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Microgrids are a viable approach for improving energy resiliency and sustainability. Current microgrid systems frequently suffer from inadequate fault identification and compensation techniques, resulting in operational inefficiencies and grid instability. Conventional control strategies may not effectively manage changing environmental conditions and variations in load, requiring a more adaptable and intelligent approach. The paper presents a sophisticated method that combines Fuzzy Logic Controllers (FLCs) with Synchrophasor Technology (ST) to tackle these challenges. This work aims to develop and execute a thorough solution for microgrids’ fault identification, compensation, and power management. The paper proposed a MATLAB/Simulink model that includes a Solar Photovoltaic (SPV) system with an FLC-based Maximum Power Point Tracking (MPPT), a Diesel Generator (DG), a Battery Energy Storage System (BESS) with an FLC-based Battery Management System (BMS), a Voltage Source Inverter (VSI), and a Unified Power Flow Controller (UPFC) with a FLC. Phasor Measurement Units (PMUs) equipped with ST are used to monitor and identify the anomalies in the system. The analysis of the simulation data provides evidence of the efficacy of the suggested approach. The SPV system demonstrates strong performance in producing voltage and current, successfully adjusting to different irradiance levels. The MPPT algorithm based on FLC guarantees the highest power efficiency. Additionally, the BESS and UPFC, controlled by FLCs, enhance grid stability and power quality. By integrating with synchrophasor-enabled PMUs, the microgrid may achieve accurate fault detection, and thus, the operator can take corrective actions to guarantee dependable operation even under challenging circumstances. Furthermore, the reliability and efficiency of the proposed system were confirmed using a Real-Time Digital Simulator (RTDS) using RSCAD with PMUs in real-world environments. By employing hardware-based PMUs, the system can accurately monitor grid interactions in real-time, enabling prompt detection of disruptions and proactive deployment of control measures. The RTDS results showcased the system’s ability to maintain grid stability and efficiently manage faults, as evidenced by the precise current and voltage magnitudes measurements and their phase angles, frequency, and Rate of Change of Frequency (ROCOF) during fault scenarios.

Deep neural networks have performed remarkably in many areas, including image-related classification tasks. However, various studies have shown that they are vulnerable to adversarial examples – images carefully crafted to fool well-trained deep neural networks by introducing imperceptible perturbations to the original images. To better understand the inherent characteristics of adversarial attacks, this paper studies the features of three common attack families: gradient-based, score-based, and decision-based. The primary objective is to recognize distinct types of adversarial examples, as identifying the type of information possessed by the attacker can aid in developing effective defense strategies. This paper demonstrates that adversarial images from different attack families can be successfully identified with a simple model. To further investigate the reason behind the observations, this paper conducts carefully designed experiments to study the distortion patterns of different attacks. Experimental results on CIFAR10 and Tiny ImageNet validated the differences in distortion patterns between various attack types for both $L_{2}$ and $L_{\infty } $ norm.

Ensuring content security and copyright protection is a critical concern in wireless communication networks. Furthermore, the emergence of services demanding high-band width and very low delay such as online Games, extended reality (XR), Metaverses, etc., Non-Orthogonal Multiple Access (NOMA) is a technology with the potential to enhance the spectral efficiency of fifth-generation (5G) wireless network and beyond. Hence, this paper studies a NOMA-based downlink system that employs intelligent reconfigurable surfaces (IRS) and operates in a physical-layer security environment. Our study also incorporates the utilization of Fountain codes (FCs), all while contending with the presence of a malicious eavesdropper. Furthermore, a cooperative jamming technique is employed to degrade the quality of the eavesdropping channel. The main contribution of this paper is to derive precise closed-form expressions of outage probability (OP), energy efficiency (EE), intercept probability (IP), and average secrecy rate (ASR) for the proposed system. We also develop a Deep Neural Network (DNN) model to evaluate OP, IP, ASR and the average number of time slots (ATS). Subsequently, Monte Carlo simulations are presented as a means to validate the theoretical findings. The simulation results yield the following insights: i) Their primary purpose is to validate the analytical formulas. ii) This research significantly contributes to deepening our understanding of IRS-NOMA systems, providing a foundation for future investigations into practical implementations. iii) We investigate the optimal power allocation factors within the IRS-NOMA framework, offering valuable insights into designing IRS-NOMA systems to achieve reliable and secure communication. iv) The results illustrate the superior performance of IRS-NOMA in comparison to the conventional IRS-Orthogonal Multiple Access (OMA) method.

Load redistribution attacks (LRAs) are a type of false data injection attack that disrupts the normal operation of the power grid by redistributing load. While most LRAs in the literature are based on the DC model, this paper proposes an LRA in the AC model using incomplete network information. To defend against the proposed LRA, the paper proposes using Moving Target Defense (MTD) to actively invalidate the attackers’ knowledge. A zero-sum defense-attack game is formulated between MTD and LRA to select an MTD operating range optimally, considering the trade-off between attack detection effectiveness and the extra operation cost. The paper defines a new payoff function as an expected incremental operation cost, consisting of the defense cost, attack cost, and attack mitigation capability. A Nash Equilibrium of the game provides optimal strategies for selecting the MTD operating range. Simulation results on the modified IEEE 14-bus system demonstrate the effectiveness of MTD in detecting the proposed LRA. The paper shows that MTD not only detects ongoing LRAs but also prevents the construction of such attacks by using the proposed game theoretical framework. The proposed approach highlights the role of MTD in enhancing the cybersecurity of power grids against LRAs.

With the rapid development of science and technology, all types of mixed media contain large amounts of data. Traditional single multimedia data can no longer satisfy daily requirements. Therefore, the cross-modal retrieval technology has become an urgent requirement. Consequently, there is a pressing need for cross-modal retrieval technology. Its purpose is to mine the connection between different modal samples, that is, to retrieve another modal sample with approximate semantics through one modal sample. For example, users can retrieve multimedia data such as images or videos with text. However, there are differences in the modal representation of different types of multimedia data, and measuring the correlation between different modes is the main problem of cross-modal retrieval. Currently, the most popular deep learning methods have achieved remarkable results in the field of data processing and graphics. Many researchers have applied deep learning methods to cross-modal retrieval to solve the problem of similarity measurement between different multimedia data. By summarizing the relevant paper methods of cross-modal retrieval, this paper provides a definition of cross-modal retrieval problems, reviews the core ideas of the current mainstream cross-modal retrieval methods in the form of three main methods, lists the commonly used data sets and evaluation methods, and finally analyzes the problems and future research trends of cross-modal retrieval.

Duct bank installation, as a popular installation type for underground power cables, has been widely used. However, laying excessive number of ducts in a duct bank has given rise to the cable arrangement problem. Meanwhile, the current academic research on the cable arrangement problem is not mature. In this regard, this paper proposes a novel stochastic optimization algorithm namely the agamogenetic algorithm to solve the cable arrangement problem. The roulette strategy is applied in the mutation process of the proposed algorithm, leading to the development of two novel operators, namely the excitation operator and the interchange operator. From the general agamogenetic algorithm, this paper further designs its customized variants for the two-ends and single-end grounding modes. Subsequently, the concept of cross-bonded grounding mode is promoted, and a cross-bonded configuration beyond the same transmission circuit is proposed, namely the generalized cross-bonded grounding mode. Due to its requirement to simultaneously optimize the cable arrangement and the sheath cross-bonded structure, a specific oriented agamogenetic algorithm is also designed. Finally, a real-life duct bank installation is used as the case study, validating that the agamogenetic algorithm has higher convergence speed, accuracy, and stability compared to existing algorithms for optimizing cable arrangements in ducts. Simulation results also show that the loss or voltage of the optimal arrangement decreases by 12.24%, 50.66%, and 11.67% under the two-ends, single-end, and cross-bonded grounding modes, respectively, compared to the standard engineering arrangement.

