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This research paper proposes the new multi-criteria search based enhanced firefly algorithm for solving selective harmonic elimination in a multilevel inverter. This new enhanced firefly utilizes adaptive nature of social and cognitive components to find the global optima. To see the effectiveness of the proposed algorithm and for the evaluation of results, a three phase nine level cascaded multilevel inverter is used. It is compared with existing meta-heuristic algorithms namely particle swarm optimization and firefly algorithm to validate its effectiveness. Crucial parameters for optimization, including population size and number of iterations, are kept same for comparison. For comparison, total harmonic distortion and convergence behaviour of algorithms against various modulation index values are considered. Moreover, results have clearly indicated that the proposed algorithm has surpassed particle swarm optimization and firefly algorithms in terms of convergence behaviour by attaining lower fitness value in lesser number of iterations. Finally, the experimental validation of selective harmonic elimination in multi-level inverter is also performed and analyzed. 2013 IEEE. This work was supported by the Khalifa University. Scopus

Reconfigurable Intelligent Surfaces (RISs) are considered to be a key enabling technology for 6G as they can potentially provide a boost in performance with a high energy efficiency. RISs rely on the use of arrays with a large number of low-cost quasi-passive reflecting elements which can be individually tuned in order to shape the radio wave propagation. This can effectively enable the implementation of smart radio environments, increasing the capacity and improving the coverage of the system. Since most RISs related studies focus on evaluating the gains of RIS based solutions in simplified communication scenarios, in this paper we investigate the potential benefits of RISs when integrated into future wireless networks within the context of post-5G/ 6G systems. With this aim, we present an iterative algorithm for accomplishing the joint design of the access point precoder and phase-shifts of the RIS elements considering a multi-stream multiple-input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) link. Based on this approach, we then present the system-level evaluation of a RIS-aided post-5G/6G network deployment operating in two different bands, mmWave and sub-THz, and which considers both near-field and far-field propagation models. The results obtained in two different environments namely, Indoor Open Office (IOO) and Urban Micro Truncated (UMT), show that the adoption of the proposed RIS-based approach can effectively improve the throughput and coverage area. info:eu-repo/semantics/publishedVersion

Instance selection is a vital technique for energy big data analytics. It is challenging to process a massive amount of streaming data generated at high speed rates by intelligent monitoring devices. Instance selection aims at removing noisy and bad data that can compromise the performance of data-driven learners. In this context, this paper proposes a novel similarity based instance selection (SIS) method for real-time phasor measurement unit data. In addition, we develop a variant of the Hoeffding-Tree learner enhanced with the SIS for classifying disturbances and cyber-attacks. We validate the merits of the proposed learner by exploring its performance under four scenarios that affect either the system physics or the monitoring architecture. Our experiments are simulated by using the datasets of industrial control system cyber-attacks. Finally, we conduct an implementation analysis which shows the deployment feasibility and high-performance potential of the proposed learner, as a part of real-time monitoring applications., 10 pages, 10 figures, 3 tables

The paper introduces a straightforward procedure for estimating the electrical parameters of a simple, but reasonably accurate, two-branches model of a supercapacitor (SC). The equivalent electrical circuit model includes the voltage and frequency dependence on the SC’s capacitance, neglecting the self-discharge phenomenon, so it is mainly devoted to short and mid-term simulations suitable for most industrial applications. The estimation procedure of the electrical parameters starts by analysing the experimental data achieved by a common constant-current discharge test. Such data are used to build a fitting function which is compared with the analytical solution and numerical approximations for the SC’s voltage evolution. Thus, initial estimated values of the electrical parameters are obtained through simple relations and are optimised by implementing the least squares method. The procedure is validated after an easy and fast extraction of the optimal parameters of the two-branches model of an SC. Several tests involving a commercial SC have been carried out in Simulink and the results have been compared against experimental data. A good accuracy of the two-branches model in a wide range of constant-current charging/discharging cycles is reported.

Security aspects in the household sphere have become a major concern in modern societies. In particular, regardless of the technology used, users increasingly appreciate a protection system to prevent material damage in the case of human errors or distractions during the cooking process. This paper presents a sensorless method for detecting and limiting overtemperature, unique to induction cooktops, based on their specific features, such as automatic pot detection and load power factor estimation. The protection system exploits the change in the load material properties at certain temperatures, the effect of which may be enhanced by arranging a multilayer structure comprising a low Curie temperature alloy and an aluminum layer. The proposed multilayer load exhibits two differentiated states: a normal state, where the cookware is efficiently heated, and a protection state, above the safety temperature, where the power factor abruptly decreases, limiting the overheating and making the state easily detectable by the cooktop. This method of overtemperature self-protection uses the electronics of conventional induction cooktops; therefore, no other sensors or systems are required, reducing its complexity and costs. Simulation and experimental results are provided for several cookware designs, thereby proving the feasibility of this proposal.

In the last few years, chatbots have become mainstream solutions adopted in a variety of domains for automatizing communication at scale. In the same period, knowledge graphs have attracted significant attention from business and academia as robust and scalable representations of information. In the scientific and academic research domain, they are increasingly used to illustrate the relevant actors (e.g., researchers, institutions), documents (e.g., articles, patents), entities (e.g., concepts, innovations), and other related information. Following the same direction, this paper describes how to integrate conversational agents with knowledge graphs focused on the scholarly domain, a.k.a. Scientific Knowledge Graphs. On top of the proposed architecture, we developed AIDA-Bot, a simple chatbot that leverages a large-scale knowledge graph of scholarly data. AIDA-Bot can answer natural language questions about scientific articles, research concepts, researchers, institutions, and research venues. We have developed four prototypes of AIDA-Bot on Alexa products, web browsers, Telegram clients, and humanoid robots. We performed a user study evaluation with 15 domain experts showing a high level of interest and engagement with the proposed agent.

Currently, the offline manual periodic detection method is a well-established practice in detecting the thermal state of the converter heatsink. This method, however, is huge in maintenance costs. To lower maintenance costs and improve maintenance efficiency in detecting the thermal dissipation state, this paper proposes an intelligent online prediction scheme based on Gauss-Newton iteration method. Firstly, the power loss model of the power module is established according to the characteristics of IGBT and FWD. The power loss of the power device is then calculated in real time with the voltage and current parameters of the converter. Next, the transient thermal model of the heatsink is established based on thermodynamics theory. And the calculation method of steady thermal resistance of heatsink based on Gauss-Newton iteration method is proposed according to the model. The transient thermal impedance data allow for timely prediction of thermal resistance of the heatsink and characterize the thermal state of the heatsink. Finally, with the help of DSP28377D, an experimental platform is built to verify the scheme. Results show that this method can realize intelligent prediction of thermal state online

