems that account for nonlinear high power amplifiers at the transparent fixed gain satellite transponder. Specifically, we consider the forward link of such systems, where the RF user link is assumed to follow the shadowed Rician model and the FSO feeder link is modeled by the Gamma-Gamma distribution in the presence of beam wander and pointing errors where it operates under either the intensity modulation with direct detection or the heterodyne detection. Moreover, zero-forcing precoder is employed to mitigate the effect of inter-beam interference caused by the aggressive frequency reuse in the user link. The performance of the system under study is evaluated in terms of the outage probability, the average bit-error rate (BER), and the ergodic capacity that are derived in exact closed-forms in terms of the bivariate Meijer’s G function. Simple asymptotic results for the outage probability and the average BER are also obtained at high signal-to-noise ratio.

Machine learning has had a significant impact on the value of spectroscopy-based characterization tools, particularly in biomedical applications, due to its ability to detect latent patterns within complex spectral data. However, it often requires extensive data preprocessing, including baseline correction and denoising, which can lead to unintentional bias during classification. To address this, we present a deep learning-based signal preprocessing method capable of handling all the defects of raw Raman spectroscopy data without any need of human intervention. To achieve this, a novel deep convolutional neural network (CNN) architecture was trained on randomly generated spectra with various defects. We demonstrate that the proposed network results in faster training and that it can perform complete spectral preprocessing in a single step with more accuracy, speed, and defect tolerance than conventional methods. These improvements make it an ideal candidate for hyperspectral imaging applications in which tens of thousands of raw spectra may need to be processed rapidly. The superiority of this method is demonstrated for simulated Raman spectra, surface-enhanced Raman spectroscopy (SERS) imaging of chemical species, classification of low resolution Raman spectra of human bladder cancer tissue, and finally classification of SERS spectra from human placental extracellular vesicles (EVs). These findings encourage the future use of deep learning as a rapid and unbiased method of preprocessing spectroscopy data and may be particularly useful in biomedical applications involving large data sets from highly heterogeneous samples and signal defects of complex nature.

Fundamental unit commitment approaches are of central importance in energy system modeling for the generation of detailed power plant schedules. However, existing approaches, which reduce complexity in a multi-stage process, often fail to generate realistic electricity prices. A new type of single-stage approach considers market-coupling implicitly so that, in addition to detailed power plant schedules, electricity prices reflecting real prices very well can be generated. In this paper, we show in a back-test for 2014 that an endogenously modeled market-coupling is the driving factor for the quality of resulting electricity prices. Conversely, it can be concluded that conventional multi-stage approaches show a significant distortion of modeled electricity prices due to missing price signals from neighboring market zones. Against the background of expanding trading capacities between market zones within the European power system, this issue becomes increasingly relevant when fundamentally modeling energy prices.

There has been much discussion about the impact and future of artificial intelligence (AI) in our lives and future generations. Many experts even believe that AI will “rule” the world. Artificial Neural Networks (ANN) have provided a convenient and often extremely accurate solution to problems within all fields, and can be seen as advanced general-purpose regression models that try to mimic the behavior of the human brain. The adoption and use of ANN-based methods in the Mechanistic-Empirical Pavement Design Guide is a clear sign of the successful use of neural nets in geomechanical and pavement systems. This work aims to provide an extensive and detailed state-of-the-art of the application of ANN models to pavement management, materials and design problems. Unlike former review articles published before 2014, this work is more descriptive and makes the review much more appealing to the reader by highlighting numerically and/or graphically the effectiveness and possible drawbacks of each ANN application.

Particle swarm optimization system based on the distributed architecture over multiple sub-swarms has shown its efficiency for static optimization and has not been studied to solve dynamic multi-objective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable a multiple sub-swarm’ subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. Inverted generational distance, mean inverted generational distance, and hypervolume difference metrics are used to assess the DPb-MOPSO performances. All quantitative results are analyzed using Friedman’s analysis of variance while the Lyapunov theorem is used for stability analysis. Compared with several multi-objective evolutionary algorithms, the DPb-MOPSO is robust in solving 21 complex problems over a range of changes in both the Pareto optimal set and Pareto optimal front. For 13 UDF and ZJZ functions, DPb-MOPSO can solve 8/13 and 7/13 on IGD and HVD with moderate changes. For the 8 FDA and dMOP benchmarks, DPb-MOPSO was able to resolve 4/8 with severe change on MIGD, and 5/8 for moderate and slight changes. However, for the 3 kind of environmental changes, DPb-MOPSO assumes 4/8 of the solving function on IGD and HVD metrics.

