Search

This paper addresses the challenging problem of Unit Commitment (UC), which involves the optimal scheduling of power generation units while adhering to numerous network operational constraints called security-constrained UC (SCUC). SCUC problem aims to minimize costs subject to turning on economically efficient generators and turning off expensive ones. These operational constraints include load balancing, voltage level at buses, minimum up and down time requirements, spinning reserve, and ramp up and down constraints. The SCUC problem, subject to these operational constraints, is a complex mixed-integer nonlinear problem (MINLP). There has been a growing interest in using evolutionary algorithms (EAs) to tackle large-scale multi-objective MINLP problems in recent two decades. This paper introduces a novel approach to address the SCUC problem, which is further complicated by including network constraints. They are pioneering the integration of single and multi-objective EAs to solve the SCUC problem while incorporating AC network constraints through hybrid binary and real coded operators. The development of an ensemble algorithm that combines mixed real and binary coded operators, extended by a bidirectional coevolutionary algorithm to tackle multi-objective SCUC problems. The paper implements a new formulation based on three conflicting objective functions: cost of energy supplied, startup and shutdown costs of generators, energy loss, and voltage deviation to solve the SCUC problem. Implementing a new formulation also addresses the solution of single and multi-objective SCUC problems using a combination of proposed technical and economic objective functions. The proposed algorithm is rigorously tested on a 10-unit IEEE RTS system and a 6-unit IEEE 30-bus test system, both with and without security constraints, addressing week-ahead and day-ahead SCUC scenarios. Simulation results show that the proposed algorithm finds near-global optimal solutions compared to other state-of-the-art EAs. Additionally, the research demonstrates the effectiveness of the proposed search operator by integrating it with a multi-objective coevolutionary algorithm driven by both feasible and infeasible solutions, showcasing superior performance in solving multi-objective SCUC problems. These results are compared with various recently implemented Multi-Objective Evolutionary Algorithms (MOEAs), demonstrating the superiority of

The field of cybersecurity has witnessed a significant shift towards Programmable Data Planes with the emergence of the P4 programming P4. The existing literature lacks a comprehensive taxonomy that provides collaborative classifications and characterization of P4-based cybersecurity solutions. In this paper, we propose a novel taxonomy to better characterize published works in the field of cybersecurity solutions developed leveraging P4 and Programmable Data Planes. Our taxonomy introduces three main categories: detection techniques, mitigation actions, and deployment platforms. Unlike existing classification approaches present in literature, our taxonomy allows categorizing a given work according to different criteria, thus enabling collaborative classifications and the identification of shared features by highlighting intra-category and inter-category relationships that can be established among different works. Through our comprehensive characterization of the works identified in the literature, we present key findings that contribute to extend the current understanding of the field. By identifying the diverse range of techniques employed and the platforms utilized, we aim at addressing the needs to understand the area, which is fundamental for future advancements. Notably, we emphasize the significance of security provisioning through the adoption of cybersecurity functional abstractions. These abstractions enable the emulation of the behavior of conventional security devices, leveraging the capabilities of P4. Furthermore, we discuss several research challenges and possible future directions that have emerged from our analysis. This paper focuses on providing valuable insights and knowledge for researchers and practitioners in the field of cybersecurity solutions based on Programmable Data Planes leveraging P4, with the final goal of opening avenues for new research.

Deep reinforcement learning (DRL) methods have emerged as a feasible solution for addressing the power resource allocation problem in ultra-dense small-cell networks (UDSCNs). In this paper, we propose a novel actor-critic-based low-coupling policy optimization (LCPO) framework. Our framework aims to achieve practicality by employing a design that consists of training and execution modules with low coupling. By adopting policy optimization methods, including advantage actor-critic (A2C) and proximal policy optimization (PPO) with state-dependent exploration (SDE) technique, LCPO demonstrates stable performance. In this study, we define the research problem of power resource allocation in UDSCNs and present the mathematical algorithm employed in the LCPO framework. We compare the performance of LCPO with other algorithms, such as deep deterministic policy gradient (DDPG) and fractional programming (FP) algorithms. Through extensive simulations, our proposed LCPO framework outperforms DDPG and FP algorithms in terms of both performance and execution time. Furthermore, to provide an up-to-date overview of the current state-of-the-art, we incorporate recent research papers in the field. The inclusion of these papers enhances the relevance of our study and allows readers to gain insights into the latest advancements in power resource allocation in UDSCNs. The results of our research highlight the effectiveness of the LCPO framework in addressing the power resource allocation problem in UDSCNs. The proposed framework offers superior performance compared to existing algorithms, making it a promising solution for optimizing power allocation in UDSCNs.

In this paper, we have developed an augmented reality cricket broadcasting application that uses player face recognition during play to display player personal data. The system utilizes the AdaBoost algorithm for player and player face detection, employing a PAL based face recognition model to recognize the faces of players on the field. The system is trained on a large dataset of cricket game footage and achieves high accuracy in detecting and recognizing players’ faces even with several conditions such as occlusion, non-uniform illumination, expression and pose variation. The system has the potential to enhance the viewing experience of cricket games by providing real-time player identification and statistics. The system can also be used in other sports to provide similar benefits. The paper discusses the system’s methodology, results, and implications for the future of sports broadcasting. Overall, the system provides a promising solution for automatic player face detection and recognition in sports broadcasting.

This paper presents a systematic bibliometric analysis of the artificial intelligence (AI) domain to explore privacy protection research as AI technologies integrate and data privacy concerns rise. Understanding evolutionary patterns and current trends in this research is crucial. Leveraging bibliometric techniques, the authors analyze 8,322 papers from the Web of Science (WoS) database, spanning 1990 to 2023. The analysis highlights IEEE Transactions on Knowledge and Data Engineering and IEEE Access journals as highly influential, the former being an early contributor and the latter emerging as a pivotal source. The study demonstrates substantial disparities in scientific productivity across countries. Specifically, the top 10 countries collectively accounted for 74.8% of the articles, with China and the USA making up nearly half of the total contribution (46.1%). In contrast, regions in Africa and South America exhibited lower scientific production. The evolution of privacy preservation research is reflected, shifting from an algorithm-oriented approach to a focus on data orientation, and subsequently, to privacy solutions centered around Cloud Computing. In recent years, there has been a shift towards embracing Federated Learning and Differential Privacy. The analysis brings to light emerging themes and identifies research gaps, notably a global disparity in research output and a lag in ethical and legal inquiry. It asserts that enhanced interdisciplinary collaboration is imperative to formulate comprehensive privacy solutions for AI. Specifically, the paper imparts invaluable insights that are pivotal for effectively addressing the evolving privacy concerns in the era of AI and big data.

This paper presents a novel localization technique—Multipath-radio tomographic imaging (RTI)—based on millimeter-wave (mm-wave) radio systems, such as 5G mobile and WiGig LAN systems. Because of their high delay and angle resolutions, mm-wave radio systems can realize highly accurate device-free localization (DFL) for smart home/smart building applications. They can thus easily separate the multipath components. The Multipath-RTI can drastically reduce the number of anchor nodes using virtual nodes created by multipaths. This paper established the signal processing procedure for RTI image generation and position estimation using Multipath-RTI. Then, a practical signal processing scheme, ray-tracing-assisted Multipath-RTI, is presented, and the DFL results obtained by multilink double-directional channel sounding measurements are shown. Here, the position estimation errors in the measurements were less than 0.5 m for all target positions. Furthermore, the empirical position estimation errors for a single target under various conditions obtained by simulations were less than 0.5 m in 96.2 % and 88.0 % of the areas in an L-shaped room and a room with an obstacle in the center, respectively. The position estimation accuracy for multiple targets was also evaluated, where, for example, the errors for three targets were less than 0.1 m at 50 % probability with failure rates of less than 20 %.