Natural language processing technology has made significant progress in recent years, fuelled by increasingly powerful general language models. This has also inspired a sizeable body of work targeted specifically towards the educational domain, where the creation of questions (both for assessment and practice) is a laborious/expensive effort. Thus, automatic Question-Generation (QG) solutions have been proposed and studied. Yet, according to a recent survey of the educational QG community's progress, a common baseline dataset unifying multiple domains and question forms (e.g., multiple choice vs. fill-the-gap), including readily available baseline models to compare against, is largely missing. This is the gap we aim to fill with this paper. In particular, we introduce a high-quality dataset in the educational domain, containing over 3,000 entries, comprising (i) multiple-choice questions, (ii) the corresponding answers (including distractors), and (iii) associated passages from the course material used as sources for the questions. Each question is phrased in two forms, normal and cloze (i.e., fill-the-gap), and correct answers are linked to source documents with sentence-level annotations. Thus, our versatile dataset can be used for both question and distractor generation, as well as to explore new challenges such as question format conversion. Furthermore, 903 questions are accompanied by their cognitive complexity level as per Bloom's taxonomy. All questions have been generated by educational experts rather than crowd workers to ensure they are maintaining educational and learning standards. Our analysis and experiments suggest distinguishable differences between our dataset and commonly used ones for question generation for educational purposes. We believe this new dataset can serve as a valuable resource for research and evaluation in the educational domain. The dataset and baselines are made available to support further research in question generation for education (https://github.com/hadifar/question-generation).

Color constancy is an important part of the human visual system, as it allows us to perceive the colors of objects invariant to the color of the illumination that is illuminating them. Modern digital cameras have to be able to recreate this property computationally. However, this is not a simple task, as the response of each pixel on the camera sensor is the product of the combination of spectral characteristics of the illumination, object, and the sensor. Therefore, many assumptions have to be made to approximately solve this problem. One common procedure was to assume only one global source of illumination. However, this assumption is often broken in real-world scenes. Thus, multi-illuminant estimation and segmentation is still a mostly unsolved problem. In this paper, we address this problem by proposing a novel framework capable of estimating per-pixel illumination of any scene with two sources of illumination. The framework consists of a deep-learning model capable of segmenting an image into regions with uniform illumination and models capable of single- illuminant estimation. First, a global estimation of the illumination is produced, and is used as input to the segmentation model along with the original image, which segments the image into regions where that illuminant is dominant. The output of the segmentation is used to mask the input and the masked images are given to the estimation models, which produce the final estimation of the illuminations. The models comprising the framework are first trained separately, then combined and fine-tuned jointly. This allows us to utilize well researched single-illuminant estimation models in a multi-illuminant scenario. We show that such an approach improves both segmentation and estimation capabilities. We tested different configurations of the proposed framework against other single-and multi-illuminant estimation and segmentation models on a large dataset of multi-illuminant images. On this dataset, the proposed framework achieves the best results, in both multi-illumination estimation and segmentation problems. Furthermore, generalization properties of the framework were tested on often used single-illuminant datasets. There, it achieved comparable performance with state- of-the-art single-illumination models, even though it was trained only on the multi-illuminant images.

Constructing click models and extracting implicit relevance feedback information from the interaction between users and search engines are very important to improve the ranking of search results. Using neural network to model users' click behaviors has become one of the effective methods to construct click models. In this paper, We use Transformer as the backbone network of feature extraction, add filter layer innovatively, and propose a new Filter-Enhanced Transformer Click Model (FE-TCM) for web search. Firstly, in order to reduce the influence of noise on user behavior data, we use the learnable filters to filter log noise. Secondly, following the examination hypothesis, we model the attraction estimator and examination predictor respectively to output the attractiveness scores and examination probabilities. A novel transformer model is used to learn the deeper representation among different features. Finally, we apply the combination functions to integrate attractiveness scores and examination probabilities into the click prediction. From our experiments on two real-world session datasets, it is proved that FE-TCM outperforms the existing click models for the click prediction.

Variable precision reduction (VPR) and positive region reduction (PRR) are common definitions in attribute reduction. The compacted decision table is an extension of a decision table. In this paper, we propose another extension, called the weighted decision table. In both types of decision tables, VPR is defined, and the corresponding discernibility matrices for the PRR are proposed. Then, algorithms for obtaining the PRR from the discernibility matrices are presented. In both types of decision tables, the relationship between VPR and PRR is established by comparing the corresponding discernibility matrices. If the precision of the VPR meets the given conditions, then the PRR algorithms can be used to obtain the results after modifying some decision values in the decision tables. An analysis of the modification process of the decision values and the compression process of decision tables is used to propose a new algorithm for VPR in decision tables that ensures credibility. The effectiveness of the proposed algorithm was evaluated by an experimental comparison with existing VPR algorithms.

Conventional energy sources are a major source of pollution. Major efforts are being made by global organizations to reduce CO2 emissions. Research shows that by 2030, EVs can reduce CO2 emissions by 28%. However, two major obstacles affect the widespread adoption of electric vehicles: the high cost of EVs and the lack of charging stations. This paper presents a comprehensive data-driven approach based demand-side management for a solar-powered electric vehicle charging station connected to a microgrid. The proposed approach utilizes a solar-powered electric vehicle charging station to compensate for the energy required during peak demand, which reduces the utilization of conventional energy sources and shortens the problem of fewer EVCS in the current scenario. PV power stations, commercial loads, residential loads, and electric vehicle charging stations were simulated using the collected real-time data. Furthermore, a deep learning approach was developed to control the energy supply to the microgrid and to charge the electric vehicle from the grid during off-peak hours. Furthermore, two different machine learning approaches were compared to estimate the state of charge estimation of an energy storage system. Finally, the proposed framework of the demand management system was executed for a case study of 24 hours. The results reflect that peak demand has been compensated with the help of an electric vehicle charging station during peak hours. 2013 IEEE. This publication was made possible by Qatar University Research grant# [QUCP-CENG-2020-2] from Qatar University, Qatar. The APC for the article is funded by the Qatar National Library, Doha, Qatar. Scopus

Increase in chip inactivity in the future threatens the performance of many-core systems and therefore, efficient techniques are required for continuous scaling of transistors. As of a result of this challenge, future proposed many-core system designs must consider the possibility of a 50% functioning chip per time as well maintaining performance. Fortunately, this 50% inactivity can be increased by managing the temperature of active nodes and the placement of the dark nodes to leverage a balance working chip whilst considering the lifetime of nodes. However, allocating dark nodes inefficiently can increase the temperature of the chip and increase the waiting time of applications. Consequently, due to stochastic application characteristics, a dynamic rescheduling technique is more desirable compared to fixed design mapping. In this paper, we propose an Ageing Before Temperature Electromigration-Aware, Negative Bias Temperature Instability (NBTI) & Time-dependent Dielectric Breakdown (TDDB) Neighbour Allocation (ABENA 2.0), a dynamic rescheduling management system which considers the ageing and temperature before mapping applications. ABENA also considers the location of active and dark nodes and migrate task based on the characteristics of the nodes. Our proposed algorithm employ Dynamic Voltage Frequency Scaling (DVFS) to reduce the Voltage and Frequency (VF) of the nodes. Results show that, our proposed methods improve the ageing of nodes compared to a conventional round-robin management system by 10% in temperature, and 10% ageing