The safety of aviation platforms has always been a major concern in the design of aviation vehicles. Airborne high-speed maneuvering targets are currently the most significant threat to all types of aviation platforms due to their high flexibility and anti-jamming capability. This paper presents a new technology of airborne flexible net for high-speed maneuvering target capture. The analysis of flexible net dynamics in this paper includes two aspects: net-expansion dynamics and capture collision dynamics. To solve the technical difficulties of the airborne unfolding of the flexible net, two symmetric design schemes, namely, the traction method and the rotation method, are developed. The mechanical analyses of the two net-expansion methods are carried out. Meanwhile, this paper establishes a collision dynamics model based on the continuous contact force method, and carries out several collision capture tests using two flexible nets. The results show that a transient acceleration is applied in all directions of the target during the collision, capable of reaching the magnitude of 104. The maximum stress inside the target aerodynamic surface reaches the magnitude of GPa, and the strong impact produced in the capture process will cause damage to the aerodynamic parts. Finally, the flight experiments of damaged targets are carried out based on six-degree-of-freedom-model, and the effect of damaged targets on the safety of aircraft platforms after contact collisions is further analyzed. The results of this paper can provide a theoretical reference for the research and development of airborne flexible nets.

The development of measurement matrices remains a pivotal focus within the domain of compressed sensing theory. This paper introduces an innovative methodology for the construction of a deterministic binary measurement matrix, harnessing the properties of chaotic sequences. This approach streamlines the process and only requires a fixed number of initial variables for matrix construction. The construction method of the measurement matrix is divided into two distinct stages. Initially, the matrix elements are extracted through gapless sampling of chaotic sequences. Recognizing the inherent correlation among chaotic sequence elements due to gapless sampling, this paper introduces a novel nonlinear binary transformation. This innovative approach effectively mitigates the correlation and concurrently addresses storage limitations. Thereafter, the entries are integrated into a Toeplitz matrix, leveraging its structured row and columnar properties to augment storage efficiency. In subsequent steps, the approach integrates the Householder transformation to generate an orthogonal basis, thereby enhancing the matrix’s performance. Additionally, an XOR operation is employed to optimize the distribution of entries across each column vector. The research substantiates that the resultant matrix exhibits low correlation, is facile in generation, and amenable to reconstruction. Comprehensive experimental data corroborate the efficacy of the constructed measurement matrix in processing both one-dimensional and two-dimensional signals. The matrix demonstrates a strong potential to meet the Restricted Isometry Property (RIP), thereby underscoring its substantial advantages across a myriad of considerations.

A strong and reliable Traffic Sign Detection and Recognition (TSDR) system is essential for the effective deployment of autonomous driving technology. In this field, numerous scholars have conducted extensive research, but current studies only consider TSDR under ideal conditions, neglecting scenarios such as rain, snow, fog, which can cause image blurring. This paper investigates the challenges of TSDR performance degradation caused by five adverse environmental conditions: rain, snow, fog, lens dirt, and lens blur. To overcome the adverse effects of these conditions on TSDR, this paper proposes a Convolutional Neural Network (CNN)-based TSDR method, consisting of three modules: adverse environment classification module, image enhancement module, and traffic sign detection module. The adverse environment classifier, based on the VGG19 architecture, identifies whether the image includes the aforementioned five adverse weather conditions. The image enhancement module named Enhance-Net enhances each of the five adverse environments separately and specifically enhances the traffic sign regions within the image, rather than the entire image area. To increase the speed of the proposed method, the traffic sign detection module utilizes the YOLOv4 framework. The proposed method’s effectiveness is assessed using the CURE-TSD dataset, which includes traffic videos recorded in various adverse environmental conditions. Experimental results demonstrate that under five different levels of adverse environments, the pro-posed method achieves 95.03% accuracy and runs at a rate of 12.79 fps (frames per second). In contrast to the current benchmark, although there is a 2.81% reduction in accuracy resulting from training the proposed method on a subset of the dataset, the speed has increased by 12.03 fps, demonstrating the efficacy of the proposed approach.

The design of an efficient Pseudo Random Number Generator (PRNG) with good randomness properties is an important research topic because it is a core component in many applications. Based on an extensive study of most PRNGs in the past few decades, this paper categorizes six distinct design scenarios under two primary groups: non-chaotic and chaotic generators. The non-chaotic group comprises Linear Feedback Shift Registers (LFSR) with S-Boxes, primitive roots, and elliptic curves, whereas the chaotic group encompasses discrete, continuous, and fractional-order chaotic generators. This paper delves into the related scientific summaries, equations, flowcharts, and designs with necessary recommendations for each PRNG scenario. Even though the focus is on the basic design characteristics that provide simple, functional and secure PRNGs, it is possible to enhance those designs for additional features and improved efficiency. Simulation outcomes and system key configurations, which produce long random sequences, are also presented and evaluated using leading criteria. The evaluation criteria include the National Institute of Standards and Technology (NIST) SP-800-22 test suite, TestU01 randomness tests, histogram, entropy, autocorrelation, and cross-correlation. Furthermore, key space, key sensitivity, and bit rate indicate that all designed examples meet international standards with high quality. The presented PRNGs are compared and integrated into an image encryption system. Although each PRNG design scenario can have a different key space, simple designs with fixed-length system keys are chosen for the sake of proper comparisons. Statistical and security assessments of the encryption system demonstrate that the PRNGs are cryptographically secure.

A small target object refers to an object whose relative size of the bounding box is very small, usually the ratio of the width of the bounding box to the width and height of the original image is less than 0.1, or the ratio of the area of the bounding box to the area of the original image is less than 0.03, or the absolute size is less than $32^{\ast } 32$ pixels. It has important applications in industrial defect detection, medical image processing, intelligent security, unmanned driving, and many other fields. Although great progress has been made in the field of target detection, which is limited to large target objects, due to the challenges of small size, inconspicuous features and insufficient data samples, the accuracy and speed of small target detection are low. To solve this problem, this paper proposes a novel small target object detection algorithm model: Swin Transformer’s DETR. In this algorithm, Swin Transformer is used as the backbone to extract the global features and local information of small targets, and a three-layer feature pyramid structure is used for feature fusion at the Neck layer to improve the calculation efficiency and model accuracy. Secondly, the detector is optimized, and the detector is replaced by two stages, and the ReLU activation function of FFN layer is replaced by the latest SwiGLU activation function, to avoid the problems of gradient disappearance and explosion and enhance the nonlinearity of the algorithm model. Large resolution size input is adopted on Tiny Person dataset, and its input value is set to [1400,800]. The above analysis is carried out on VOC and Tiny Person datasets, and the detection rates of small target objects are 88.9% and 48.3% respectively. The results show that the Swin Transformer’s DETR algorithm model proposed in this paper performs well on various datasets, and has strong generalization ability, stability and accuracy in different scenarios and datasets, which is higher than other algorithm models.