It is important in production to achieve accurate counting and density estimation of high-density culture fry under the environmental conditions of aquaculture scenarios in an efficient and accurate manner. However, none of the current methods for fry counting works well under the high-density and high-overlap conditions of real aquaculture scenarios. Therefore, in this paper, we propose a high-density farming fry monitoring network model, Super-Resolution GAN Density Estimate Attention Network (SGDAN), which incorporating an image enhancement algorithm and an attention mechanism, and we create a high-density farming fry dataset (HD-FryDataset) based on the environmental conditions of real aquaculture scenarios. The network model is designed to improve and optimize the targeted subnetworks for several key aspects of high-density fish fry monitoring work. Four subnetworks are included for image optimization, feature extraction, attention, and density map estimation. The experimental results show that the SGDAN network model achieved an average counting accuracy of 97.57% on the high-density culture fry dataset, which was 8.23% and 2.06% higher than those of MCNN and CSRNet, respectively. Additionally, the MAE and RMSE of the model were reduced by 71.9% and 67.3% and by 34.3% and 33.2% compared with those of MCNN and CSRNet, respectively. The model proposed in this paper also has a better ability to generate predictive density maps. The density maps generated by SGDAN have values of the evaluation metrics PSNR and SSIM of 20.33 and 0.933, respectively, which are 3.31 and 0.037 and 2.63 and 0.031 higher than those of MCNN and CSRNet. In general, the network model proposed in this paper outperforms existing network models in two applications: accurate counting of fry and generation of density maps for high-density culture in aquaculture. It also provides a good solution for digitizing the number of fry and visualizing the density of high-density culture in intelligent aquaculture systems.

Anomaly detection is of paramount importance in many real-world domains characterized by evolving behavior, such as monitoring cyber-physical systems, human conditions and network traffic. Current research in anomaly detection leverages offline learning working with static data or online learning focusing on constant adaptation to evolving data. At the same time, lifelong learning represents an emerging trend, answering the need for machine learning models that continuously adapt to new challenges in dynamic environments while retaining past knowledge. Although this aspect could be beneficial to build effective and robust anomaly detection models, lifelong learning research is mainly dedicated to proposing new model update strategies in image classification and reinforcement learning domains. The limited scope addressed by lifelong learning works thus far creates a gap in understanding whether such techniques and capabilities can be fruitfully exploited in anomaly detection contexts, which represents the main motivation of this paper. More specifically, anomaly detection provides unique challenges, such as an evolving normal class and limited availability of anomalies, which significantly differs from the landscape and scenarios of lifelong image classification and reinforcement learning. In this paper, we face this issue by exploring, motivating, and discussing lifelong anomaly detection, as well as providing foundations with regard to scenarios, strategies, and metrics. First, we explain why lifelong anomaly detection is relevant, defining challenges and opportunities to design anomaly detection methods that deal with lifelong learning complexities. Second, we formulate and characterize lifelong learning settings tailored for anomaly detection problems, and design a scenario generation procedure that enables researchers to experiment with lifelong anomaly detection using existing datasets. Third, we perform experiments with popular anomaly detection methods on proposed lifelong scenarios, emphasizing the gap in performance that could be filled with the adoption of lifelong learning. In summary, our efforts are directed at assessing the performance of non-lifelong anomaly detection models in lifelong scenarios and how the adoption of lifelong learning impacts their learning capabilities. Overall, we conclude that the adoption of lifelong anomaly detection is important to design more robust models that provide a comprehensive view of the environment, as well as simultaneous adaptation and knowledge retention.

This paper is a review of chosen reports on the integration of photovoltaics and agrotechnics and also presents the concept of an agro-photovoltaic foil tunnel module. This concept assumes the possibility of integrating modules into intelligent agro-photovoltaic fields. The proposed solution combines cultivation in a foil tunnel with a photovoltaic farm. The paper reviews electronic solutions to control ambient parameters, particularly those influencing the rate of photosynthesis in foil tunnels. The article also presents an analysis of the energy and water balances of the module in 4 different climate zones, including locations in Poland, Spain, Algeria, and Colombia. The module’s water requirements include drip irrigation and fogging systems. Water is assumed to be obtained from rainfall, desalination, non-conventional (treated, desalinated) resources, groundwater, and sub-artesian wells. The module’s energy needs include electricity consumption for pumping freshwater, desalination and water treatment, irrigation and fertilization, additional lighting and heating of plants, folding and unfolding of foil covers, and energy consumption related to automation. The article also analyzes crop shading by solar panels and its impact on the selected crop. The functionality of the presented solution was assessed for tomato cultivation in the four selected areas. A 240 m2 foil tunnel was considered. 60 m2 of the tunnel was covered by PV panels. The analysis results show that the most energetically effective agro-photovoltaic cultivation of tomatoes appears to be in the Saharan region of Africa where a 16 MWh annual energy surplus was obtained. Cultivations in Spain (Cartagena) and Colombia (Cali) seem to be slightly less effective, with approximately 15.5 MWh/a. The least effective agro-photovoltaic cultivation of tomatoes proved to be in Poland where the energy surplus reached 8.5 MWh/a. However, the economic return from cultivation strongly depends on local energy and tomato prices. The system of smart tunnels proposed by the authors combines photovoltaics with controlled protection of crops against unfavorable and extreme climatic conditions. In addition, the system allows plants to be grown with the maximum possible days with natural growing conditions, i.e. with uncovered tunnels, with natural bio-fertilization, and with natural sunlight.

In this paper, we investigate the use of the spontaneous speech of dysarthric people for training an automatic speech recognition (ASR) model for them. Although the spontaneous speech of dysarthric people can be collected relatively easily compared to script-reading speech, which is obtained by having them read a prepared script, labeling the spontaneous speech of dysarthric people is very difficult and costly. For training an ASR model using unlabeled speech data, pseudo-labeling and self-supervised feature learning have been studied as effective approaches; however, the effectiveness of these approaches has not been clear when they are applied to the unlabeled dysarthric speech. In addition, pseudo-labeling may not be effective since the pseudo-labels of dysarthric speech include many errors and are not reliable. In this paper, we evaluate the above two approaches for the dysarthric speech recognition, and we propose a multi-task learning approach, which combines these approaches to train an ASR model that is robust against the errors in the pseudo-labels. Experimental results using Japanese and English datasets demonstrated that all approaches are effective, but among them, the proposed multi-task learning approach showed the best performance.

Steel is a crucial material that is extensively utilized in various aspects of daily life and holds significant importance. However, during its production, there is a possibility of certain defects arising that could have a negative impact on the quality of the steel. Only by accurately detecting the defects on the steel surface can we avoid the harm caused by defects in steel. Because the steel surface defect detection algorithm is prone to misdetection, missed detection, and other problems, a steel surface defect detection algorithm based on improved YOLOX is proposed. First, the CSPCrossLayer module proposed in this paper is used to replace the CSPLayer structure in the backbone network to enrich the gradient information of the network and strengthen the feature extraction capability; Then, the SA (Shuffle Attention) module is added after the output of the backbone network, highlighting the general information to input high-quality features for the feature fusion network; Finally, the PSblock module is proposed to replace the CSPLayer structure in the feature fusion network, which reduces redundant computations to efficiently perform feature fusion on feature layers of different scales and improves the feature fusion capability of the model. The experiments involved testing the algorithm on two datasets: the publicly available NEU-DET dataset and a steel rail dataset collected in this paper. The algorithm can reach 77% mAP on the NEU-DET dataset, while the detection speed is 100 FPS. It reaches 88.8% mAP on the steel rail dataset, and the detection speed is 93FPS. These results demonstrate that the proposed algorithm is capable of swiftly and accurately detecting surface defects in steel materials.