Prognosis is a challenging technology that aims to accurately predict and estimate the remaining useful life of a component or system in order to enhance its reliability and performance. Although prognosis research for predictive maintenance is a well-researched topic, practical examples of successful prognostic applications remain scarce. This is due to the lack of available run-to-failure data to build the prediction model as maintenance is usually conducted regularly to avoid significant defects. This paper proposes a novel prognosis method that can be applied to real-world railway maintenance planning without employing runto-failure data. The key idea is that the fault severity assessment and approximate remaining time prediction are often all that is needed in order to plan maintenance. Firstly, using motor current signals, a degradation indicator on railway door systems is generated based on the dynamic time warping method to measure similarity between typical normal and faulty behaviour. Then, the K-means algorithm is applied to assess fault severity, followed by the representative time estimation for each level of fault severity. This estimation thus allows the remaining time prediction until reaching the critical fault severity level without using runto-failure data. As a result, the proposed method enables predictive maintenance planning for railway door systems. In addition, the fault severity threshold can be updated by additional operational data, enabling the remaining time prediction to be more reliable. Furthermore, the proposed method can be applied to conventional railway assets and other electro-mechanical actuators as motor current signals are primarily available from the controller or motor drive without additional sensors. Engineering and Physical Sciences Research Council (EPSRC): EP/T518104/1

Software defect prediction aims to identify potentially defective software modules to better allocate limited quality assurance resources. Practitioners often do this by utilizing supervised models trained using historical data. This data is gathered by mining version control and issue tracking systems. Version control commits are linked to issues they address. If the linked issue is classified as a bug report, the change is considered as bug fixing. The problem arises from the fact that issues are often incorrectly classified within issue tracking systems. This introduces noise into the gathered datasets. In this paper, we investigate the influence issue classification has on software defect prediction dataset quality and resulting model performance. To do this, we mine data from 7 popular open-source repositories, create issue classification and software defect prediction datasets for each of them. We investigate issue classification using four different methods ; a simple keyword heuristic, an improved keyword heuristic, the FastText model and the RoBERTa model. Our results show that using the RoBERTa model for issue classification produces the best software defect prediction datasets, containing on average 14.3641% of mislabeled instances. SDP models trained on such datasets achieve superior performance, to those trained on SDP datasets created using other issue classification methods, in 65 out of 84 experiments, with 55 of them being statistically relevant. Furthermore, in 17 out of 28 experiments we could not show a statistically relevant performance difference between SDP models trained on RoBERTa derived software defect prediction datasets and those created using manually labeled issues.

This paper presents a methodology for generating geometries for interior permanent magnet (IPM) motors in electric vehicles (EVs) through the application of artificial intelligence (AI) and image processing (IP) techniques. Due to the implementation of green agreements and policies aimed at reducing greenhouse gas emissions, EVs have become popularity. As a consequence, the improvement studies on the powertrain and battery system of EVs are focused. Especially for the powertrain, design optimization studies of e-motor have increased in the literature. One of the most widely used e-motor topologies is interior permanent magnet (IPM) motor. However, designing the IPM motor presents a challenge due to the dynamic considerations with geometric constraints. Therefore, e-motor designers encounter challenges related to determining initial geometry and the long time of the optimization process. To address these challenges, a novel approach is proposed that leverages machine learning (ML) techniques in combination with IP to generate initial geometries and design parameters for IPM motors. The proposed approach generates images of the motor geometry and extract dimensional features from the resulting images by using artificial neural networks (ANNs). The proposed method performs an analysis of the input vectors to reduce their size using techniques such as Histogram, 2D Maximum, 2D Mean, 2D Minimum, 2D Standard Deviation, and 2D Variance to enhance feature extraction. Additionally, FFT (Fast Fourier Transform) and IFFT (Inverse Fast Fourier Transform) are used to improve the neural network process in generating the image geometry. Further, the generated image geometry is improved by applying digital filtering techniques such as Log, FFT, Log+FFT, Laplacian, Sobel, and Histogram Equalization. Finally, the trained ANNs are tested to validate the results by using Ansys RMXprt and Maxwell. Eventually, the proposed method represents an innovative solution to generating initial geometries for IPM motors in EVs that satisfies desired design requirements. This approach leverages the power of AI and image processing techniques, which could lead to significant improvements in the optimization process for IPM motors, accelerate the designer’s analysis process, and enhance the performance of EVs.

Fully-supervised object detection and instance segmentation models have accomplished notable results on large-scale computer vision benchmark datasets. However, fully-supervised machine learning algorithms’ performances are immensely dependent on the quality of the training data. Preparing computer vision datasets for object detection and instance segmentation is a labor-intensive task requiring each instance in an image to be annotated. In practice, this often results in the quality of bounding box and polygon mask annotations being suboptimal. This paper quantifies empirically the ground truth annotation quality and COCO’s mean average precision (mAP) performance by introducing two separate noise measures, uniform and radial, into the ground truth bounding box and polygon mask annotations forthe COCO and Cityscapes datasets. Mask-RCNN models are trained on various levels of noise measures to investigate the performance of each level of noise. The results showed degradation of mAP as the level of both noise measures increased. For object detection and instance segmentation respectively, using the highest level of noise measure resulted in a mAP degradation of 0.185 & 0.208 for uniform noise with reductions of 0.118 & 0.064 for radial noise on the COCO dataset. As for the Cityscapes datasets, reductions of mAP performance of 0.147 & 0.142 for uniform noise and 0.101 & 0.033 for radial noise were recorded. Furthermore, a decrease in average precision is seen across all classes, with the exception of the class motorcycle. The reductions between classes vary, indicating the effects of annotation uncertainty are class-dependent.

This work was supported in part by the European Union (EU)-Funded Project Airborne Data Collection on Resilient System Architectures (ADACORSA) (www.adacorsa.eu) through the Electronic Components and Systems for European Leadership (ECSEL) Joint Undertaking (JU) support from the European Union’s Horizon 2020 Research and Innovation Programme and Germany, The Netherlands, Austria, Romania, France, Sweden, Cyprus, Greece, Lithuania, Portugal, Italy, Finland, Turkey, under Agreement 876019; in part by the Spanish Ministerio de Economía y Competitividad under Project TED2021-129949A-I00; and in part by the Junta de Andalucía under Project P20_00265., Sensor fusion is a critical aspect in autonomous drone navigation as several tasks, such as object detection and self-pose estimation, require combining information from heterogeneous sources. The performance of these solutions depends on several factors, such as the characteristics of the sensors and the environment, as well as the computing platforms, which can heavily impact their accuracy and response time. Carrying out such performance evaluations through real flight tests can be a resource-demanding, time- consuming, and, at times, risky process, which is why researchers often rely on simulation environments for testing and validating sensor fusion algorithms. The simulation environment should provide photorealistic environmental features, as well as a comprehensive set of sensors, in order to allow to test the most extensive set of sensor fusion algorithms. This paper presents AirLoop, an AirSim-based flight simulator for Hardware- in-the-Loop and Software-in-the-Loop algorithm testing and validation. AirLoop extends the sensor setup provided by AirSim with an FMCW RADAR sensor simulation, which has been evaluated based on the Infineon Technologies BGT60TR13C RADAR. Furthermore, this work provides several Software-in-the- Loop (SITL) and Hardware-in-the-Loop (HITL) demonstrations, including interfacing with the Pixhawk 2 flight controller and an extensive evaluation of the communication of the engine with the NVIDIA Jetson Nano, which has been evaluated in various use cases, including dataset creation, object detection, Path Planning, and Simultaneous Localization and Mapping (SLAM)., European Union (EU)-Funded Project Airborne Data Collection on Resilient System Architectures(ADACORSA), European Commission 876019, Spanish Government TED2021-129949A-I00, Junta de Andalucia P20_00265