Ovarian cancer (OC) is one of the most common types of cancer in women. Surgery and chemotherapy are still the most common forms of treatment; however, their success depends on lots of factors describing the type of cancer, the size, shape, and its origin, and thus early and accurate detection could bring lots of benefits to increasing survival rate by applying custom/ personalized and effective treatment. This is why many researchers aim to obtain accurate computer-aided diagnosis (CAD) systems to assist in the early detection and diagnosis of such diseases. The current paper presents a systematic review of new trends in designing different deep learning-based intelligent systems for accurate OC detection and diagnosis. The paper presents the advantages of using deep learning approaches for OC diagnosis, the most used methods, and datasets. It performs a detailed analysis concerning the most preferred, effective, and accurate architecture. 95 articles published in journals and impact conferences were investigated between 2018 and 2024, focusing on 2021 and 2024. All included studies are indexed in PubMed, Scopus, or ISI Web of Science. The authors of the analyzed articles used private databases (the majority) and public databases. Neural networks and combined DL systems recently used for ovarian tumor detection, classification, and segmentation were investigated. The performances obtained by the most relevant systems were also presented. Finally, a comparison with other reviews in the field highlighted the advantages of this article.

Probability estimation measures the likelihood of different outcomes in a statistical context. It commonly involves estimating either the parameters or the entire distribution of a random variable. Parametric approaches, where a specific functional form is assumed for data distribution, have been used in various fields, particularly in computational statistics for modeling and simulating physical phenomena. However, non-parametric methods have gained prominence, especially in machine learning and signal processing. These methods are focused on estimating or modeling probability density functions without relying on predefined parametric forms. It becomes crucial when faced with unknown or complex distributions, especially if parametric assumptions do not hold. This paper deals with the non-parametric method based on the Parzen window for probability density function estimation, a versatile approach applicable to univariate and multivariate data. Having a sufficient amount of data, this method provides reliable estimates, while at the same time, it is quite suitable for implementation. Considering the advantages of hardware implementations compared to software solutions, this paper introduces analog-digital hardware for the Parzen approach. The proposed solution avoids the need for sorting operations, which are typically challenging to implement in hardware. The simulation is performed using PSpice software (OrCad version 22.1) showing that the required processing time is under 420 ns.

In response to the repeated occurrence of loosening in the fixed structure of the ring leads at the end of generator, as well as issues such as bolt loosening and lead wear, during the recent major overhauls of a large steam turbine generator, this paper conducts an analysis of the causes of loosening and proposes corresponding improvement measures. Taking the generator end ring leads fixed structure as the research object, this paper employs comparative analysis and finite element simulation methods to analyze the causes of loosening. It identifies factors such as bolt fixing methods and cleat support methods contributing to loosening, and proposes improvement solutions including increasing the wrapping angle of the cleat and enhancing pretightening force of the bolts. According to the actual component size of the unit, a sample test platform is designed and built, and the correctness of the loosening cause and the effectiveness of the improvement plan are verified by the sample test. The experimental results confirm that the cleat support form and bolt fixing method significantly contribute to fixing structural loosening, while the proposed improvements effectively delay bolt loosening. Increasing the cleat wrapping angle and pre-tightening force of the bolts are both feasible on-site and offer practical engineering value, laying the foundation for solving the technical challenges associated with securing the generator’s end ring lead structure and ensuring safe and stable generator operation.

Blood is vital to sustain human life, to ensure that hospitals or blood centers have sufficient blood reserves, this paper is based on Long- and Short-Term Temporal Patterns with Deep Neural Networks (LSTNET) and Attention Mechanism, uses Gaussian Process Bayesian Optimization (GPBO) to optimize the model, and propose GPBO-LST-Attention network model. Based on the clinical blood transfusion data of a medical institution in Harbin in the past 10 years, the clinical blood transfusion volume was modeled and the blood usage in the future was predicted. The model proposed in this paper was subjected to ablation experiments and compared with other models, using the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) analyses to evaluate the model performance, and Akaike Information Criterion corrected (AICc) was used to analyze the model performance. It is shown that when the attention module and the GPBO algorithm are used in the GPBO-LST-Attention model proposed in this paper, the RMSE is reduced by 0.789, the MAE by 0.592, and the MAPE by 0.49. The prediction error values of error indexes and the AICc value are lower than the other models. It shows that the model established in this paper can accurately predict blood consumption in a future time and has excellent model performance, which can provide data reference for the blood reserve of hospitals or blood centers.

White matter hyperintensities (WMHs) are lesion in brain magnetic resonance images generally associated with Alzheimer’s disease (AD) and cognitive decline. Finding WMHs of AD poses a great challenge for diagnosis. This paper interprets a brain MR image as a hyperspectral image so that a well stablished hyperspectral subpixel detection algorithm, constrained energy minimization (CEM), is applicable to solving the WMHs detection problem at mixed pixel and subpixel level. To resolve nonlinear mixing in detecting WMHs nearby boundaries, a nonlinear CEM, called kernel CEM (KCEM) is also developed. Since CEM is a hyperspectral technique without taking spatial correlation into account, CEM was also extended to iterative CEM (ICEM) by including spatial filters to capture spatial information for WMHs detection. This paper combines ICEM and KCEM to derive a new WHMs detection algorithm, iterative KCEM (IKCEM) to improve ICEM and KCEM on WMHs detection. To evaluate the WMHs detection performance, two criteria, Dice similarity index (DSI) and 3D ROC analysis are used as evaluation tools. In order to show the superiority of IKCEM, two commonly used software packages, statistical parametric mapping (SPM)-based algorithms, SPM-lesion growth algorithm (SPM-LGA) and SPM-lesion prediction algorithm (SPM-LPA) are implemented for validation and comparison.

A method for optimizing multiple responses in solenoids utilized in a magnetic nozzle assembly for an electric plasma thruster is presented. The primary goal is to minimize power usage while maintaining a sufficiently high magnetic flux density at the coil axis. This is especially critical during testing in a vacuum chamber, where heat dissipation is a challenge, especially for small components and intricate assemblies. This paper presents a method for quickly calculating the magnetic field along the solenoid axis that provides accurate estimates, independent of coil size. Additionally, we present general and concrete results for the employed magnetic nozzle design to illustrate the applied optimization method. The paper details a methodology for optimizing multiple parameters of a solenoid, including its radius, length, current, number of turns, and wire diameter. The aim is to minimize, maximize, or meet specific targets for magnetic flux density, power, or mass. Additional studies could utilize a suitable metric to optimize the homogeneity of the magnetic field of multiple coils. The coils designed using the algorithm have a radius of 17.5 mm, a length of 21 mm, consist of 300 turns with a wire diameter of 1 mm, and operate at a direct current of about 5 A. The potential maximum magnetic field is 40 mT with a power consumption of around 30 W.