Effective energy management is crucial for sustainability, carbon reduction, resource conservation, and cost savings. However, conventional energy forecasting methods often lack accuracy, suggesting the need for advanced approaches. Artificial intelligence (AI) has emerged as a powerful tool for energy forecasting, but its lack of transparency and interpretability poses challenges for understanding its predictions. In response, Explainable AI (XAI) frameworks have been developed to enhance the transparency and interpretability of black-box AI models. Accordingly, this paper focuses on achieving accurate household energy consumption predictions by comparing prediction models based on several evaluation metrics, namely the Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The best model is identified by comparison after making predictions on unseen data, after which the predictions are explained by leveraging two XAI frameworks: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These explanations help identify crucial characteristics contributing to energy consumption predictions, including insights into feature importance. Our findings underscore the significance of current consumption patterns and lagged energy consumption values in estimating energy usage. This paper further demonstrates the role of XAI in developing consistent and reliable predictive models.

Given the complexity and uncertainty of the underwater environment, it is of great importance to generate realistic and high-quality images. In this paper, we propose six unconditional generative models based on the Diffusion-Styegan2 generative model, incorporating Wasserstein, R2 regularization terms, and other techniques for anemone image generation. The Wasserstein distance technique is used in the loss part of Diffusion-Styegan2, combined with the back propagation algorithm to compute the gradient in the neural network while retaining the computational map to improve the training efficiency and training stability; the R2 regularization term is used to introduce the r2 hyperparameter, and the L2 regularization technique is used based on the original R1 regularization term to regularize the gradient of the discriminator to improve the training and generation performance of the model; the ADA technique is used based on DWBG-Stylegan2 to further improve the quality and stability of the generated images. In addition, a set of SA datasets (sea anemone datasets) with a resolution of $256^{\ast} 256$ is proposed in this paper. The experimental results show that the FID value of Diffusuion-Stylegan2 is 10.31, the value of FID of DWBG-Stylegan2 is 8.32, the value of FID of Diffusion-Stylegan2-R2 is 9.58, and the optimal FID value of this experiment is achieved by DWBG-Stylegan2-ADA with a value of 5.67, which is considerably lower compared to the FID value of Diffusion-Stylegan2. Therefore, techniques such as Wasserstein and R2 regularization terms can effectively generate more realistic images of anemones. Meanwhile, this experiment provides new ideas and methods for the construction of the unconditional generative model.

Vision-based smart agriculture is an important way to improve the efficiency of agricultural production. Labeling images for deep learning in complex field photos is a difficult task. In this paper, an image augmentation method that can help reduce the workload of image labeling based on synthetic images is proposed. The synthetic images consisted of three parts: crop, weed, and soil. The crop and weeds were obtained automatically by Excess Green (ExG) and minimum error threshold segmentation. The data augmentation method was tested on image classification, object detection, and semantic segmentation tasks by Resnet, YOLOV5, and DeeplabV3. The accuracy of the classification model reached 0.99. The IoU of object detection and semantic segmentation were 0.98 and 0.96, respectively. The results showed that the method in this paper was acceptable despite slight overfitting. This method was proposed based on the characteristics of field images, and it was meant for reducing the workload of labeling images.

During the past few decades, research on digital media watermarking–initially designed for digital images with the envisioned applications of copyright protection or copy control– has significantly evolved with respect to other covers (i.e., video, audio, speech) and many more potential applications, including tamper detection, broadcast monitoring, and, more recently, fake news detection. As a result, various surveys have tried to summarize certain aspects of this research field as it has grown. This has led to more than 130 survey papers being written at different points in time, describing various parts of the scientific efforts focused on digital media watermarking. Considering the above, the aim of this paper is twofold. First, we conduct a meta-survey based on 64 selected research works, in order to summarize the most notable survey papers in this field, which allows us to “draw a map” of this research area. Second, we focus on providing the requirements for digital watermarking techniques when applied to their most recent application: detecting fake news in multimedia content. Finally, an outline of the approach taken within the DISSIMILAR (Detection of fake newS on SocIal MedIa pLAtfoRms) project for the detection of disinformation is presented.

As one of the important energy industries, the safe production of coal mines not only concerns the safety of employees, but also affects the sustainable development of society. Recent rescue cases have shown that the layout of material bases and the allocation of emergency materials need to be further optimized. For this problem, this paper adopts the material hierarchical reserve mode for the first time, combining the material hierarchical reserve mode, multi-level material bases selection model, and the Fuzzy Analytic Hierarchy Process (FAHP) method to determine the optimal location of the coal mine material bases in Liaoning Province. Determine the reserve level of emergency materials through evaluation and clustering analysis of various emergency materials. Improve the traditional gravity model and construct a multi-objective planning model for multi-level material bases site selection. Apply the reserve level of emergency materials and real regional data to the site selection model, and use the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to solve the model. Then, the optimal site selection scheme was selected using FAHP, and sensitivity analysis was conducted. The research results indicate that: 1) 9 county-level, 6 city-level and 2 provincial-level material bases will be constructed; 2) location selection of material bases is not only affected by costs, but also by the density of road networks; 3) use of hierarchical material reserve mode can significantly reduce the construction cost of material bases. Compared to the traditional material reserve mode, it can reduce costs by 86.6%. Compared to a single-level material bases, it can still reduce costs by 74.7%. The research results of this paper can be used for the selection of emergency material bases and the allocation of emergency material reserves, which can reduce costs and promote the improvement of regional emergency rescue capabilities.

Unmanned Aerial Vehicle (UAV) applications are extended extremely. Some applications need to use several UAVs for a general mission which can be considered a UAV fleet. One of the important characteristics for the evaluation of a UAV or UAV fleet is reliability. There are studies in which methods for analysis of their reliability are considered. Reliability analysis of UAV fleets is less frequently studied, although a single UAV cannot be performed in many applications and requires the involvement of multiple UAVs. Typically, this analysis is based on evaluating two states as operational/functioning and faulting. This paper proposes a new method to calculate the availability of a UAV fleet as one of the reliability characteristics. Unlike well-known UAV fleet analysis approaches, in this paper, the availability is studied based on a mathematical model of a Multi-State System (MSS). MSS allows us to examine more than two states (operational and faulty) of the system, so this analysis is more detailed. In this paper, based on MSS, the analysis of various topologies of UAV fleets is implemented. This mathematical model is used for the analysis and evaluation of topologies of homogeneous and heterogeneous UAV fleets, which can be irredundant or redundant hot stable systems. The interpretations of different topologies of fleets as typical structures of MSS (series, parallel, $k$ -out-of- $n$ ) are considered. New mathematical definitions of the availability of UAV fleets of different topologies based on MSS structure function are proposed. These definitions allowed us to consider the influence of the number of UAVs on the UAV fleet availability and compare the availabilities of considered topologies of fleets.

Despite consistent maintenance and monitoring equipment installed in pumped storage hydropower (PSH) facilities, many shafts and electrical component failures are reported, possibly resulting from undetected sources. These sources include undetectable vibrations or, in certain conditions, high-frequency mechanical or electrical harmonics. This paper presents a direct method for plotting Campbell diagrams of large motors supplied by variable frequency drives (VFDs) for torsional analysis purposes in PSH systems. These diagrams display the precise locations where torsional stress components induced by VFDs can interfere with shaft resonance modes. The method simplifies the determination of the magnitude of stimulus forces in the motor airgap that may threaten the shaft. The method has been successfully applied to two-level, three-level neutral-point clamped, and seven-level cascaded H-bridge multilevel inverters, which are commonly used industrially available VFD topologies for pumped PSH plants. The paper also discusses the theoretical motor-pump voltage, current, and torque spectra when driven by a cascaded H-bridge multilevel converter operating with bypassed and faulty cells. The accuracy of the theoretical developments is supported by selected simulations results at different operating points and different fault conditions. Hybrid experimental-numerical VFD-induced harmonic stress analysis is also performed to demonstrate the relevance of the proposed study.