Latest developments in low voltage wide-bandgap semiconductor technology have increased the popularity of single-phase multi-level (ML) totem pole (TP) power-factor correction (PFC) converters. Phase shifted flying capacitor based power stages offer several advantages, such as higher input current ripple frequency, smaller size inductor and differential mode (DM) filter, and usage of low voltage Gallium Nitride (GaN) devices with better figure-of-merit. Even though it has been proven that ML TP PFC converters have good power density and efficiency, the determination of optimal voltage levels and switching frequency requires a system-level optimization. This paper proposes an optimization framework for ML TP PFC converters, taking the power stage, thermal, DM filter, and magnetic designs, as well as practical design considerations, into account to determine the voltage-levels and switching frequency that minimize the power losses, cost and volume of the total system. To process output power of 3700W, the optimization tool suggests using a 4-Level GaN TP PFC topology switched at 45 kHz. The outcome of the tool has been compared with other fixed inductor current ripple designs, as well as optimized designs for 3-Level and 5-Level TP PFC converters in terms of cost, volume, and power losses. In accordance with the optimization results, a prototype of a 4L TP PFC rated at 3700 W is designed, which achieves a peak efficiency of 99.6% at 1 kW and >99.1% efficiency under the full power range.

A fully-quantum network implies the creation of quantum entanglement between a given source node and some other destination node, with a number of quantum repeaters in between. This paper tackles the problem of quantum entanglement distribution by solving the routing problem over an infrastructure based on quantum repeaters and with a finite number of pairs of entangled qubits available in each link. The network model considers that link purification is available such that a nested purification protocol can be applied at each link to generate entangled qubits with higher fidelity than the original ones. A low-complexity multi-objective routing algorithm to find the shortest path between any two given nodes is proposed and assessed for random networks, using a fairly general path extension mechanism that can fit a large family of particular technological requirements. Different types of quantum protocols require different levels of fidelity for the entangled qubit pairs. For that reason, the proposed algorithm identifies the shortest path between two nodes that assures an end-to-end fidelity above a specified threshold. The minimum requirements for the end-to-end entanglement fidelity depend on the whole extension of the paths, and cannot be looked at as a local property of each link. Moreover, one needs to keep track not only of the shortest path, but also of longer paths holding more entangled qubits than the shorter paths in order to satisfy the fidelity criterion. Thus, standard single parameter shortest-path algorithms do not necessarily converge to the optimal solution. The problem of finding the best path in a network subject to multiple criteria (known as multi-objective routing) is, in general, an NP-hard problem due to the rapid growth of the number of stored paths. This work proposes a metric that identifies and discards paths that are objectively worse than others. By doing so, the time complexity of the proposed algorithm scales near-to-linearly with respect to the number of nodes in the network, showing that the shortest path problem in quantum networks can be solved with a complexity very close to the one of the classical counterparts. That is analytically proved for the case where all the links of a path have the same fidelity (homogeneous model). The algorithm is also adapted to a particular type of path extension, where different links along a path can be purified to different degrees, asserting its flexibility and near-to-linearity even when heterogeneous fidelities along the sections of a path are considered. info:eu-repo/semantics/publishedVersion

Cancer is one of the most challenging diseases because of its complexity, variability, and diversity of causes. It has been one of the major research topics over the past decades, yet it is still poorly understood. To this end, multifaceted therapeutic frameworks are indispensable. \emph{Anticancer peptides} (ACPs) are the most promising treatment option, but their large-scale identification and synthesis require reliable prediction methods, which is still a problem. In this paper, we present an intuitive classification strategy that differs from the traditional \emph{black box} method and is based on the well-known statistical theory of \emph{sparse-representation classification} (SRC). Specifically, we create over-complete dictionary matrices by embedding the \emph{composition of the K-spaced amino acid pairs} (CKSAAP). Unlike the traditional SRC frameworks, we use an efficient \emph{matching pursuit} solver instead of the computationally expensive \emph{basis pursuit} solver in this strategy. Furthermore, the \emph{kernel principal component analysis} (KPCA) is employed to cope with non-linearity and dimension reduction of the feature space whereas the \emph{synthetic minority oversampling technique} (SMOTE) is used to balance the dictionary. The proposed method is evaluated on two benchmark datasets for well-known statistical parameters and is found to outperform the existing methods. The results show the highest sensitivity with the most balanced accuracy, which might be beneficial in understanding structural and chemical aspects and developing new ACPs. The Google-Colab implementation of the proposed method is available at the author's GitHub page (\href{https://github.com/ehtisham-Fazal/ACP-Kernel-SRC}{https://github.com/ehtisham-fazal/ACP-Kernel-SRC}).

This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enable handoffs between beams that belong to the same or different BSs. We propose the use of long short-term memory (LSTM) recurrent neural networks with three different approaches and vary the number of lookbacks of the beam measurements to study the performance of the prediction used for the proactive beam handoff. Simulations show that while UE positions can improve the prediction performance, it is only up to a certain point. At a sufficiently large number of lookbacks, the UE positions become irrelevant to the prediction accuracy since the LSTMs are able to learn the optimal beam based on implicitly defined positions from the time-defined trajectories., (c) 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Developing a secure information processing environment highly depends on securing all the layers and devices in the environment. Edge/Fog computing environments are no exception in this case, and the security of these environments highly depends on securing Internet of Things (IoT) devices which are the most vulnerable devices throughout the environment. The adoption of Edge/Fog computing paradigms by new emerging technologies has stimulated malware development for IoT platforms. Recent attacks initiated by IoT malware show that these attacks have a tremendous impact on compromised systems in terms o the Quality of Service because of the number of infected IoT devices. In the light of these developments, there is an enormous need for efficient solutions. However, defense capabilities against these new malware types are highly constrained by the limited understanding of these new emerging paradigms and the lack of access to malware samples. This study mainly focuses on IoT malware to understand the behaviors of malware in the most vulnerable layer of the Edge/Fog computing environments. Mainly, 64 IoT malware families are identified from 2008 when the first known IoT malware emerged to October 2022. These malware families are systematically characterized by various aspects, including target architecture, target device, delivery methods, attack vectors, persistence techniques, and their evolution from existing malware. During this characterization process, two different investigation frameworks, 'Cyber Kill Chain' and 'Mitre ATT&CK for ICS,' have been adopted in the different investigation layers. This paper aims to bring light to future researches with the presented features of the IoT malware.