In the realm of navigation systems, Inertial Measurement Unit (IMU) sensors play a pivotal role. The advent of Micro-Electro-Mechanical System (MEMS) sensors has introduced a lightweight and cost-effective alternative for IMUs. However, MEMS IMUs come with the challenge of larger stochastic errors that accumulate over time, resulting in navigation drifts. To address this issue, the conventional approach involves fusing IMU with the Global Navigation Satellite System (GNSS) for reliable navigation. Nevertheless, this fusion setup fails in providing ubiquitous navigation during GNSS outage scenarios due to persistent IMU errors. In this paper, an efficient methodology is developed to mitigate navigation drifts by eliminating IMU errors using Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) Machine Learning (ML) algorithms. In contrast to existing methodologies that employ high-end and expensive IMUs for training models to denoise low-cost MEMS IMUs, this paper proposes utilizing Inverse Kinematics (IK). This approach helps to derive clean IMU training data from the Position, Velocity, Attitude (PVA) values estimated through the Extended Kalman Filter (EKF) when GNSS is available and reliable. The distinctive advantage of the IK approach lies in its capacity to obtain real-time pseudo error-free IMU data without the necessity for high-end IMUs to train ML models. The proposed method undergoes testing in both Loosely coupled and Tightly coupled EKF scenarios using simulation and real dataset under varying GNSS outage durations. Comparisons are made between the denoised IMU signals and signal processing techniques such as Moving Average (MA) and Savitzky Golay (SG). Additionally, we present a comparative analysis of the proposed algorithms against Convolutional Neural Networks (CNN). Results demonstrate a noteworthy enhancement in position, velocity, and orientation estimation. Furthermore, the computation time required for model training and prediction across various algorithms is analyzed. The outcomes prove the superiority of the proposed tree-based algorithms over the conventional filtering methods and CNN in denoising IMU and improving the navigation results over 90% in all the states compared to the PVA values obtained using raw IMU.

This paper proposes a new design methodology to regulate the output voltages of auxiliary power units in an electric vehicle application. The proposed method includes designing and implementing a robust fuzzy logic controller to load variation and input voltage fluctuation. The existing output voltage regulation techniques for auxiliary power units comprise a proportional-integral controller, which fails to provide good transient performance concerning load variation and input voltage fluctuation. To overcome the drawback of the existing technique, thereby improving transient performance, a proportional-integral controller is replaced by a fuzzy logic controller. This paper addresses a detailed analysis of comparing the proportional-integral and fuzzy logic controllers to segregate and utilize the best among them based on their performance. In addition, the issue of cross-regulation is also eliminated for the proposed design. Also, the proposed design involves the use of a single-input triple-output converter of a 100 W prototype which is designed to furnish three output voltages 24 V, 14.4 V, and 5 V to supply the auxiliary power units of an electric vehicle. To validate the effectiveness of the proposed approach, simulation is conducted on the MATLAB/Simulink platform, and the quantitative results of the proposed design, which include output voltage ( $V_{o_{1}}$ ) =24 V, rise time ( $T_{r}$ ) =0.02 sec, and steady-state error ( $e_{ss}$ ) =0, are better than the existing values: output voltage ( $V_{o_{1}}$ ) =23.4 V, rise time ( $T_{r}$ ) =0.345 sec, and steady-state error ( $e_{ss}$ ) =0.6 V.

This paper addresses the hypothesis that the preference of a worker toward a work spot depends on attribute information such as her or his age and position and the type of work task on which she or he is working on that day. The system proposed in this paper is designed to estimate work-spot preferences using machine learning (ML) for activity-based workplaces. The system collects a variety of items related to attribute information and work-spot preferences for each work task from individual workers in advance. It constructs ML models using the collected data and estimates the work-spot preference for a worker. We extend the system to improve the accuracy for the case where the estimation is performed with a limited number of samples for training data. To examine the system, this paper presents two types of experiments: with computer graphics (CG) and with a real environment. Through these experiments, we collect data from people and construct ML models for the estimation of work-spot preferences based on attribute information and types of work tasks. The results demonstrate that the proposed system works for estimating the work-spot preference for each individual worker in accordance with the attribute information and the type of work tasks.

In this paper, we propose an optimal driving control algorithm that enables autonomous electric vehicle to track the reference path while avoiding front obstacles safely and quickly. An artificial potential field (APF) consists of a repulsive field that prevents collisions with nearby obstacles and road boundaries and an attractive field that brings the autonomous electric vehicle closer to the reference path. This APF is used as a path planning technique in this paper. Both the target longitudinal velocity and target yaw angle from the APF are transmitted to the model predictive controller (MPC) for path tracking of the autonomous electric vehicle. It predicts the future vehicle lateral behavior after a few seconds and optimally outputs the fast and accurate front steering angle and each wheel longitudinal force. In particular, the target vehicle longitudinal force for following the target longitudinal speed is set as the control input constraint of the proposed MPC. Based on CarSim and MATLAB/Simulink co-simulations, the high performance of the optimal driving control algorithm proposed in this paper is clearly confirmed in some driving scenario tests.

In this paper, the problem of the assist-as-needed (AAN) control of the upper limb exoskeleton is studied. Due to the complexity of the upper limb exoskeleton structure and driving mode, the control parameters of the equipment need to be adjusted to obtain satisfactory control performance. This paper presents an adaptive gain control method based on AAN. Through the dynamic gain adjustment mechanism, the system can adjust the control force in real-time to avoid excessive or insufficient auxiliary force. It can overcome the problem that traditional adaptive cannot identify dynamically changing parameters efficiently, and effectively avoid the overshoot and oscillation problems of high-gain adaptive, and do not need to obtain unknown disturbance information in complex models. The method adopts the on-demand auxiliary strategy to realize the self-adaptation of the auxiliary force and mode adjustment, which ensures the comfort, safety and effectiveness in the rehabilitation process. Through simulation experiment and comparison with the latest AAN controller, it can be seen that the controller has good anti-jamming effect and adaptive adjustment ability to auxiliary force under unknown interference.

The rapid increase in data generation has led to outsourcing computation to cloud service providers, allowing clients to handle large tasks without investing resources. However, this brings up security concerns, and while there are solutions like fully homomorphic encryption and specific task-oriented methods, challenges in optimizing performance and enhancing security models remain for widespread industry adoption. Outsourcing computations to an untrusted remote computer can be risky, but attestation techniques and verifiable computation schemes aim to ensure the correct execution of outsourced computations. Nevertheless, the latter approach incurs significant overhead in generating a proof for the client. To minimize this overhead, the concept of a Correct Execution Environment (CEE) has been proposed (CEEv1), which omits proof generation for trusted parts of the prover. This paper proposes a new hardware-based CEE (CEEv2) that supports virtual memory and uses an inverted page table mechanism to detect, or prevent, illegal modifications to page mappings. The proposed mechanism supports virtual memory and thwarts virtual-to-physical mapping attacks, while minimizing software modifications. The paper also compares the proposed mechanism to other similar mechanisms used in AMD’s SEV-SNP and Intel’s SGX.

This paper delves into the assessment of public engagement and the categorization of emotional states to gain deeper insights into individuals’ involvement in the revitalization of historical communities and their emotional experiences during this process. First, this paper proposes an enhanced Faster R-CNN model for detecting and analyzing public participation. Specifically, our model incorporates attention mechanisms, both in the spatial and feature channels, to enhance the identification of human subjects and facilitate more precise target localization. Additionally, this paper addresses the challenge of detecting multi-scale targets by employing Feature Pyramid Network (FPN) technology, which enriches the features computed by Faster R-CNN. Furthermore, a method for describing public emotions based on semantic analysis is proposed. Leveraging semantic analysis algorithms, double LSTM and self-attention mechanisms are employed to categorize the emotions of individuals and generate captions to understand the emotions. Experiments validate the effectiveness of our approach in accurately pinpointing individuals within images, enabling a comprehensive assessment of public participation in the restoration of historic residential areas. Our method obtains a CIDEr value of 127.0 and an F value of 0.8856. Moreover, our method proficiently characterizes participants’ emotional states, providing valuable technical support for societal development.