The paper presents an online version of the identification method for estimating the impulse responses in the case of a two-input single-output linear empirical model of type 1 diabetes that allows us to adapt the model parameters due to the intra-subject time variability in real time. The method builds on and augments our original research by providing important enhancements concerning the online parameter estimation, recursive formulation of essential equations, improved regularization, and new effective approaches to numerically solve the estimation problem. Recursive equations are derived to update the covariance matrix of the sample cross-correlation function, as well as the inverse of this covariance matrix, where the customized Sherman-Morrison formula was considered. To efficiently update the parameter estimate at each sample while avoiding direct calculation of the Hessian matrix inverse, two alternative strategies are proposed to be applied instead. The first is based on the numeric minimization by the conjugate gradient method, whereas the second takes advantage of the Schulz method to approximate the inverse Hessian matrix. As a result, all steps of the identification algorithm were designed so that only basic linear operations are required. Features to robustify the estimate were also involved, as the optimal regularization strategies based on the inverse of the covariance matrix of the actual parameter distribution and the inter-sample parameter drift were applied. In the end of the paper, a series of simulation-based experiments was carried out to assess the effectiveness of the proposed method and to demonstrate all of its aspects and important characteristics. The documented results showed that the method can yield valid estimates of impulse responses and also effectively adapt parameters in real time under the influence of time-varying physiology.

This paper proposes a current-fed dual active bridge (DAB) converter, connected in parallel on the secondary-side AC-link, as a DC–DC converter with a high boost ratio and high input current capacity. Compared to conventional converters, the proposed converter can reduce the number of components without complicating power control and increase input current capacity. However, the parallel connection on the secondary-side AC-link could cause a circulating current. Therefore, this paper develops an equivalent circuit model of the proposed converter and theoretically analyzes the characteristics of the circulating current. The analysis reveals the influences caused by the circulating current on the proposed converter and shows a guideline for reducing the circulating current. Furthermore, using a prototype rated at 2 kW, the validity of the theoretical analysis is demonstrated through experiments. In the experiments, the validity of the theoretical analysis was confirmed, and it was verified that the proportion of the circulating current relative to the secondary-side converter’s current was less than 8% at maximum. In addition, introducing a phase-shift control on the primary-side converters reduced the input current ripple by 38.6%. Furthermore, even under conditions where the circulating current is relatively large, the efficiency drop of the proposed converter was minimal. This indicates that the influence of the circulating current on the proposed converter is sufficiently small.

As power distribution systems evolve in complexity and scale, the coordination and control of distributed energy resources (DERs), intelligent devices, and agents become increasingly challenging. Distribution utilities invest in advanced distribution management systems (ADMSs) and distributed energy resource management systems (DERMSs) to enhance distribution systems’ reliability, resiliency, and efficiency. Standardizing the Application Programming Interface (API) for ADMS applications is crucial to accelerate the integration of advanced distribution technologies. This paper introduces a distributed application architecture within the GridAPPS-D platform, aiming to address the limitations of centralized architectures in terms of scalability, maintainability, and flexibility. The proposed architecture draws inspiration from the Laminar Coordination Framework and is validated through extensive stakeholder engagement. Emphasizing extensibility, boundary deference, structural scalability, and securability, the layered framework is well-suited for large-scale distribution networks with diverse grid-edge devices and ownership structures. The contributions of this paper include a distributed layered architecture with defined distributed areas, a Common Information Model (CIM)-based standardized API for developing and deploying distributed applications (Distributed App API), and the design process and reference implementation of distributed services and applications. Based on laminar coordination, the software architecture combines centralized, distributed, and edge-control paradigms for effective distributed operations. The paper concludes with extending the centralized API in GridAPPS-D to distributed APIs for standards-based message exchange, emphasizing the need for scalable communication to coordinate diverse distributed agents. This work provides a foundation for advancing the field of distributed control in power distribution systems, supporting both centralized and distributed analytics and control arrangements.

The gearbox fault diagnosis method based on deep learning lacks good interpretability, and the complexity of the model leads to prolonged diagnostic times. Based on this, this paper proposes a Compute Unified Device Architecture (CUDA) parallelized Bayesian Network (BN) approach for gearbox fault diagnosis. To address the problem of the Max-Min Hill-Climbing (MMHC) algorithm easily getting trapped in local optima during BN learning, this paper introduces the Snake Optimization (SO) algorithm. This algorithm utilizes multiple individuals to represent potential solutions and updates their positions at each iteration, employing a diversity search strategy to avoid falling into local optima. In response to the high complexity and poor real-time inference of BN fault diagnosis methods, this paper utilizes the CUDA platform as a development framework and employs a CPU+GPU heterogeneous parallel BN to improve the operational efficiency of the model, thereby enhancing the real-time capability of fault diagnosis. Finally, validation is conducted on the gearbox dataset from Southeast University, demonstrating that the proposed method achieves a diagnosis accuracy of 99.7% on 800 fault samples with a training time of 10.4 seconds. Compared to traditional methods, this approach exhibits significant advantages in diagnostic accuracy and training speed, effectively enhancing the accuracy and stability of fault diagnosis.

This paper presents an approach for reducing the memory requirements of periodically executed dataflow applications, while minimizing the period when deployed on a many-core target. Often, implementations of dataflow applications suffer from data duplication if identical data has to be processed by multiple actors. In fact, multi-cast (also called fork) actors can produce huge memory overheads when storing and communicating copies of the same data. As a remedy, so-called Multi-Reader Buffers (MRBs) can be utilized to forward identical data to multiple actors in a First In First Out (FIFO) manner while storing each data item only once by sharing. However, using MRBs may increase the achievable period due to contention when accessing the shared data. This paper proposes a novel multi-objective design space exploration approach that selectively replaces multi-cast actors with MRBs and explores actor and FIFO channel mappings to find trade-offs between the objectives of period, memory footprint, and core cost. In distinction to the state-of-the-art, our approach considers (i) memory-size constraints for on-chip memories, (ii) hierarchical memories to implement the buffers, e.g., tile-local memories, (iii) supports heterogeneous many-core platforms, i.e., core-type dependent actor execution times, and (iv) optimizes the buffer placement and overall scheduling to minimize the execution period by proposing a novel combined actor and communication scheduling heuristic for period minimization called Communication-Aware Periodic Scheduling on Heterogeneous Many-core Systems (CAPS-HMS). Our results show that the explored Pareto fronts improve a hypervolume indicator over a reference approach by up to 66 % for small to mid-size applications and 90 % for large applications. Moreover, selectively replacing multi-cast actors with corresponding MRBs proves to be always superior to never or always replacing them. Finally, it is shown that the quality of the explored Pareto fronts does not degrade when replacing the efficient scheduling heuristic CAPS-HMS by an Integer Linear Program (ILP) solver that requires orders of magnitude higher solver times and thus cannot be applied to large scale dataflow network problems.