Driving strategy in dynamic environment is crucial to the automated vehicle safety. In extremely emergency scenarios with unavoidable collision (UC), especially those with complex impact patterns, the potential crash risk should be well considered. This paper proposes a crash mitigation (CM) algorithm for UCs, which directly embeds a generalized crash severity index (CSI) model to vehicle-to-vehicle collisions of multiple impact patterns. The idea is that during the short time before a collision, the vehicle will actively adapt its position and poses to minimize the potential crash severity level after the collision. To this end, the generalized CSI model is introduced to estimate the potential crash severity of all sample paths, from which a crash-severity-optimal trajectory is obtained. To improve the inferring time efficiency of the planning module, a neural network is constructed and deployed to approximate the nonlinear severity model. The proposed algorithm is first validated through simulations of UC scenarios, including entry ramp merging, intersection crossing and downhill/uphill crossing. Then for the intersection crossing scenario, the algorithm is deployed to a real car and validated through digital-twin experiments. Results show that by combining the braking and steering interventions for better crash severity reduction, the proposed strategy can achieve better mitigation effects than commonly-used collision avoidance (CA) strategies. This reveals that a new mindset of comprehensive safety strategy should not focus only on CA, but also the last resort of CM if collision is unavoidable. Our work may contribute as a promising solution to the safety problem in emergency scenarios.

In this paper, a diffusion maximum correntropy criterion (DMCC) algorithm with adaption kernel width is proposed, denoting as DMCC$_{\rm adapt}$ algorithm, to find out a solution for dynamically choosing the kernel width. The DMCC$_{\rm adapt}$ algorithm chooses small kernel width at initial stage to improve its convergence speed rate, and uses large kernel width at completion stage to reduce its steady-state error. To render the proposed DMCC$_{\rm adapt}$ algorithm suitable for sparse system identifications, the DMCC$_{\rm adapt}$ algorithm based on proportional coefficient adjustment is realized and named as diffusion proportional maximum correntropy criterion (DPMCC$_{\rm adapt}$). The theoretical analysis and simulation results are presented to show that the DPMCC$_{\rm adapt}$ and DMCC$_{\rm adapt}$ algorithms have better convergence than the traditional diffusion AF algorithms under impulse noise and sparse systems.

In the age of rapid technological evolution, synthetic realities and AI-generated content are transforming the very fabric of digital interaction. This article delves into the multifaceted landscape of artificial intelligence's role in the creation of auto-generated content, spanning text, images, videos, and 3D mediums. The challenges posed by such developments---ranging from misinformation campaigns to threats against traditional forensics---are examined in depth. At the same time, the article highlights emergent opportunities in the realms of security, privacy, and digital forensics that are spurred by these AI advancements. Within this discourse, the paper seeks to propose robust policies and regulatory frameworks that can uphold the integrity of digital media while ensuring societal safety. Balancing ethical considerations, the article underscores the necessity of international cooperation, technological innovation, and public awareness to navigate the transformative potential of synthetic media. Through a blend of case studies, future projections, and policy discussions, the article offers a comprehensive perspective on the intricate interplay between AI-enabled content and societal implications.

To investigate the diagnostic accuracy of deep learning (DL) algorithms to detect primary open-angle glaucoma (POAG) trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS). 66,715 photographs from 3,272 eyes were used to train and test a ResNet-50 model to detect the OHTS Endpoint Committee POAG determination based on optic disc (n=287 eyes, 3,502 photographs) and/or visual field (n=198 eyes, 2,300 visual fields) changes. OHTS training, validation and testing sets were randomly determined using an 85-5-10 percentage split by subject. Three independent test sets were used to estimate the generalizability of the model: UCSD Diagnostic Innovations in Glaucoma Study (DIGS, USA), ACRIMA (Spain) and Large-scale Attention-based Glaucoma (LAG, China). The DL model achieved an AUROC (95% CI) of 0.88 (0.82, 0.92) for the overall OHTS POAG endpoint. For the OHTS endpoints based on optic disc changes or visual field changes, AUROCs were 0.91 (0.88, 0.94) and 0.86 (0.76, 0.93), respectively. False-positive rates (at 90% specificity) were higher in photographs of eyes that later developed POAG by disc or visual field (19.1%) compared to eyes that did not develop POAG (7.3%) during their OHTS follow-up. The diagnostic accuracy of the DL model developed on the OHTS optic disc endpoint applied to 3 independent datasets was lower with AUROC ranging from 0.74 to 0.79. High diagnostic accuracy of the current model suggests that DL can be used to automate the determination of POAG for clinical trials and management. In addition, the higher false-positive rate in early photographs of eyes that later developed POAG suggests that DL models detected POAG in some eyes earlier than the OHTS Endpoint Committee.