Soaring energy demand in power sector demands an intelligent electricity grid which is able to meet the energy requirements of consumers with higher efficiency and reliability. This paper discusses about how Smart Grid (SG) is the solution for such requirements in the energy sector. SG constitutes of three main components: Information Technology (IT), Operational Technology (OT), and Advanced Metering Infrastructure (AMI). It is important to synchronize these three components in order to seamlessly deliver electricity to the consumers connected to the electric network. Cyberattack is increasing sharply in the SG infrastructure and triggers disrupting or malicious manipulation of entire electricity network. It is crucial to maintain the data traffic, power transmission and distribution with high reliability. This paper aims at arriving at the taxonomy of attacks that are subjected to IT, OT and AMI components of SG. This exhaustive taxonomy further opens avenues to evolve mitigation strategies for these attacks which will eventually aid in SG protection. Elaborate discussion about various mitigation strategies for various types of attacks is performed. The systematic approach of methods and techniques along with the type of solution it provides to SG applications is discussed. It is evident from literature that Machine Learning (ML), Deep Learning (DL) and signal processing techniques are very efficient and robust in attack detection and retaliating in SG. ML model pro. Hence the importance of framework and tools in SG environment is analyzed. A case study is also presented that emphasizes on SG Attacks on the specific system and its mitigation strategies.

Mobile app developers struggle to prioritize updates by identifying feature requests within user reviews. While machine learning models can assist, their complexity often hinders transparency and trust. This paper presents an explainable Artificial Intelligence (AI) approach that combines advanced explanation techniques with engaging visualizations to address this issue. Our system integrates a bidirectional Long Short-Term Memory (BiLSTM) model with attention mechanisms, enhanced by Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). We evaluate this approach on a diverse dataset of 150,000 app reviews, achieving an F1 score of 0.82 and 89% accuracy, significantly outperforming baseline Support Vector Machine (F1: 0.66) and Convolutional Neural Network (CNN) (F1: 0.72) models. Our empirical user studies with developers demonstrate that our explainable approach improves trust (27%) when explanations are provided and correct interpretation (73%). The system’s interactive visualizations allowed developers to validate predictions, with over 80% overlap between model-highlighted phrases and human annotations for feature requests. These findings highlight the importance of integrating explainable AI into real-world software engineering workflows. The paper’s results and future directions provide a promising approach for feature request detection in app reviews to create more transparent, trustworthy, and effective AI systems.

The fitting efficiency of lines or arcs fitting methods for various structural designs cannot meet the design requirements, and the fitting accuracy also needs to be improved. Therefore, this paper proposes a novel algorithm that uses a smaller number of elliptical arc segments to accurately fit the curve, while point-to-point continuity and a tangent relationship between all connected line segments of the curve can be maintained (G1 continuity). This algorithm uses elliptical arcs for curve fitting, and the advantage of using this conventional quadratic curve is that it does not require a higher-order equation to achieve that all connected line segments are tangent to each other, and its feature data is easily obtainable. In this paper, the novel elliptic arc fitting algorithm is used for the sine function curve, butterfly curve, parabola function curve, spiral line function curve and NURBS curve. The results show that this proposed method could be used to fit the curves with a smaller number of elliptical arc segments while satisfying the accuracy requirements. This algorithm with a simple solution method requires a significant reduction in the number of fitting segments while achieving the same fitting accuracy as other algorithms. It provides new ideas for the field of geometric modeling, tool path generation for numerical control (NC) machining and robot path planning.

This paper presents a state-of-the-art planar microwave sensor designed for highly precise alcohol characterization in aqueous solutions, with a primary focus on its application in COVID-19 disinfectants. Modified from the Jerusalem patch, the sensor operates at 5.58 GHz, achieving a unique balance between heightened sensitivity and cost-effectiveness. A tailor-made 3D-printed case minimizes errors, securely housing the sensor and feeding tube. The sensor effectively discriminates between ethanol and methanol, revealing a notable 16 MHz frequency gap. In COVID-19 applications, it maintains alcohol percentages at 65–75%, with 1% increments. The paper outlines a mathematical model extracting concentrations with the maximum error of only smaller than 1.81%, affirming the sensor’s precision. Beyond technical prowess, the sensor’s non-destructive nature, real-time monitoring applicability, and freedom from life-cycle limitations mark it as an innovative tool for checking the percentage of alcohol and types of alcohol before using it to kill the virus, contributing significantly to global efforts on disinfectant measurements with noninvasive nature and high precision. This modified Jerusalem sensor stands as a transformative solution, offering unprecedented advantages in design, operational capacity, and broader support for virus-killing applications.

The development of abstractive summarization methods is a crucial task in Natural Language Processing (NLP) that presents challenges, which require the creation of intelligent systems that are capable of extracting the main idea from text effectively and generate coherent summary. Numerous existing abstractive approaches do not take into account the importance of the broader context or fail to capture the global semantics in identifying salient content for summary. Moreover, there is lack of studies that extensively evaluated abstractive summarization models for specific domains, such as medical scientific document summarization. With this motivation behind, this paper developed an integrated framework for abstractive summarization of medical scientific documents that integrates topic-aware Heterogeneous Graph Neural Network with a Transformer model. The suggested framework uses Latent Dirichlet Allocation (LDA) for topic modeling to uncover latent topics and global information, thus preserving document-level attributes important for creation of effective summaries. In addition to topic modeling, the framework utilized a Heterogeneous Graph Neural Network (HGNN), capable of capturing the relationship between sentences through graph-based document representation, and allows for the concurrent updating of both local and global information. Finally, the framework is integrated with a Transformer decoder, which greatly enhances the ability of model to produce accurate and informative abstractive summaries. The performance of proposed framework is evaluated on publicly available PubMed dataset related to medical scientific papers. Experimental results illustrate that the suggested framework for abstractive summarization showed superior performance as compared to the state-of-the-art models, achieving high F1-Scores: 46.03 for Rouge-1, 21.42 for Rouge-2, and 39.71 for Rouge-L. Our research makes a significant contribution to the field of natural language processing, particularly in the area of medical scientific document summarization. It demonstrates superior performance and provides a deeper understanding of document structure, and has the potential to impact various applications by offering efficient access to information.

With the increasing penetration of natural gas in power systems, the resilience of electric-gas coupled distribution networks under extreme fault perturbations has received increasing attention. Since the entire phase resilience of an electric-gas coupled distribution network after an extreme event depends on its system function, it is necessary to consider a multi-phase recovery process in order to comprehensively improve the resilience and rapid recovery of electric-gas coupled distribution networks against extreme events. In this paper, a fast fault isolation and service restoration methodology that integrates active islanding effect and RCS is proposed to improve the resilience and fast recovery capability of electric-gas coupled distribution networks. Firstly, an electric-gas coupled distribution network model is constructed, after which the multi-stage recovery process is modeled in detail by combining the dynamic mechanism of the load supply curve of the electric-gas coupled distribution network under the whole process of extreme faults, including the topological constraints and power constraints of each stage and the coupling relationship between each stage. The effectiveness of the proposed multi-stage resilience optimization method is verified using the IEEE33 node power system and the natural gas 7-node distribution system as examples. It is demonstrated that the multi-stage restoration method proposed in this paper can greatly improve the resilience of coupled electric-gas distribution networks.