Although AI algorithms and applications become more and popular in the healthcare sector, only few institutions have an operational AI strategy. Identifying the best suited processes for ML algorithm implementation and adoption is a big challenge. Also, raising human confidence in AI systems is elementary to building trustworthy, socially beneficial and responsible AI. A commonly agreed AI auditing framework that provides best practices and tools could help speeding up the adoption process. In this paper, we first highlight important concepts in the field of AI auditing and then restructure and subsume them into an ML auditing core criteria catalog. We conducted a scoping study where we analyzed sources being associated with the term “Auditable AI” in a qualitative way. We utilized best practices from Mayring (2000), Miles and Huberman (1994), and Bortz and Döring (2006). Based on referrals, additional relevant white papers and sources in the field of AI auditing were also included. The literature base was compared using inductively constructed categories. Afterwards, the findings were reflected on and synthesized into a resulting ML auditing core criteria catalog. The catalog is grouped into the categories: Conceptual Basics, Data & Algorithm Design and Assessment Metrics. As a practical guide, it consists of 30 questions developed to cover the mentioned categories and to guide ML implementation teams. Our consensus-based ML auditing criteria catalog is intended as a starting point for the development of evaluation strategies by specific stakeholders. We believe it will be beneficial to healthcare organizations that have been or will start implementing ML algorithms. Not only to help them being prepared for any upcoming legally required audit activities, but also to create better, well-perceived and accepted products. Potential limitations could be overcome by utilizing the proposed catalog in practice on real use cases to expose gaps and to further improve the catalog. Thus, this paper is seen as a starting point towards the development of a framework, where essential technical components can be specified.

This paper undertakes a comprehensive investigation that surpasses the conventional examination of signal enhancement techniques and their effects on visual Simultaneous Localization and Mapping (vSLAM) performance across diverse scenarios. Going beyond the conventional scope, the study extends its focus towards the seamless integration of signal enhancement techniques, aiming to achieve a substantial enhancement in the overall vSLAM performance. The research not only delves into the assessment of existing methods but also actively contributes to the field by proposing innovative denoising techniques that can play a pivotal role in refining the accuracy and reliability of vSLAM systems. This multifaceted approach encompasses a thorough exploration of the intricate relationships between signal enhancement, denoising strategies, their cumulative impact on the performance of vSLAM in real-world applications and the innovative use of Generative Adversarial Networks (GANs) for image inpainting. The GANs effectively fill in missing spaces following object detection and removal, presenting a novel state-of-the-art approach that significantly enhances overall accuracy and execution speed of vSLAM. This paper aims to contribute to the advancement of vSLAM algorithms in real-world scenarios, demonstrating improved accuracy, robustness, and computational efficiency through the amalgamation of signal enhancement and advanced denoising techniques.

The rapid advancement of Large Language Models (LLMs) has significantly enhanced the capabilities of text generators. With the potential for misuse escalating, the importance of discerning whether texts are human-authored or generated by LLMs has become paramount. Several preceding studies have ventured to address this challenge by employing binary classifiers to differentiate between human-written and LLM-generated text. Nevertheless, the reliability of these classifiers has been subject to question. Given that consequential decisions may hinge on the outcome of such classification, it is imperative that text source detection is of high caliber. In light of this, the present paper introduces DeepTextMark, a deep learning-driven text watermarking methodology devised for text source identification. By leveraging Word2Vec and Sentence Encoding for watermark insertion, alongside a transformer-based classifier for watermark detection, DeepTextMark epitomizes a blend of blindness, robustness, imperceptibility, and reliability. As elaborated within the paper, these attributes are crucial for universal text source detection, with a particular emphasis in this paper on text produced by LLMs. DeepTextMark offers a viable “add-on” solution to prevailing text generation frameworks, requiring no direct access or alterations to the underlying text generation mechanism. Experimental evaluations underscore the high imperceptibility, elevated detection accuracy, augmented robustness, reliability, and swift execution of DeepTextMark.

The current expansion of renewable energies, restructuring in power systems, and high tendency to preserve the privacy of energy customers could lead to significant changes in distribution systems. Accordingly, studies on local markets have introduced a suitable framework for energy trading known as the Peer-to-Peer (P2P) market in distribution systems. Several research works have been done on the P2P market modeling and optimization in recent years. This paper aims to develop an appropriate P2P market framework in which the distribution system operator (DSO) can rerun the P2P market and maneuver in the case of the network failure. Respectively, in case of any grid failure, the P2P market can be adjusted according to new conditions of the grid, which is not studied in the previously developed methodologies. In addition to enabling the P2P framework to be usable when a network failure occurs in the system, this paper proposes a reliability cost model associated with participated sellers in the P2P market, which enables buyers to consider the reliability of the sellers in their decisions. Consequently, the developed scheme facilitates the reliable operation of distribution systems in a distributed manner. Finally, the developed structure is simulated on a 33-bus test system to analyze its effectiveness in operating the system.

Modern cyber-physical systems are becoming increasingly complex to model, thus motivating data-driven techniques such as reinforcement learning (RL) to find appropriate control agents. However, most systems are subject to hard constraints such as safety or operational bounds. Typically, to learn to satisfy these constraints, the agent must violate them systematically, which is computationally prohibitive in most systems. Recent efforts aim to utilize feasibility models that assess whether a proposed action is feasible to avoid applying the agent’s infeasible action proposals to the system. However, these efforts focus on guaranteeing constraint satisfaction rather than the agent’s learning efficiency. To improve the learning process, we introduce action mapping, a novel approach that divides the learning process into two steps: first learn feasibility and subsequently, the objective by mapping actions into the sets of feasible actions. This paper focuses on the feasibility part by learning to generate all feasible actions through self-supervised querying of the feasibility model. We train the agent by formulating the problem as a distribution matching problem and deriving gradient estimators for different divergences. Through an illustrative example, a robotic path planning scenario, and a robotic grasping simulation, we demonstrate the agent’s proficiency in generating actions across disconnected feasible action sets. By addressing the feasibility step, this paper makes it possible to focus future work on the objective part of action mapping, paving the way for an RL framework that is both safe and efficient.

Urdu, being a common language in South Asia, has not received significant attention in terms of language processing compared to more advanced languages. In the field of Natural Language Processing (NLP), the task of text summarization holds great importance due to its ability to comprehend textual content and generate concise summaries. Text summarization can be either extractive or abstractive in nature. While considerable efforts have been made to advance extractive summarization techniques, the limitations associated with it have been extensively explored and explained in the paper. However, the domain of abstractive summarization for the Urdu language remains largely unexplored. The challenges and underlying factors that have impeded progress in this domain have also been addressed. This paper specifically focuses on abstractive summarization of the Urdu language using supervised learning. To accomplish this, a labeled dataset consisting of Urdu text and its abstractive summaries is required. A dataset of Urdu text and its corresponding abstractive summaries has been prepared for the purpose of supervised learning. Additionally, the paper presents the results of summary generation, measured in terms of a rough score. Transformer’s encoder-decoder network was employed to generate abstractive summaries in Urdu, yielding a ROUGE-1 score of 25.18 in Urdu text summarization. Moreover, a novel evaluation metric called the “disconnection rate” has been introduced as a context-aware evaluation metric to enhance the assessment of a summary, known as the Context Aware RoBERTa Score.

Three-phase, three-level Vienna rectifiers are widely used in power supplies for the advantages of high power density, low switching voltage stress and low grid side current harmonics. However, due to the presence of positive and negative sequence components under the unbalanced weak power grid, the control structure using the traditional proportional integral (PI) control is complicated and requires decoupling and multiple coordinate transformations, and it has longer dynamic response time. This paper presents an unbalanced current control strategy that combines improved sliding mode control (SMC) with new reaching law and multi proportional resonance (MPR) control of Vienna rectifier under unbalanced weak power grid. This paper establishes the mathematical model of Vienna rectifier under unbalanced weak current network. In the $\alpha \beta $ coordinate system, dual second-order generalized integrator (DSOGI) is used to separate the positive and negative sequence components of the voltage. Suitable sliding mode surfaces and sliding mode convergence laws were designed. Finally, the results of simulations and experiments were used to verify the effectiveness of the proposed method.