This paper proposes a path planning method for a nonholonomic mobile robot that takes turnabouts on a narrow road. A narrow road is any space in which the robot cannot move without turning around. Conventional path planning techniques ignore turnabout points and directions determined by environmental data, which might result in collisions or deadlocks on a narrow road. The proposed method uses the Deep Q-network (DQN) to obtain a control strategy for path planning on narrow roads. In the simulation, the robot learned the optimal velocity commands that maximized the long-term reward. The reward is designed to reach a target with a smaller change in robot velocity and fewer turnabouts. The success rate and the number of turnabouts in the simulation and experiment were used to evaluate the trained model. According to simulation and environmental data, the proposed strategy enables the robot to travel on narrow roads. Additionally, these outcomes demonstrate comparable performance on a number of roadways that are not part of the learning environments, supporting the robustness of the trained model.

It is well known that activities in running water or wind and waves expose the Autonomous Surface Vessels (ASVs) to considerable challenges. Under these conditions, it is essential to develop a robust control system that can meet the requirements and ensure the safe and accurate execution of missions. In this context, this paper presents a new topology for controller design based on a combination of the Successive Loop Closure (SLC) method and optimal control. This topology enables the design of robust autopilots based on the Proportional-Integral-Derivative (PID) controller. The controllers are tuned from the solution of the optimal control problem, which aims to minimize the effects of model uncertainties. To verify the effectiveness of the proposed controller, a numerical case study of a natural ASV with 3 Degree of Freedom (DoF) is investigated. The results show that the methodology enabled the tuning of a PID controller capable of dealing with different parametric uncertainties, demonstrating robustness and applicability for different prototype scenarios.

Most music emotion recognition approaches perform classification or regression that estimates a general emotional category from a distribution of music samples, but without considering emotional variations (e.g., happiness can be further categorised into much, moderate or little happiness). We propose an embedding-based music emotion recognition approach that associates music samples with emotions in a common embedding space by considering both general emotional categories and fine-grained discrimination within each category. Since the association of music samples with emotions is uncertain due to subjective human perceptions, we compute composite loss-based embeddings obtained to maximise two statistical characteristics, one being the correlation between music samples and emotions based on canonical correlation analysis, and the other being a probabilistic similarity between a music sample and an emotion with KL-divergence. The experiments on two benchmark datasets demonstrate the effectiveness of our embedding-based approach, the composite loss and learned acoustic features. In addition, detailed analysis shows that our approach can accomplish robust bidirectional music emotion recognition that not only identifies music samples matching with a specific emotion but also detects emotions expressed in a certain music sample., Comment: 27 pages, 14 figures, This paper has been accepted to IEEE Access

Abnormality detection and localization (ADL) have been studied widely in wireless sensor networks (WSNs) literature, where the sensors use electromagnetic waves for communication. Molecular communication (MC) has been introduced as an alternative approach for ADL in particular areas such as healthcare, being able to tackle the shortcomings of conventional WSNs, such as invasiveness, bio-incompatibility, and high energy consumption. In this paper, we introduce a general framework for MC-based ADL, which consists of multiple tiers for sensing the abnormality and communication between different agents, including the sensors, the fusion center (FC), the gateway (GW), and the external node (e.g., a local cloud), and describe each tier and the agents in this framework. We classify and explain different abnormality recognition methods, the functional units of the sensors, and different sensor features. Further, we describe different types of interfaces required for converting the internal and external signals at the FC and GW. Moreover, we present a unified channel model for the sensing and communication links. We categorize the MC-based abnormality detection schemes based on the sensor mobility, cooperative detection, and cooperative sensing/activation. We also classify the localization approaches based on the sensor mobility and propulsion mechanisms and present a general framework for the externally-controllable localization systems. Finally, we present some challenges and future research directions to realize and develop MC-based systems for ADL. The important challenges in the MC-based systems lie in four main directions as implementation, system design, modeling, and methods, which need considerable attention from multidisciplinary perspectives., Comment: *Ali Etemadi and Maryam Farahnak-Ghazani are co-first authors

Mobile device tracking technologies based on various positioning systems have made location data collection ubiquitous. The frequency at which location samples are recorded varies across applications, yet it is usually pre-defined and fixed, resulting in redundant information, and draining the battery of mobile devices. In this paper, we first answer the question “at what frequency should individual human movements be sampled so that they can be reconstructed with minimum loss of information?”. Our analysis unveils a novel linear scaling law of the localization error with respect to the sampling interval. We then present DuctiLoc, a location sampling mechanism that utilises the law above to profile users and adapt the position tracking frequency to their mobility. DuctiLoc is energy efficient, as it does not rely on power- hungry sensors or expensive computations; moreover, it provides a handy knob to control energy usage, by configuring the target positioning accuracy. Controlling the trade-off between accuracy and sampling rate of human movement is useful in a number of contexts, including mobile computing and cellular networks. Real-world experiments with an Android implementation show that DuctiLoc can effectively adjust the sampling frequency to individual mobility habits and target accuracy level, reducing the energy consumption by 60% to 98% with respect to a baseline periodic sampling. Regional Government of Madrid TRUE inpress

Ongoing advancements in the design and fabrication of soft robots are creating new challenges in modeling and control. This paper presents a dynamic Cosserat rod model for a single-section 3D-printed pneumatic soft robotic arm capable of combined stretching and bending. The model captures the manufacturing variability of the actuators by tuning the pressure-strain relation for each actuator. Moreover, it includes a simple model of the pneumatic actuation system that incorporates the transient response of proportional pressure-controlled electronic valves. The model was validated experimentally for several quasi-static and dynamic motion patterns with actuation frequencies ranging from 0.2 Hz to 20 Hz. The model reproduced the quasi-static experiments with an average tip error of 4.83% of the arm length. In dynamic conditions, the average tip error was 4.33% for stretching and bending motions, 5.64% for five motor babbling experiments, and 22.53% for three challenging sinusoidal patterns. An ablation study of the model components found that the most influential factors for the average accuracy were gravity and strain gains, followed by damping and pressure transient. This work could assist researchers in focusing on the most significant aspects for closing the real-to-sim gap when modeling pneumatic soft robotic arms.

Power systems proliferated by distributed generation sources are becoming increasingly prone to frequency and voltage disturbances. These problems are exacerbated in microgrids since they have fewer intrinsic disturbance-rejecting measures and features. To increase the reliability and stability of emerging power systems, the advanced control structures of the distributed generation sources based on power electronics devices must be deployed during suboptimal operating conditions. The aggravating circumstance is that both voltage and frequency excursion can be transient and long-lasting and consequently can occur simultaneously. The algorithm proposed in this paper integrates voltage support (nominal voltage restoration) and inertia emulation features with the comprehensive current references management scheme, thus securing improved grid operating conditions during several different faults and occurrences. The control algorithm is developed and tested in the context of a small microgrid, but it can be applied with minimal alterations in traditional grids, also. To prove that it is possible to decrease simultaneously voltage unbalances and frequency deviations, a test microgrid consisting of a synchronous generator, photovoltaic system, battery storage system, and controllable balanced and unbalanced loads was developed in a hardware-in-the-loop environment.

Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications. Recently there have also been many theoretical contributions of quantum machine learning algorithms with experiments performed on quantum simulators. However, most questions concerning the potential of machine learning on quantum computers are still unanswered such as How well do current quantum machine learning algorithms work in practice? How do they compare with classical approaches? Moreover, most experiments use different datasets and hence it is currently not possible to systematically compare different approaches. In this paper we analyze how quantum machine learning can be used for solving small, yet practical problems. In particular, we perform an experimental analysis of kernel-based quantum support vector machines and quantum neural networks. We evaluate these algorithm on 5 different datasets using different combinations of quantum feature maps. Our experimental results show that quantum support vector machines outperform their classical counterparts on average by 3 to 4% in accuracy both on a quantum simulator as well as on a real quantum computer. Moreover, quantum neural networks executed on a quantum computer further outperform quantum support vector machines on average by up to 5% and classical neural networks by 7%.

This paper demonstrates the current state-of-the-art in low-cost, low loss ruggedized polymer-based 3-D printed G-band (140 to 220 GHz) metal-pipe rectangular waveguide (MPRWG) components. From a unique and exhaustive up-to-date literature review, the main limitations for G-band split-block MPRWGs are identified as electromagnetic (EM) radiation leakage, assembly part alignment and manufacturing accuracy. To mitigate against leakage and misalignment, we investigate a ‘trough-and-lid’ split-block solution. This approach is successfully employed in proof-of-concept thru lines, and in the first polymer-based 3-D printed 90° twist and symmetrical diaphragm inductive iris-coupled bandpass filters (BPFs) operating above 110 GHz. An inexpensive desktop masked stereolithography apparatus 3-D printer and a commercial copper electroplating service are used. Surface roughness losses are calculated and applied to EM (re-)simulations, using two modifications of the Hemispherical model. The 7.4 mm thru line exhibits a measured average dissipative attenuation of only 12.7 dB/m, with rectangular-to-trapezoidal cross-sectional distortion being the main contributor to loss. The 90° twist exhibits commensurate measured performance to its commercial counterpart, despite the much lower manufacturing costs. A detailed time-domain reflectometry analysis of flange quality for the thru lines and 90° twists has also been included. Finally, a new systematic iris corner rounding compensation technique, to correct passband frequency down-shifting is applied to two BPFs. Here, the 175 GHz exemplar exhibits only 0.5% center frequency up-shifting. The trough-and-lid assembly is now a viable solution for new upper-mm-wave MPRWG components. With this technology becoming less expensive and more accurate, higher frequencies and/or more demanding specifications can be implemented.

One of the main restrictions of commercial cobots can be found in their limited payload to mass ratios. Flexible link manipulators seem to offer interesting advantages over traditional rigid robotics, in terms of lower self-weight, lower energy consumption and safer operation. However, the design and loading specifications in the general literature on flexible link manipulators differ from those expected in a collaborative industrial setting. In this paper, we want to investigate whether the use of flexible links can be truly beneficial for collaborative robotics. Firstly, the theoretical potential of flexible links to increase the payload to mass ratio is investigated. The feasibility to design a cylindrical flexible link for specific, realistic loading conditions is investigated, and the effect of link flexibility on the demanded motor torque and maximum reachable payload is visualised, while considering cylindrical links. Subsequently, to get insights in the accuracy and usability of such a manipulator, weexperimentally quantify to what extent the undesired side effects of the flexible design can be counteracted using an appropriate controller. To comply with the envisioned application of collaborative robotics, a control strategy based on strain measurements along the link and robust to payload changes is proposed. The obtained accuracy was measured by tracking the end effector position using a Vicon motion capture system, considering two types of single link designs; firstly, a very flexible link setup with rectangular cross section, and secondly, a cylindrical flexible link, loaded to reach a payload to mass ratio of 1.

This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become more and more important due to the increase of availability in many industrial fields. Labeling, sorting or filtering highly transient measurement data for training condition based maintenance (CbM) models is cumbersome and error-prone. For some applications it can be sufficient to filter measurements by simple thresholds or finding change-points based on changes in mean value and variation. But a robust diagnosis of a component within a component group for example, which has a complex non-linear correlation between multiple sensor values, a simple approach would not be feasible. No meaningful and coherent measurement data which could be used for training a CbM model would emerge. Therefore, we introduce an algorithm which uses a recurrent neural network (RNN) based Autoencoder (AE) which is iteratively trained on incoming data. The scoring function uses the reconstruction error and latent space information. A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering. For evaluation, we propose a new similarity measure based on the curvature for a more intuitive time-series subsequence clustering metric. A comparison with seven other state-of-the-art algorithms and eight datasets shows the capability and the increased performance of our algorithm to cluster MTSD online and offline in conjunction with mechatronic systems., 26 pages, 11 figures, for associated python code repositories see https://github.com/Jokonu/mt3scm and https://github.com/Jokonu/abimca; Minor spelling and grammar corrections, fixed wrong bibtex entry for SOStream, some improvements and corrections in formulas of section 4

Blade tip-timing (BTT) signals are always severely under sampled, so reconstruction is much needed for vibration analysis. Blade vibration responses are sparse in order domain and classical compressed sensing (CS) algorithms are difficult to reconstruct vibration orders due to lack of prior sparse information under variable speeds. In order to address this issue, this paper introduces deep learning (DL) into BTT vibration reconstruction and proposes an end-to-end deep compressed sensing (DCS) method. Firstly, a multi-coset BTT measurement model is built under variable speeds and the DCS model is derived in order domain, where a specific convolutional neural network (CNN) is designed. Next, a Simulink model is built to generate training and testing samples. The simulation results show that the convolution layer with the rectified linear unit (ReLU) layer placed after the batch normalization (BN) layer can improve the reconstruction performance and the proposed method has better reconstruction accuracy and efficiency than classical CS algorithms. Finally, experiments are done and the results demonstrate that blade vibration orders can be recovered accurately by the proposed method, which will provide a novel way of BTT signal analysis.

Human movement analysis is a key area of research in robotics, biomechanics, and data science. It encompasses tracking, posture estimation, and movement synthesis. While numerous methodologies have evolved over time, a systematic and quantitative evaluation of these approaches using verifiable ground truth data of three-dimensional human movement is still required to define the current state of the art. This paper presents seven datasets recorded using inertial-based motion capture. The datasets contain professional gestures carried out by industrial operators and skilled craftsmen performed in real conditions in-situ. The datasets were created with the intention of being used for research in human motion modeling, analysis, and generation. The protocols for data collection are described in detail, and a preliminary analysis of the collected data is provided as a benchmark. The Gesture Operational Model, a hybrid stochastic-biomechanical approach based on kinematic descriptors, is utilized to model the dynamics of the experts' movements and create mathematical representations of their motion trajectories for analysis and quantifying their body dexterity. The models allowed accurate the generation of human professional poses and an intuitive description of how body joints cooperate and change over time through the performance of the task.