In order to find a simpler method to simulate the charging of spacecraft surface and further analyze the characteristics of the charging reaction of low-orbit spacecraft surface materials, this paper uses COMSOL to perform finite element analysis on the charging of low-orbit spacecraft surface materials at an altitude of 300–400 km. First, the surface charging potential is established using the particle transport equation. By calculating the thermal diffusion movement of particles, the adsorption, secondary electron emission and backscattering effects of the material surface are evaluated to simulate the charging potential of the spacecraft surface. The results show that COMSOL based on the plasma module can replace SPIS to simulate the charged properties of spacecraft surface materials, indicating that different physical properties of the material surface are attached with different magnitudes of potential and small potential differences; In addition, based on the particle transport equation, this paper obtains the surface charging results of the Kapton connector between the solar panel and the spacecraft body, which is prone to charging. This research content provides a simplified method and new direction for the simulation study of spacecraft surface charging.

In the modern manufacturing system, the industrial automation plays an important role in improving the efficiency, productivity, and overall performance of the system by increasing throughput, reducing downtime and more. However, challenges arise in implementation of real-time continuous data streaming and the integration of diverse data types, posing significant track events. To overcome the challenges, the present paper proposes a methodological approach that employs linear mixed models for data integration and online stochastic gradient descent with the Adaptive Gradient algorithm for processing streaming data in real-time. The use of linear mixed models proves advantageous, allowing for the effective integration of data from diverse sources. The methodology captures complex relationships within the data, offering a comprehensive understanding of the industrial process. A case study of predictive maintenance systems for industrial machinery is demonstrated for empirical validation for the online stochastic gradient descent with Adaptive Gradient facilitates real-time processing of streaming data. This adaptive algorithm ensures continuous adjustment to evolving data patterns, and providing a holistic view of industrial processes. Through empirical results, the demonstration of the system’s efficiency in increasing throughput and in-built system visualization is achieved. This paper contributes to the advancement of industrial automation by presenting an innovative solution for handling disparate data types in a unified framework. The findings cover the way for improved operational efficiency and reliability in complex industrial environments.

This review paper provides a comprehensive overview of recent advances in nuclear quadrupole resonance (NQR) spectroscopy for substance detection, highlighting its principles, methodologies, and applications. The paper elucidates the fundamental physics underlying NQR spectroscopy, emphasizing the interaction between nuclear quadrupole moments and electric field gradients. It explores the various experimental techniques and instrumentation developments that have enabled the sensitive detection and precise characterization of substances containing quadrupolar nuclei. A significant portion of the survey is dedicated to discussing the diverse applications of NQR spectroscopy, including the detection of explosives, drug pharmaceuticals, and material authentication. Furthermore, the survey examines the challenges and limitations associated with NQR spectroscopy, including issues related to signal-to-noise ratio (SNR), temperature dependency, and substance restrictions. Strategies to overcome these challenges are discussed, offering insights into the future directions of NQR spectroscopy research that includes artificial intelligence (AI), internet of things (IoT) integration, incorporating a cloud database for NQR parameter storage, and multi-modal analysis.

Customer segmentation is an important aspect in aiding businesses to comprehensively understand their customer base and tailor their marketing strategies for optimal effectiveness. Traditional approaches to segmentation have predominantly concentrated on demographic factors and observable characteristics. However, these approaches have limitations that prevent them from capturing the intricate user journeys of each identified segment. Hence, this paper proposes an approach to customer segmentation using clustering algorithms, specifically the K-Means, BIRCH, and Gaussian Mixture Model on the dataset derived from the Wi-Fi advertising system, with a focus on tracking the user progression through the stages of the AIDA (Attention, Interest, Desire, Action) Model. This paper not only presents an AIDA-based metric designed for Wi-Fi advertising data, it also strives to measure the different stages in the user journey analysis. Through the combination of the AIDA Model and the clustering algorithms, the main objective is to gain a nuanced understanding of the distinct stages characterizing the user journey within each identified segment. This approach further incorporates a dynamic-characteristics range table to delineate the weak and strongly engaged behavioral traits, thereby demonstrating the efficacy of combining the AIDA Model with the clustering algorithms in unraveling nuanced insights into customer behavior across diverse stages of the user journey for each segmented group. Based on the detailed AIDA levels of each user segment, it suggests actionable insights for businesses to enhance marketing strategies by identifying which stages to emphasize, ultimately leading to improved campaign effectiveness and user satisfaction.

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, the path planning problem in UAV cooperative operations has become increasingly prominent. This paper studies the path planning problem of UAV swarms, aiming to propose an effective method to improve the efficiency and safety of task execution. The Marine Predator Algorithm (MPA) is a popular optimization algorithm known for its high performance. However, it sometimes encounters issues such as low efficiency in spiral flight and slow predator speed, leading to slow convergence and susceptibility to local optima. To address these limitations, this paper proposes the Multi-strategy Enhanced Marine Predator Algorithm (MEMPA). The four enhancement strategies are: random spiral flight strategy, crisscross strategy, centroid boundary control strategy, and improved update rules for eddy formation and the FADs’ effect. These strategies collectively enhance the algorithm’s global search ability and its capacity to escape local optima. Compared with FDB-PPSO, AGWO, HPHHO, LSHADE, LSHADE-cnEpSin, MadDE, NMPA, QQLMPA, and the original MPA across four different dimensions of the 30 CEC2017 test functions, the Wilcoxon rank sum test results show that MEMPA outperforms the original MPA by 21, 19, 22, and 25 on the four dimensions, respectively. According to the results of the Friedman mean rank test, the top three algorithms are LSHADE, MPA, and MEMPA, with scores of 2.91, 2.77, and 2.14, respectively. Additionally, the proposed MEMPA was used to solve the UAV swarm path planning problem. The results show that the solutions obtained by MEMPA are 10%, 10%, and 13% better than those obtained by MPA, NMPA, and QQLMPA, respectively. It is superior to the selected comparison algorithms in terms of solution effectiveness and stability, demonstrating its potential in practical applications.

The facility layout of the production workshop primarily focuses on the layout planning of production equipment to reduce material handling costs within the workshop. With the development of the low-carbon economy, a scientifically rational facility layout planning contributes to improving overall operational efficiency and reducing carbon emissions in the production process. However, the design of a production workshop facility layout is a complex optimization problem involving multiple objectives, such as minimizing handling costs, maximizing total non-logistics relationships, and optimizing the utilization of workers’ working hours. Additionally, it must adhere to various constraints, including area utilization rates and equipment utilization constraints. Existing layout methods often fall short of meeting the practical requirements of engineering practice. Therefore, to address the optimization challenges related to the facility layout of the production workshop, this paper establishes a comprehensive optimization model, targeting low-carbon logistics within the production workshop and optimizing the overall non-logistical relationships between operational units as the optimization objectives. Subsequently, this paper proposes an improved Particle Swarm Optimization (PSO) method, considering task collaboration, to solve the integrated optimization model of the facility layout for the valve component production workshop at Company A. Finally, the validity of the model and algorithm is confirmed through example calculations and result analysis. The analysis results demonstrate that, under the same conditions, the improved PSO algorithm outperforms PSO and SGA (Simple Genetic Algorithm) algorithms in terms of optimization results, iteration counts, and runtime. In conclusion, this study introduces task collaboration to enhance the traditional PSO algorithm. Simultaneously, we consider both logistics and non-logistics relationships in optimizing facility layout design. This provides theoretical references and new solving algorithms for low-carbon logistics.