Electrical Multiple Units (EMUs) need to undergo services like repairing, cleaning and wheel turning according to a train shunting and service scheduling (SSS) plan at the high-speed railway depot. Disturbances in the timetable at the station and overtime services affect the original SSS plan. Therefore, frequent modifications to the original SSS plan and rescheduling of some EMUs are required to recover the feasibility of the plan. This paper considers a rescheduling method for integrating train shunting, service scheduling and routing at EMU depots with various layouts. In this paper, the movement process of EMUs at depots is represented with the help of a two-layer time-space network. A heuristic algorithm for rescheduling is designed. Case studies based on a real-world depot show that the heuristic can repair the original SSS plan in 180 seconds while ensuring that the new SSS plan is as close as possible to the original SSS plan.

This paper showcases a systematic procedure to design the most efficient re design of a commercially available induction machine to be used as a traction motor in an Electric Freight Locomotive for the rail journey from Tehran to Mashhad. Using Taguchi optimization, copper and aluminium wound versions of the machine are optimized for an 1870-ton load locomotive with a peak power of 1203kW. The driving cycle is summarized using the K-means clustering method to obtain representative points. Full details of the drive cycle and guiding rail adhesion modelling are given and subsequently used in a sequential multi-physics and multi-operation mode robust procedure which optimizes the geometry and cooling parameters whilst taking manufacturing tolerances into account. The paper thus presents a thorough comparative study of aluminium-based and copper-based designs to meet a realistic Electric Freight Locomotive specification. Results indicated that the proposed approach is effective in optimum and robust design of conventional and aluminium induction machines and can be applied to other machine types operating on other rail journeys. Switching to aluminium is one way of improving the recyclability of motors, but in the scenario considered here, results in a 1.1% reduction in efficiency over the driving cycle.

The Metaverse platform offers 3D immersive experiences through virtual reality, artificial intelligence, and blockchain technologies. Blockchain-based Metaverse has garnered recent interest due to its decentralization, anonymity, and other security features. Integrating blockchain into Metaverse promotes the formation of a shared ecosystem and provides digital asset tracking, privacy protection, and security in both virtual and physical environments. This paper explores the role of blockchain in creating and managing the Metaverse platform. It is intended to provide a helpful reference for researchers and practitioners in the field of the blockchain-based Metaverse. The main topics covered include the concept and characteristics of the Metaverse; its different use cases, architectures, and technologies; Metaverse as a service provider; Metaverse management; Metaverse users and data; and blockchain-based Metaverse in terms of roles, technologies, applications, and challenges. In addition, recent advances in the blockchain-based Metaverse and challenges such as scalability, interoperability, and edge resource sharing will be discussed. The paper will conclude by highlighting open research issues and future directions, including improving immersion, scalability, security, and integration with existing systems.

The energy limit would abbreviate the lifetime of sweep coverage network. Wireless charging technology might be the best way to extend the lifetime of sweep coverage networks, as sensors are required to constantly move to ensure periodic target coverage. This paper studies the sweep coverage facilitated by rechargeable sensors, which persistently navigate between target nodes and charging stations to fulfill the periodic coverage demands of the targets while ensuring they return to the charging stations before their energy runs out. This paper firstly proposes a general definition of the Chargeable Sweep Coverage (CSC) problem, and studies the CSC problem by examining various constraints. We also propose one CSC problem subject to a special constraint where the sensors must return to their initial charging stations to recharge. The CSC problem under constrain is also NP-hard. In this paper, the problem is modeled as Maximum nodes inclusion problem with unbound candidate paths. We discuss the effective paths of the problems by situation, bound the candidate paths to certain lengths using discretization technology, and employ existing approximation algorithms to get the approximation solutions. When the charging period is greater than the sweep period, we get a $\frac {2}{5}$ -approximation algorithm. For the opposite, we get $\frac {1}{6}$ -approximation algorithms for it. The validity and scalability of the proposed algorithms are proved by theoretical proofs and experimental simulations.

In this paper, a fractional order active (P) and reactive (Q) power controller of doubly fed induction generator (DFIG) is developed and it performance is demonstrated using hardware in loop (HIL) testing with Opal RT OP4510 real-time platform. The DFIG in this paper is modelled using real-time State Space Nodal (SSN) based solver. The Rotor Side Converter (RSC) and doubly fed induction machine are implemented using Opal-RT’s ARTEMiS – nodal admittance-based SSN solver which uses smaller time steps than the conventional algorithms to facilitate real-time hardware-in-loop performance analysis of the system. The MATLAB-based FOMCON platform is utilized for designing the proposed fractional order controller. The proposed controller is developed and tested for various dynamic conditions. The performance of conventional proportional integral control and proposed fractional order control using Opal-RT’s OP4510 real-time platform is compared. The experimental results demonstrate improved efficiency with the use of proposed fractional order controller.

To support dramatically increasing services from internet of thing (IoT) devices with the sporadic and fluctuated generation of short packet traffic, this paper investigates joint dynamic time division duplexing (TDD) and radio resource control (RRC) connection management in a single-cell massive IoT network. Specifically, under the grant-free transmission incurring packet collision, this study models the factors affecting the time resource utilization (TRU) and energy consumption of IoT devices as a comprehensive system utility and further formulates the problem as a decision-making process aiming for balancing the long-term average TRU and energy consumption. To address the formulated problem, based on the deep reinforcement learning framework, this paper designs an experience-driven joint dynamic TDD and RRC connection management scheme that intelligently i) determines the TDD configuration based on the most recent downlink (DL)/uplink (UL) traffic demands and ii) adjusts the RRC state of each IoT device to control the maximum number of transmitting IoT devices. Finally, trace-driven simulation results demonstrate that the proposed scheme outperforms existing benchmarks, such as Static TDD and Dynamic TDD, in terms of transmission success ratio difference (TSRD) (up to 89% reduction), time resource utilization (TRU) (up to $17\times $ increase), and energy consumption (up to 70% reduction) of IoT devices.

Sixth-generation (6G) communication is emerging as a seamless and massive connection of almost all digital devices. Vehicles, which are extensively linked with human mobility, must keep up with a certain technological pace to maintain compatibility with the 6G era. 6G is also expected to potentially revolutionize Vehicle-to-Everything (V2X) communication. However, this modernization will surface several security challenges in the complex heterogeneous architecture of V2X communication in 6G. Similarly, the expansion of V2X is expected to introduce its own unconventional security risks and vulnerabilities. Therefore, the current paper aims to provide an overview of the security challenges associated with and solutions for V2X communication in the upcoming 6G era to visualize the future of this research domain. This paper discusses the architecture and standards utilized in 6G-enabled V2X communications and provisions a comprehensive analysis of V2X security in the Confidentiality, Integrity, Availability, Authentication, and Access Control (CIA3) domains. Through this process, we analyze the impact of the emerging technological concepts of Blockchain and Federated Learning (FL) in 6G-enabled V2X communication. Therefore, we propose a Blockchain-enabled FL-based generic security architecture for V2X communication in 6G networks. Ultimately, this review highlights the key lessons that have been learned and the future research directions in the domain of security of V2X communications in the 6G including Privacy in 3D Fog Computing, Privacy in Augmented Reality, Secure Software Defined Networking (SDN), Physical Layer Security in the THz Spectrum, SUMO (Simulation of Urban MObility), and Intrusion Detection using AI.