To monitor network operation in real-time, power system operators have developed wide-area monitoring systems (WAMS). However, the centralized communication and information processing architecture of WAMS cannot be extended easily to distribution networks. In this aspect, a three-level distributed network monitoring architecture is proposed in this paper, concerning the dynamic analysis of transmission, primary and secondary distribution networks by exploiting measurements of ambient data and transient responses. In the proposed architecture, operators are responsible for the operation and analysis of their own grid but also can share an overview of the system performance to facilitate their operational coordination. Different online and offline applications are supported within the architecture, including small-signal, transient and frequency stability analysis as well as dynamic equivalencing and real-time inertia estimation. Measurement-based algorithms and models are proposed for each case. Finally, the performance of the developed algorithms has been tested by using a combined transmission and distribution power system model.

Publisher Copyright: © 2013 IEEE. Restructuring in power systems has resulted in the development of microgrids (MGs) as entities that could be operated in grid-connected or islanded modes while managing the operation of their systems. On the other hand, privatization and integration of independently operated distributed resources in energy systems have caused the introduction of multi-agent structures. In this regard, new operational management methodologies should be employed by the MG operator (MGO) to efficiently operate the system while addressing the distributed nature of multi-agent structures. Accordingly, this paper aims to provide a new algorithm to operate an islanded multi-agent MG utilizing the peer-to-peer (P2P) management concept, which copes with the distributed nature of the system. Consequently, each agent would independently schedule its respective local resources while participating in the hourly P2P market scheme. Moreover, MGO manages the power transactions among the agents. Furthermore, different types of power generation resources are modeled in the proposed optimization scheme while scenario-based stochastic optimization, as well as the condition-value-at-risk index, are deployed to address the uncertainty and the operational risk associated with the operational optimization of renewable energy resources. Finally, the developed framework is implemented on a 10-bus-MG test system to investigate its effectiveness in the management of the system and also on a 33-bus-MG test system to study its scalability.

Network slicing is one of the fundamental tenets of Fifth Generation (5G)/Sixth Generation (6G) networks. Deploying slices requires end-to-end (E2E) control of services and the underlying resources in a network substrate featuring an increasing number of stakeholders. Beyond the technical difficulties this entails, there is a long list of administrative negotiations among parties that do not necessarily trust each other, which often requires costly manual processes, including the legal construction of neutral entities. In this context, Blockchain comes to the rescue by bringing its decentralized yet immutable and auditable lemdger, which has a high potential in the telco arena. In this sense, it may help to automate some of the above costly processes. There have been some proposals in this direction that are applied to various problems among different stakeholders. This paper aims at structuring this field of knowledge by, first, providing introductions to network slicing and blockchain technologies. Then, state-of-the-art is presented through a global architecture that aggregates the various proposals into a coherent whole while showing the motivation behind applying Blockchain and smart contracts to network slicing. And finally, some limitations of current work, future challenges and research directions are also presented. This work was supported in part by the Spanish Formación Personal Investigador (FPI) under Grant PRE2018-086061, in part by the TRUE5G under Grant PID2019-108713RB-C52/AEI/10.13039/501100011033, and in part by the European Union (EU) H2020 The 5G Infrastructure Public Private Partnership (5GPPP) 5Growth Project 856709. Publicado

This paper proposes a decentralized control scheme that mitigates floods in urban drainage systems (UDSs). First, we develop a partitioning algorithm of the UDS relying on a graph model of the system. Once this is done, we design a local controller for each partition based on the replicator dynamics model (a set of differential equations that describes the evolution of a population of players involved in a strategic game). The decentralized nature of the proposed strategy makes it suitable for applying it in large-scale systems. Stability of the closed-loop system is proved by using Lyapunov theory. Furthermore, we simulate the performance of the decentralized control scheme in two case studies. One of them models part of the Bogotá (Colombia) stormwater UDS. Finally, we compare the proposed technique with two widely used methods for-real time control of UDSs, i.e., constrained linear quadratic regulator (LQR) and model predictive control (MPC).

With the increase of travel demands in the networked metro system, more and more metro transfer stations are suffering from oversaturated situations, leading to the accumulation of passengers in the transfer corridor and boarding area with potential accident risks. To further improve the transfer efficiency and passenger accumulation safety at transfer stations, the joint operation strategy is adopted. Metro trains are allowed to run across different metro lines and passengers switch other lines without transfer. This paper proposes 1) a meaningful, yet simple, way for defining the running strategy of trains for joint operation with two intersecting metro lines and 2) an optimal model for the train frequency to minimize indicators associated with the passenger service and train capacity. Regulation constraints such as waiting time of through and transfer passengers, number of available trains, load factor, and departure interval are taken into account. Finally, a case study of the joint operation in the Beijing metro system is implemented to demonstrate the performance and effectiveness of the proposed approaches.

In this paper, an effective procedure for broken rotor bar (BRB) fault detection in a three-phase squirrel-cage induction machine (SCIM) is proposed. This approach relies on a motor current signature analysis (MCSA) by observing the specific fault-related current component generated by applying the DC injection braking method. Unlike the traditional MCSA, which is commonly focused on the detection of BRB sidebands around the fundamental current component, the proposed methodology introduces a new BRB feature in the current spectrum which makes it much easier for identification. The distinctive time-frequency evolution pattern of this feature provides the reliable identification of BRBs, even under no-load operating conditions, thus overcoming the major drawback of traditional MCSA-based methods. Fault severity classification is easily performed through the magnitude inspection of the BRB fault-related current component. In addition, the proposed approach does not require high-complexity signal processing algorithms to achieve reliable results. The proposed concept is presented theoretically, assisted by a magnetically coupled multiple circuit model of the SCIM, both with healthy and faulty rotor bars. Finally, the experimental tests validate the proposed methodology and demonstrate its effectiveness and usefulness.

To explore the mechanism of multiple conjunctural causation underlying differences in financial performance, this study selects 39 Chinese companies from the Fortune Global 500 as samples and applies configurational thinking and the qualitative comparative analysis (QCA) approach to integrate five variable conditions concerning corporate social responsibility (CSR). The results show that 3 path configurations achieve high financial performance. Specifically, the first configuration includes highly responsible governance, superior employee rights and interests, and effective environmental protection. The second configuration includes highly responsible governance, superior employee rights, and active public charity. The third configuration includes highly responsible governance, active public charity, and effective environmental protection. The four dimensions involved in the above three path configurations have alternative effects in interpreting financial performance. When the level of responsible governance ability is high, as long as any two conditions among employee rights and interests, environmental protection and public charity also attain a high level, this situation can drive the corporation to achieve high financial performance. Based on the conclusions of this paper, management implications from the perspectives of firms are also proposed.