Time-Sensitive Networking (TSN) is an in-development technology that enables predictability over Ethernet or wireless networks. Network interfaces compliant with the IEEE 802.1Qbv standard provide different queues/gates on each bridge egress port. In this way, a global network schedule can be set by defining the opening and closing times (Gate Control List, GCL) for each gate. In this paper, we propose a new method to schedule GCLs by dividing the problem into several subproblems. We use Weighted Fair Queuing (WFQ) to set the ordering of frames, and then generate an Integer Linear Programming (ILP) model to optimize the TSN scenario. Next, we assign gates to the scheduled windows, trying to ensure frame isolation whenever possible. Our results show that we can schedule GCLs around 2 times faster than previous studies and up to 5.5 orders of magnitude faster if we choose to obtain any valid solution instead of the optimal one. In addition, we are able to schedule systems with utilization up to 85%, whereas previous papers reach 65%. Moreover, our approach does not need to predefine the number of windows or gates, as required by other methods.

In scenarios involving photovoltaic power generation and electrical dynamic loads, complex electricity metering signals often exhibit strong randomness and rapid fluctuations. These characteristics frequently lead to substantial errors in electricity metering, thereby affecting fair and equitable energy transactions. This paper presents a novel modal decomposition theory in the run-length domain. Such a theory is developed to map signals from the amplitude domain to run sequences in the run-length domain. Subsequently, it decomposes signals into a quasi-steady, slowly varying mode, and another dynamic, rapidly fluctuating mode, facilitating the extraction of sensitive characteristics over prolonged durations. Furthermore, the paper proposes characteristics representation method for complex electricity metering signals by constructing parameters and characteristic functions in the run-length domain. Additionally, the paper specifically extracts sensitive characteristics for photovoltaic complex electricity metering signals using the modal decomposition theory in the run-length domain. Finally, experimental validation is conducted to elucidate the impact of these sensitive characteristics on electricity meter errors with the maximum dynamic error observed at −15.53%.

Predicting the price of soybean meal futures has always been a hot topic in the financial market. It is influenced by many factors and has complex nonlinear characteristics, which makes it difficult to predict accurately. This paper proposes a prediction model for soybean meal futures price based on CNN-ILSTM-SA, which consists of CNN (Convolutional Neural Network), ILSTM (Improved Long Short-Term Memory), and SA (Self-Attention mechanism). CNN can well perform feature extraction on the input time series data. The ILSTM model presented in this paper removes the output gate of LSTM (Long Short-Term Memory) and improves the forget gate and input gate, thus reducing ILSTM’s complexity, shortening training time, and improving learning ability. SA can get the correlation between different input terms and predicted values, and assign different weight coefficients according to the correlation level to improve prediction accuracy. To verify the CNN-ILSTM-SA’s effectiveness, we compare it with eighteen baseline models, and the experimental results show that it is optimal. The MAE (Mean Absolute Error) of CNN-ILSTM-SA is 39.0441, the smallest of all models. As the data of the soybean meal futures price fluctuates in the range of 3500-4500 in experiments, the proportion of the MAE value of 39.0441 is 0.87%-1.15%, which is at an extremely low level; $\mathrm {R}^{2}$ (R-square) is 0.97487, which is closest to 1; The training time is 157.64s.

Considering the zoned shock effeteness, a multi-degradation system model subject to dependent competing failure processes is developed in this paper. The multi-degradation is constructed by the degradations in both a soft failure model and a hard failure model. Reliability assessment of the soft failure model has been widely studied, but degradation in a hard failure process with an extreme shock model has been rarely studied. The existing methods adopted approximate approaches to evaluate extreme shock model reliability with threshold degradation, which could not satisfy the accuracy requirement of system reliability calculation. To improve the calculation accuracy, a multi-degradation model subject to stochastic-dependent competing failure processes is investigated in this paper. A distribution-based algorithm is developed to calculate the extreme shock model reliability with threshold degradation. Furthermore, according to the effect of the shock process in the hard failure process, threshold degradation can be divided into degradation caused by both the zoned shock process and general degradation. Consequently, the closed-form formulas for system reliability with the multi-degradation model are derived. A case study is employed to illustrate the developed model, and the effectiveness of the algorithm is validated through Monte Carlo simulations.

A soft starter is commonly utilized in various industrial induction motor systems to reduce the inrush current during motor startup. The conventional voltage ramp-up method, used in the typical soft starter, gradually increases the voltage over the startup period. However, this method can result in a significant voltage increase as the induction motor approaches its rated speed, resulting in a rapid torque increase compared to the beginning part of the startup phase. This sudden torque increase can potentially cause physical damage to the induction motor system due to a transient current overshoot, which in turn can result in resonance. Additionally, the changes in the parameters of the induction motors can lead to the variations in torque during startup, especially because of the air gap permeance. Therefore, a firing angle control method for the soft starter that maintains a constant torque, facilitating a gradual increase in speed, is desirable. This paper proposes a novel firing angle control method, to achieve a constant torque and a gradual speed increase, using only current feedback. This paper is the first time that a change in air gap permeance is considered and compensated for in controlling the firing angle of the soft starter. The air gap permeance variation is modeled as an effective inertia in the transient response of the induction motor. Experimental results demonstrate that the proposed method effectively reduces rapid torque increases and speed variations compared to the conventional voltage ramp-up method, through its application to a 30kW industrial induction motor system.

Blockchain is gaining popularity as a technology that provides secure data sharing and management for the Internet of vehicles (IoV). The current research on cross-chaining between blockchains is still inefficient, and how to perform efficient cross-chain data communication has not been well studied. This paper studies the cross-chain protocol in the IoV environment, and designs a lightning-c cross-chain protocol based on the lightning network. The protocol consists of three steps. The user opens the cross-chain channel through election of nodes, and conducts transactions in the cross-chain channel, then the cross-chain channel is closed and the transaction is broadcasted to the blockchain. Our proposed approach enables efficient interoperability between heterogeneous blockchains. Compared with the traditional cross-chain protocol BTCRelay, it is proved that the lightning-c cross-chain mechanism can greatly improve the throughput of cross-chain transactions. The experimental results show that the method proposed in this paper is more efficient.

This paper presents the robust linear quadratic regulator (LQR) control for the uncertain modular multilevel converters (MMCs). The classical LQR control prioritizes optimizing the performance index but lacks the ability to address converter uncertainties. This paper takes into account this limitation by developing a model of the MMC that considers parameter uncertainties. To enable LQR control in uncertain MMC systems, an optimization approach for the performance index employing linear matrix inequality (LMI) technique is proposed. Consequently, a novel robust control strategy for MMCs is established. The real-time performance of this LMI-based LQR control is evaluated against classical LQR control under both nominal and non-nominal conditions and single phase-to-ground fault using Opal-RT system. For this purpose, as the switching function model sufficiently captures all aspects of the MMC’s behavior, the switching function model of the MMC is implemented on the Opal-RT OP4610XG system for real-time emulation of MMC. The results from real-time simulation of the system indicate that the proposed LMI-LQR method offers an advantage in robustness over the classical LQR method in presence of parameters uncertainty.