The rise of Internet of Things (IoT) has led to increased security risks, particularly from botnet attacks that exploit IoT device vulnerabilities. This situation necessitates effective Intrusion Detection Systems (IDS), that are accurate, lightweight, and fast (having less inference time), designed particularly to detect botnet attacks in resource constrained IoT devices. This paper proposes SkipGateNet, a novel deep learning model designed for detecting Mirai and Bashlite botnet attacks in resource constrained IoT and fog computing environments. SkipGateNet is a lightweight, fast model combining 1D-Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) layers. The novelty of this model lies in the integration of ‘Learnable Skip Connections’. These connections feature gating mechanisms that enhance detection by focusing on relevant features and ignoring irrelevant ones. They add adaptability to the architecture, performing feature selection and propagating only essential features to deeper layers. Tested on the N-BaIoT dataset, SkipGateNet efficiently detects ten types of botnet attacks, with a remarkable test accuracy of 99.91%. It is also compact (2596.87 KB) and demonstrates a quick inference time of 8.0 milliseconds, suitable for real-time implementation in resource-limited settings. While evaluating its performance, parameters like precision, recall, accuracy, and F1 score were considered, along with statistical reliability measures like Cohen’s Kappa Coefficient and Matthews Correlation Coefficient. These highlight its reliability and effectiveness in IoT security challenges. The paper also compares SkipGateNet to existing models and four other deep learning architectures, including two sequential CNN architectures, a simple CNN+LSTM architecture, and a CNN+LSTM with standard skip connections. SkipGateNet surpasses all in accuracy and inference time, demonstrating its superiority in addressing IoT security issues.

The Single Instruction Multiple Data (SIMD) architecture, supported by various high-performance computing platforms, efficiently utilizes data-level parallelism. The SIMD model is used in traditional CPUs, dedicated vector systems, and accelerators such as GPUs, vector extensions, and Xeon Phi. It provides performance throughput in computation-intensive and data-parallel applications. Despite the similarity of data-processing principles between these architectures, porting various programming models between the reviewed platforms is challenging. Furthermore, enhancing the programmability of these architectures is an important feature for utilizing their emerging computing power and simplifying programming complexity. This paper reviews the basic principles of optimization techniques to run asynchronous Multiple Instruction Multiple Data (MIMD) on SIMD accelerators. It also surveys several GPU programming paradigms and application programming interfaces (APIs) and classifies these frameworks into different groups based on their criteria. In addition, a review of studies that performed a comparison of the collaborative execution of GPUs with CPUs and Xeon Phi is presented in this paper. This study will be beneficial for developers and researchers in the field of computer architecture and parallel computing of intensive scientific applications, specifically for early-stage high-performance computing researchers, to obtain a brief overview of performance optimization opportunities as well as the challenges of existing SIMD platforms.

The recently emerged symbol-level precoding (SLP) technique is a promising solution in multi-user wireless communication systems due to its ability to transform harmful multi-user interference (MUI) into useful signals, thereby improving system performance. Conventional symbol-level precoding designs have a significant computational complexity that makes their practical implementation difficult and imposes excessive computational complexity on the system. To deal with this problem, we suggest a new deep learning (DL) based approach that utilizes low-complexity designs of symbol-level precoding. This paper focuses on DL-based one-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where one-bit digital-to-analog converters (DACs) are used to reduce cost and power. Unlike previous works, the optimized one-bit precoder for multiuser massive MIMO system (HDL-O1PmMIMO) for a wide range of signal-to-noise-ratio (SNR) has a low computational complexity, making it suitable for real precoding scenarios. In this paper, we first design an unsupervised DL-based precoder (UDL-O1PmMIMO) to address the low SNR scenarios, using which we then design a hybrid DL-based precoder (HDL-O1PmMIMO) to address both low and high SNR scenarios. The method suggested in this article utilizes a novel residual DL network structure, which helps overcome the problem of training very deep networks. Additionally, a novel customized cost function, specifically for one-bit precoding in massive MIMO systems, is introduced to optimize the performance of the system in handling interference. The results of an experiment conducted on a general test set using Python and MATLAB show that the proposed approach outperforms existing methods in three aspects: it has a lower bit error rate, it takes less time to generate the precoded vector, and it is more resistant to imperfect channel estimation.

Environment perception using camera, radar, and/or lidar sensors has significantly improved in the last few years because of deep learning-based methods. However, a large group of these methods fall into the category of supervised learning, which requires a considerable amount of annotated data. Due to uncertainties in multi-sensor data, automating the data labeling process is extremely challenging; hence, it is performed manually to a large extent. Even though full automation of such a process is difficult, semi-automation can be a significant step to ease this process. However, the available work in this regard is still very limited; hence, in this paper, a novel semi-automatic annotation methodology is developed for labeling RGB camera images and 3D automotive radar point cloud data using a smart infrastructure-based sensor setup. This paper also describes a new method for 3D radar background subtraction to remove clutter and a new object category, GROUP, for radar-based object detection for closely located vulnerable road users. To validate the work, a dataset named INFRA-3DRC is created using this methodology, where 75 % of the labels are automatically generated. In addition, a radar cluster classifier and an image classifier are developed, trained, and tested on this dataset, achieving accuracy of 98.26% and 94.86%, respectively. The dataset and Python scripts are available at https://fraunhoferivi.github.io/INFRA-3DRC-Dataset/.

A novel acquisition scheme of pseudo-noise (PN) codes is proposed for spreading satellite communication systems relying on the proposed multi-voter model of this paper. Based on the proposed model, the acquisition of PN codes can be attributed to a voting and selecting mechanism to pick out the erroneous chips. Although message passing algorithm (MPA) is a feasible algorithm for decoding PN codes, MPA does not get good detection performance due to the limited number of parity check equations in the acquisition scheme. To overcome the negative impact of the limited number of parity check equations, this paper proposes single-voter and multi-voter models, which combine chip and sequence estimation on the basis of the chip-flipping (CF). It is known that the bit-flipping (BF), weighted-bit-flipping (WBF) and other algorithms based on BF are credible for low density parity check (LDPC) codes. Because of the lack of research, these algorithms have not been extended to the detection of PN codes. As the same as BF, the inputs of the proposed CF algorithm are the hard-decision samples. For the proposed CF, there exists an optimal flipping-threshold which is similar to the random weight of the WBF. Owing to the low computation complexity of CF, the unlimited number of parity check equations can be enabled in the voting model. The experimental results show that the detection performance of the proposed method of $N > $ 15 is improved by 2 dB compared with MAP-based method and 4 dB compared with LEAP-based method at the detection probability 99%.

This paper proposes a Bayesian neural network method for predicting equipment operational trends based on a channel attention mechanism. Traditional time series prediction methods have limitations in handling complex data and nonlinear relationships. To enhance prediction accuracy and stability, the paper introduces a channel attention mechanism to capture crucial features and contextual information within the data. This mechanism automatically adjusts the weights of feature channels to focus on the influence of key features. By leveraging the advantages of Bayesian neural networks, the model undergoes multiple updates and adjustments while considering uncertainty factors, progressively improving the predictive outcomes. In experiments, the paper utilizes power transformer data from a Kaggle public dataset and a substantial amount of temporary facility equipment data from the Winter Olympics site, comparing the performance against other commonly used prediction methods. Results demonstrate the significant superiority of the Bayesian neural network method with channel attention mechanism in equipment trend prediction, outperforming traditional time series models and other commonly used methods.