Since the emergence of the Bitcoin cryptocurrency, the blockchain technology has become the new Internet tool with which researchers claim to be able to solve any existing online problem. From immutable log ledger applications to authorisation systems applications, the current technological consensus implies that most of Internet problems could be effectively solved by deploying some form of blockchain environment. Regardless this ‘consensus’, there are decentralised Internet-based applications on which blockchain technology can actually solve several problems and improve the functionality of these applications. The development of these new blockchain-based solutions is grouped into a new paradigm called Blockchain 3.0 and its concepts go far beyond the well-known cryptocurrencies. In this paper, we study the current trends in the application of blockchain on the paradigm of Public Key Infrastructures (PKI). In particular, we focus on how these current trends can guide the exploration of a fully Decentralised Identity System, with blockchain as be part of the core technology.

Rising demand for the integration of IoT services in cloud computing has brought cloud-native principles (e.g., system automation, loosely coupled services, etc.) to the edge of the network. A fundamental element of the cloud-native approach is a microservice — an instance, which is usually virtualised, serving for only a defined purpose. Virtualised microservices are mostly used in cloud-based IoT systems, which can be covered with multi-agent system (MAS) paradigm. The maintenance of a MAS requires a constant monitoring to track the system state and to satisfy the service level agreement (SLA) (or any other) requirements. This paper presents a resource efficient reliability model for MAS IoT systems for monitoring purposes. First, we provide a thorough mathematical analysis to describe the generic system model and its elements. This model takes into account SLA requirements and it is characterised with linear time complexity, simplicity of computations and input metrics (e.g., combination of SLA for CPU and its workload), where all of the input data are aligned to the same range. Then, we evaluate time complexity and provide a measurement to demonstrate the reliability of the model application. The results show that the proposed model is efficient for large number of the IoT agents. It is approximately 98 times faster in computation with 1,000 systems when compared with [D. Ursino, et al., 2020] and 51 times faster in computations with 1,000 systems when compared with [J. Yao, et al., 2019].

In the data-driven world, data is created in huge volume and then analyzed by several organizations to get benefit from them. Smart city is one of the examples to use big data to offer improved services for its resident and tourist. However, some countries face certain obstacles to analyze the big data integration for sustainability in smart city development. Therefore, the purpose of this research paper is to identify and analyze the significant barriers related to sustainable smart city development. To accomplish this objective, fourteen barriers of big data analytics are selected through the combined approach of literature review and expert input. After that, these barriers are evaluated using the best worst method for obtaining deeper insights. The result of this study reveals that the most significant barrier is ‘lack of technologies for BDA’, ‘lack of BDA framework’, ‘nature of big data’, and ‘low availability of analytics platforms for big data’. These barriers need to address on priority to develop a sustainable smart city. This study is helpful to the urban planner, government, and consultancy agencies to decide on the adoption of BDA for sustainable smart city development. Further, they can also optimize their resources in the best possible manner to achieve the sustainable development of the existing smart cities.

Similarity learning which is useful for the purpose of comparing various characteristics of images in the computer vision field has been often applied for deep metric learning (DML). Also, a lot of combinations of pairwise similarity metrics such as Euclidean distance and cosine similarity have been studied actively. However, such a local similarity-based approach can be rather a bottleneck for a retrieval task in which global characteristics of images must be considered important. Therefore, this paper proposes a new similarity metric structure that considers the local similarity as well as the global characteristic on the representation space, i.e., class variability. Also, based on an insight that better class variability analysis can be accomplished on the Stiefel (or Riemannian) manifold, manifold geometry is employed to generate class variability information. Finally, we show that the proposed method designed through in-depth analysis of generalization bound of DML outperforms conventional DML methods theoretically and experimentally.

Programmable Logic Controllers (PLCs) are essential components for enabling remote monitoring and automated control in industrial control systems. Recently PLCs are often utilized in a modernized power grid system for implementing an additional layer of automated control, such as operation of circuit breakers under specific conditions. Thus, in order to create a software-based smart grid testbed (or ‘cyber range’ for cyber security experiments); emulation of the PLC is imperative. OpenPLC is a software widely used for emulating PLCs, but unfortunately it does not support IEC 61850 standard, which is the globally adopted standard for substation automation in smart power grid systems. Thus, in this paper, the enhancement of OpenPLC to support IEC 61850 protocol and information models is discussed. The performance of the implementation has been validated to corroborate its application for use cases in the smart grid paradigm. Subsequently, the implementation is demonstrated in a smart grid cyber range to evaluate the impacts of attacks and thereby, the effectiveness of security measures and robustness of PLC control logic. The implementation, named ‘OpenPLC61850’, is made available as an open-source project for the wider research and industry community.

Capacitive power transfer (CPT) technology is becoming increasingly popular in various application areas. Due to its limitations, such as low frequency, low coupling capacitance, and the high voltage stress on metal plates, the studies on high power CPT applications fell behind previously. Therefore, the wideband gap (WBG) semiconductor devices and the compensation topologies are further adopted to tackle these limitations. The main purpose of the paper is to review CPT applications in terms of performance parameters, advantages, disadvantages, and also challenges. Initially, the basic principles of CPT technology are examined, which cover compensation topologies, coupler structures, transfer distance, power electronic components, and system control methods. Then, CPT applications are evaluated for performance parameters (i.e., power level, operation frequency, system efficiency, transfer distance) along with compensation types, inverter types, and coupler types. The applications are categorized into six main groups according to industrial topics as safety, consumer electronics, transport, electric machines, biomedical, and miscellaneous. Herein, power level changes from $\mu \text{W}$ to kW ranges, the operation frequency varies from 100s of kHz to 10s of MHz ranges as well. The maximum system efficiency is recorded as 97.1 %. The transfer distance varies from $\mu \text{m}$ range to 100s of mm ranges. The full-bridge inverter topology and four-plate coupler structure are noticeable in CPT applications. Finally, advantages, disadvantages, and challenges of CPT applications are evaluated in detail. This review is expected to serve as a reference for researchers who study on CPT systems and their applications.

International audience; This paper presents an overview of the challenges and state-of-the-art physical layer enhancement designs for next-generation railway communication, also known as high-speed train (HST) communication. The physical layer design for the HST should adapt from its counterpart in the general-purpose network because of the harsh propagation environment and extreme conditions, stringent latency and reliability requirements of dedicated railway applications, and frequency band scarcity caused by regulation. In this survey, we examine how conventional multiple-input-multiple-output (MIMO) family techniques such as beamforming, multi-cell MIMO, and relays can enhance the physical layer performance for HST. Physical layer enhancement assisted by novel reconfigurable intelligent surface (RIS) technology was also analyzed from different perspectives. Dedicated control channels, reference signals, waveforms, and numerology designs for train-to-infrastructure (T2I) and train-to-train (T2T) communication in sidelinks are also reviewed. Finally, a brief introduction to artificial intelligence (AI)/machine learning (ML)-aided HST physical layer design is provided. Several promising research avenues have also been suggested.