The Covid-19 disease has spread widely over the whole world since the beginning of 2020. Following the epidemic which started in Wuhan, China on January 30, 2020 the World Health Organization (WHO) declared a global health emergency and a pandemic. Researchers of different disciplines work along with public health officials to understand the SARS-CoV-2 pathogenesis and jointly with the policymakers urgently develop strategies to control the spread of this new disease. Recent findings have observed specific image patterns from computed tomography (CT) for patients infected by SARS-CoV-2 which are distinct from the other pulmonary diseases. In this paper, we propose an explainable-by-design that has an integrated image segmentation mechanism based on SLIC that improves the algorithm performance and the interpretability of the resulting model. In order to evaluate the proposed approach, we used the SARS-CoV-2 CT scan dataset that we published recently and has been widely used in the literature. The proposed Super-xDNN could obtain statistically better results than traditional deep learning approaches as DenseNet-201 and Resnet-152. Furthermore, it also improved the explainability and interpretability of its decision mechanism when compared with the xDNN basis approach that uses the whole image as prototype. The segmentation mechanism of Super-xDNN favored a decision structure that is more close to the human logic. Moreover, it also allowed the provision of new insights as a heat-map which highlights the areas with highest similarities with Covid-19 prototypes, and an estimation of the area affected by the disease.

The 2-D structure of graphene, an allotrope of carbon with remarkable characteristics, is revolutionizing the technology world. This article provides a synopsis of three popular methods for graphene synthesis. The mechanical exfoliation method attempts to separate graphene layers from bulk graphite and is used for small-scale production. The next method is the chemical vapor deposition (CVD) synthesis, which uses a reaction between a gaseous hydrocarbon and a solid substrate to promote graphene growth. Finally, the modified Hummer’s method involves the use of oxidizing and reducing agents to obtain graphene oxide (GO) from graphene flakes under low-temperature conditions from which graphene is synthesized as the final product.

Merging of multiple satellite datasets is a simple yet effective way to reduce prediction error. However, most merging methods for satellite data today are based on weighted averaging first proposed in 1969 for economic forecasting, which does not provide optimal outcomes when applied to satellite data. If our aim is to produce a merged data product that minimizes the prediction errors against a prediction target, there is no reason to insist that the merged product be an average of the parent datasets. A more disciplined approach based on mathematical optimization would be to minimize prediction errors. However, formulating merging as an optimization problem is insufficient by itself as the statistics needed for optimization, e.g. signal-to-noise ratio (SNR) of parent products, are often unavailable in practice and must be estimated jointly. In this paper, we address both of these problems for data merging. We first formulate optimal merging of satellite data as a SNR optimization (SNR-opt), and propose an estimation method to jointly estimate the required SNRs. This SNR-based approach has a natural interpretation as a multi-input single-output Wiener filter. Through extensive experimental validation on three global- scale satellite-derived soil moisture and land surface temperature products, we demonstrate that our SNR optimization significantly improves merging results over weighted averaging schemes.

Several types of Routing Protocols for mobile ad hoc networks have been proposed recently for their application in various areas such as military, government and many other fields. In this paper, we have proposed a comparative study of various routing protocols for mobile ad-hoc networks particularly emphasizing on its security aspect. Furthermore, we have discussed the comparison among existing routing protocols for MANET. Main factor for comparison is routing methodologies and knowledge required to make routing decisions. The comparison is made on the basis of the following factors: integrity, confidentiality, authentication, nonrepudiation and availability. Analysis of the secured version of proposed routing method has been done on the basis of the five security requirements and the same has been shown in the comparison tables.

There is a rise of interest within the applied physics and engineering electromagnetics community in exploring the topological structure of various systems and devices. This paper provides an outline for a new approach to microwave circuit theory via Lie theory emphasizing the deep topological structure of transmission lines and is expected to shed light on related issues like nano-EM and MTMs.

This paper considers the power allocation for non-orthogonal multiple access (NOMA) users to enable using the successive interference cancellation (SIC) while providing reliable error performance. The derived closed-form expressions are applicable for arbitrary number of NOMA users each of which has a square or rectangular quadrature amplitude modulation (QAM) constellation with arbitrary order. The obtained numerical results show that power assignment process at the transmitter for the superposition process and at the receiver for the SIC process should be performed meticulously because the power difference between the weakest and strongest users can be tremendous when the number of users or the modulation orders increase. Moreover, the derived expressions can be used to reduce the computational complexity that is required to obtain the optimal power coefficients using brute force methods by significantly reducing the search space.