It has been recently demonstrated by Bellini’s group that macroscopic states, such as coherent states, can be entangled by the delocalized photon addition. Deymier’s group has shown that phase bits (phi-bits) gates implemented by employing the topological acoustics (TA) principles can be used to implement the TA-based quantum computing analogs. This motivates us to revisit our previous papers where we have already described how to implement the universal quantum gates in integrated optics using optical hybrid, directional coupler, Mach-Zehnder interferometer, and periodically poled lithium niobate (PPLN) waveguides, but in a different context. In this paper, we describe how to implement the universal set of quantum gates classical analogs in integrated optics by employing classical polarization states derived from classical coherent states. The main problem for integrated optics implementation on a single photon level has been to implement the controlled-phase gate because the existing optical nonlinear devices where incapable of introducing the ${\pi }$ rad phase shift on a single photon level through the Kerr effect, which is not a problem at all when the classical polarization states are used instead. We also describe how to implement quantum qudit analogs based on orbital angular momentum (OAM) states and corresponding qudit gates. To highlight the importance of the proposed concepts, we experimentally demonstrate the controlled-phase gate analog operation between the classical coherent states.

This paper presents a set of benchmarks to evaluate the performance of Fuzzy Rule Interpolation (FRI) methods under various challenging conditions. FRI methods are widely used for handling sparse fuzzy rule bases and reducing decision complexity. Despite lacking overlap with the antecedents of any rule in the rule bases, FRI can still produce a conclusion. To unify the requirements of FRI methods, several conditions have been proposed. Among these, the convex and normal fuzzy set condition and the Piece-wise linearity condition are the most common. In this paper, we introduce new benchmark scenarios for testing FRI methods. These benchmarks aim to serve as a reference for evaluating and comparing the accuracy and effectiveness of FRI methods. By using these benchmarks, researchers can compare different FRI methods and identify areas for improvement in the field of fuzzy inference.

This paper studies the trajectory tracking control problem of underactuated surface vessels (USVs) under false data injection attacks (FDIAs). We propose a concise nonlinear finite-time disturbance observer (FTDO) to compensate composite disturbances caused by FDIAs and external disturbances. An online approximator is designed using adaptive techniques to compensate for the dynamic uncertainties of the system. In addition, we consider the limit of input saturation at the control input. Under the framework of the backstepping method, this paper designs a practical finite-time control (FTC) method and performs a rigorous theoretical analysis of it through the Lyapunov stability theory. Finally, the effectiveness of the proposed control scheme was verified through simulation. This research provides a feasible solution to the trajectory tracking problem of underactuated vessels in FDIAs environment and provides theoretical support for further research in related fields.

Currently, in the semiconductor market, reliability response to Electro Static Discharge (ESD) situations is being discussed as an alternative to internal Integrated Circuit (IC) destruction. In this paper, the ESD protection circuit design integrated into the Low Drop Out (LDO) regulator presents an alternative to the reliability of the internal IC. Additionally, the LDO regulator proposed in this paper is designed to minimize output voltage changes by significantly improving gain using a high gain buffer structure. Undershoot and overshoot were effectively regulated by utilizing a high gain buffer. Under the conditions of an input voltage range of 3.3–4.5 V, a maximum load current of 400 mA, and an output voltage of 3 V, the output of the proposed LDO regulator with a high gain buffer structure kept at the undershoot and overshoot voltages of 32 mV and 36 mV, respectively. In addition, the ESD protection circuit used so far was designed with only a single diode, but the proposed ESD protection circuit is a new structure designed based on Silicon Controlled Rectifier (SCR). The proposed ESD protection circuit has an incomparable area efficiency compared to diodes.

To identify a nanocomposite material capable of mitigating surface charge accumulation issues in silicone rubber composite insulators, this paper investigated the surface charge dissipation characteristics of micron-sized Al2O3/silicone rubber composites with different weight percentages under positive dc voltage. Furthermore, this paper investigated the influence of the uncharged and charged silicone rubber on the surface flashover voltage under positive dc voltage. The result show that the addition of micron-sized Al2O3 particles effectively ameliorates the severity of surface charge accumulation. In comparison to pure silicone rubber, the reduction in maximum surface charge density and average surface charge density of different weight percentage Al2O3/silicone rubber composites ranges from 18.60% to 50.60% and from 33.26% to 57.53%, respectively. When there is no surface charge on the silicone rubber surface, the addition of micron-sized Al2O3 particles at different weight percentages results in a decrease in positive dc surface flashover voltage. Conversely, when a positive surface charge is applied to the silicone rubber surface, the greatest reduction in positive dc surface flashover voltage occurs in pure silicone rubber. Furthermore, the positive DC flashover voltage of pure silicone rubber can recover to 86.53% of its value without applied surface charge at a decay time of 60 minutes. In contrast, the addition of micron-sized Al2O3 particles at different weight percentages diminishes the impact of surface charge on the surface flashover voltage, allowing it to recover to 92.03% to 94.18% of its value without applied surface charge.

The most versatile resource for storing energy is one that can rapidly charge or discharge while supporting the use of renewable energy. As renewable energy sources advance rapidly, batteries play a pivotal role in this progress. When integrating battery energy storage into a renewable energy system, it’s crucial to address the issue of battery degradation while implementing operational strategies. Furthermore, since solar irradiation varies due to changing cloud conditions, it can impact how batteries charge and discharge. This study focuses on investigating battery degradation and lifetime. Experimental work is being conducted with lead acid batteries connected to a solar photovoltaics system. The paper provides a detailed investigation of commonly used methods for predicting battery lifespan. It also analyzes aspects such as the effects of depth of discharge (DoD) and battery charge/discharge on temperature changes due to degradation. Using the coarse average approach, global battery aging, weighted Ah aging method, and RFC method, this paper estimates the DoD, temperature, life cycle loss (%), and lifespan and evaluates the extent of battery degradation. The battery lifespan is estimated using this method to be 8.42, 8.72, 8.33, and 8.93 years, respectively.

The increasing demand for wind power requires more frequent inspections to identify defects in the Wind Turbine Blades (WTBs). These defects, if not detected, can compromise the structural integrity and safety of wind turbines. As WTBs are crucial and costly components, they may suffer material degradation and fatigue failure, which affects their performance and safety. Thus, the urgency for efficient and regular monitoring to maintain their structural integrity is greater than ever. This review paper explores innovative methods in fatigue testing, damage detection, and structural reliability in WTBs, focusing on the use of recent inspection methods, including those that take advantage of drones. Drones are used to identify defects such as cracks, erosion, and coating irregularities using text high-resolution imagery with the onboard cameras. Various investigators have developed novel data-driven approaches, incorporating machine learning and deep learning, to accurately identify these defects. Although deep text learning-based image processing has been successful in other public infrastructure contexts, its application to wind turbine inspection from aerial images presents unique challenges. This paper also highlights the critical role of failure inspection in enhancing the operational integrity of WTBs, showcasing state-of-the-art deep learning techniques that are pivotal for identifying and analyzing failures in WTBs from images captured by drones. The paper provides insights into the latest developments in using drone imagery for blade defect detection, contrasting this method with traditional non-destructive techniques. This approach could significantly transform the wind energy industry by offering a more efficient, automated, and precise way of ensuring the structural health of wind turbines. Unlike previous studies that predominantly focus on isolated aspects such as inspection or fatigue, this review paper not only integrates the three major aspects of WTBs integrity in terms of aerial inspection, image processing using machine learning, and structural integrity of the blade but also undertakes an extensive examination of the prevailing methodologies in the field, pinpointing crucial gaps and challenges. It provides a detailed review of existing research, covering various areas including automated inspection, image processing techniques, fatigue analysis, and the reliability of wind turbines. This approach enriches the discourse by offering a multifaceted perspective on WTB maintenance, thereby advancing the understanding of operational integrity within the field of wind energy.