Alzheimer’s disease is a progressive disease for which researchers have yet to discover the main cause, but believe it probably involves a combination of age-related changes in the brain, genetic, environmental and lifestyle factors. Alzheimer’s is an irreversible disease that still has no cure. Therefore, its early diagnosis is very important to prevent its progression. Developing Machine Learning algorithms in healthcare, especially in brain disorders such as Alzheimer’s disease, provides new opportunities for early diagnosis and recognition of important biomarkers. This paper presents an overview of advanced studies based on Machine Learning techniques for diagnosing Alzheimer’s disease and different stages of mild cognitive impairment based on magnetic resonance imaging (MRI) images in the last 10 years. Also, this paper comprehensively describes the commonly efficient Machine Learning algorithms in each stage of magnetic resonance imaging processing used in the papers, which can facilitate the comparison of algorithms with each other and provide insight into the impact of each technique on classification performance. This review can be a valuable resource to gain a new perspective on the various research methods used in recent studies on Alzheimer’s disease.

High-speed permanent magnet synchronous motors face significant stator current harmonics issues due to small stator inductance and low carrier ratio. LCL topology family of filters are unable to ensure optimal filtering performance across the full frequency domain. This paper proposes a novel high-order dual-trap filter for high-speed permanent magnet synchronous motor (HS-PMSM) to enhance the filtering performance. The proposed filter utilizes the stator inductance to construct the LCL-based topology, enabling high attenuation of harmonics in the high-frequency range, and trap branches achieve strong suppression of harmonics near the switching frequency and its multiples. Furthermore, the filter improves robustness in response to parameter variations by tuning parameters of trap branches. The paper improves the filter parameter design guidelines by considering the specific characteristics of HS-PMSM. Additionally, a PI control parameter design methodology is presented, utilizing the amplitude margin and phase margin of the open-loop transfer function. The effectiveness and feasibility of the proposed method is validated through comprehensive experimental analyses, comparing the performance of traditional filters against the proposed filter.

This paper studies the synchronization and tracking issue for the connected and automated vehicle (CAV) platoon with mixed-order nonlinear vehicle dynamics. Previous researches have demonstrated that vehicle platoons can improve traffic efficiency and minimize the incidence of traffic collisions. However, vehicles within the same platoon may have different-order dynamics in practical scenarios. The heterogeneity issue of a vehicle platoon caused by the different-order dynamics is difficult to address by existing platoon control methods. Therefore, the platoon synchronization and tracking control problem for the heterogeneous CAV platoon which consists of mixed-order vehicles with a hybrid of second- and third-order dynamics is formulated in this paper. The proposed novel distributed sliding mode (DSM) control protocols for vehicles with second- and third-order dynamics aim to guarantee the convergence of vehicle state errors and the stability of the heterogeneous platoon. The distributed neural network-based adaptive estimation technique are developed in this paper to approximate the unknown uncertainties and compensate for the model nonlinearities of the platoon system. The uniform ultimate boundedness of errors and string stability are substantiated through the Lyapunov theory and infinity-norm technique. The efficacy of the developed platoon control strategies can be verified by the numerical examples.

This paper explores the scenario that a jammer attacks an intelligent transmitter which can sense and adapt to the jamming environment. Given the non-cooperative relationship between the transmitter and the jammer, two main challenges are addressed in this paper: how to model their interactions and how to devise a jamming strategy without prior knowledge of the transmitter. A non-zero-sum game is used to model and analyze such non-cooperative interactions. An approximate mixed-strategy Nash equilibrium (NE) under complete information is derived to serve as a benchmark for comparison. According to the non-zero-sum game model, a Deep Q-Network (DQN) approach is proposed to determine jamming strategies by exploiting the detection results of the legitimate signals, such as Acknowledgements (ACKs) feedback and the modulation recognition results obtained by the jammer. Simulation results demonstrate that, without requiring complete information about the transmitter, the proposed DQN approach can achieve a similar utility as the benchmark strategy using complete information. Compared to other learning-based jamming schemes and random jamming strategy, the proposed DQN approach achieves a higher packet error rate for the communication transceiver with reduced jamming power consumption.

A miniature single-piston valve flow-matching water-hydraulic pump is described in this paper, which designs with a unique double-face cam mechanism to achieve the reciprocating motion of the piston and a new flow-matching valve to adapt to the requirements of the frequency of flow-matching, and the use of a brushless DC motor to regulate the speed and control the flow of the pump. In this paper, the mechanical structure and working principle of this new type of water-hydraulic pump are described, the mathematical model of the valve flow distribution process is established, the transfer functions of the suction and drain process are obtained, and the relationship between the structural parameters of the distribution valve and the transient output characteristics of this type of water-hydraulic pump is simulated, and the simulation results show that the quality of the valve spool is the key factor affecting the switching speed of the distribution valve. The transient pressure and transient flow rate of the pump are studied experimentally, which reveals the pulsation law of the output characteristics of this single-piston pump, and lays a theoretical foundation for the design of a double-piston pulsation-free hydraulic pump.

The overall network volume in backbone optical networks is constantly growing, composed of many smaller network services with increasing trends and various seasonality. Due to the recent advances in machine learning algorithms, short- and long-term traffic fluctuations can be forecasted. Consequently, the backbone optical network can be adapted to traffic changes aiming to improve its performance. However, an important challenge lies in developing effective optimization methods capable of adapting to traffic changes to leverage the knowledge about the traffic. To this end, this paper addresses the time-varying traffic in spectrally-spatially flexible optical networks (SS-FONs), which are a promising technology to mitigate backbone network requirements of vast traffic volume transmission. The main contribution of this paper is twofold. Firstly, we introduce a new traffic prediction method using multioutput regression and temporal features to forecast traffic between all node pairs and integrate this prediction method into an optimization framework developed for dynamic resource allocation in translucent SS-FONs with time-varying traffic. Secondly, we evaluate potential network performance improvements from periodic lightpath reallocation through extensive numerical experiments. According to the results of experiments run on two representative optical network topologies, the proposed approach with periodic resource allocation allows achieving up to 7.8 percentage points reduction of bandwidth blocking compared to the reference scenario without reallocation. Consequently, the network requires up to 23.4% fewer transceivers to deliver the same traffic in considered scenarios and thus the power consumption savings are provided.

Capacitive deionization is used to purify water by applying an electric charge across two porous electrodes. These electrodes are positioned in a spacer channel that transports the desalinated water. Capacitive deionization is a more energy-efficient approach to water purification than traditional methods. The process is believed to create clean water and store energy in a capacitive electrical double layer near the charged surface. Unlike previous review papers in this area, which mostly focus on energy recovery in terms of the technological advancement, this paper aims to conduct a bibliometric review and analysis of capacitive deionization cells, specifically focused on energy recovery mechanisms for treating contaminated water. The manuscript also analyzes capacitive deionization energy efficiency and energy-efficient capacitive deionization technology. Various relevant databases are utilized to select articles under specific conditions. Our findings suggest that the utilization capacitive deionization’s energy recovery mechanism from industrial wastewater has valuable opportunities for further research and growth. The selection and review of relevant articles and their analysis in this paper can aid in the systematic development of the capacitive deionization module. This would offer viable suggestions for future research towards more efficient energy recovery and consumption, which will subsequently contribute to net zero transitioning.