Search

Data anomaly detection in small hydropower stations is an important research area because it positively affects the reliability of optimal scheduling and subsequent analytical studies of small hydropower station clusters. Although many anomaly detection algorithms have been introduced in the data preprocessing stage in various research areas, there is still little research on effective and highly reliable anomaly detection systems for practical applications in small hydropower stations. Therefore, this paper proposes a real‐time data anomaly detection system for small hydropower clusters (RDADS‐SHC) considering multiple time series. It addresses the difficulties of timely detection, alerting, and management of real‐time data anomalies (errors, omissions, and so on) in existing small hydropower stations. It proposes a real‐time data anomaly detection algorithm for small hydropower stations integrated with the Z‐score and dynamic time warping, which can detect and process abnormal information more accurately and efficiently, thereby improving the stability and reliability of data sampling. The paper proposes a Keepalived‐based hot‐standby RDADS‐SHC deployment model with m (m ≥ 2) units. It can automatically remove and restart faulty services and switch to their standbys, which significantly improve the reliability of the proposed system, ensuring the safe and stable operation of related functional services. This paper can detect anomalous data more accurately, and the system is more stable and reliable in a cluster detection environment. The actual operation has shown that compared with existing anomaly detection systems, the architecture and algorithms proposed in this paper can detect anomalous data more accurately, and the system is more stable and reliable in the small hydropower cluster detection environment. It solves abnormal data management in small hydropower stations and provides reliable support for subsequent analysis and decision‐making. [ABSTRACT FROM AUTHOR]

The complexity of the interconnected grid and the continuous increase of new energy sources have led to an acute problem with low‐frequency oscillation (LFO) in power system. Identification and monitoring of LFO in power grid are prerequisites for effective control of low‐frequency oscillation phenomena. To address matter that the traditional S‐transform time‐frequency window function has a fixed scale and cannot be applied to the specific local characteristics of different signals, an adaptive generalized S‐transform algorithm based on a bat algorithm is proposed in this paper. It uses adjustment parameters to control the generalized Gaussian window function. The parameters are automatically adjusted by a bat algorithm adaptive optimization to find the best time‐frequency characterization. Secondly, the PMU data waveform with implicit low‐frequency oscillation information is converted into a two‐dimensional time‐frequency figure including the onset moment, frequency, and amplitude. The system enables identification and visual monitoring of low‐frequency oscillations. After that, simulation experiments of New England system are conducted. The superiority of the proposed method is verified, which can greatly improve the time‐frequency resolution of PMU active power data signal and has effective noise immunity. [ABSTRACT FROM AUTHOR]

Accurate and timely forecasting of critical components is pivotal in intelligent transportation systems and traffic management, crucially mitigating congestion and enhancing safety. This paper aims to comprehensively review deep learning algorithms and classical models employed in traffic forecasting. Spanning diverse traffic datasets, the study encompasses various scenarios, offering a nuanced understanding of traffic forecasting methods. Reviewing 111 seminal research works since the 1980s, encompassing both deep learning and classical models, the paper begins by detailing the data sources utilized in transportation systems. Subsequently, it delves into the theoretical underpinnings of prevalent deep learning algorithms and classical models prevalent in traffic forecasting. Furthermore, it investigates the application of these algorithms and models in forecasting key traffic characteristics, informed by their utility in transport and traffic analyses. Finally, the study elucidates the merits and drawbacks of proposed models through applied research in traffic forecasting. Findings indicate that while deep learning algorithms and classic models serve as valuable tools, their suitability varies across contexts, necessitating careful consideration in future studies. The study underscores research opportunities in road traffic forecasting, providing a comprehensive guide for future endeavors in this domain. [ABSTRACT FROM AUTHOR]

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]

Stroke is a high morbidity and mortality disease that poses a serious threat to people's health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representation capabilities and can automatically learn discriminant features from large amounts of data. This paper uses a range of physiological characteristic parameters and collaborates with deep neural networks, such as the Wasserstein generative adversarial networks with gradient penalty and regression network, to construct a stroke prediction model. Firstly, to address the problem of imbalance between positive and negative samples in the stroke public data set, we performed positive sample data augmentation and utilized WGAN‐GP to generate stroke data with high fidelity and used it for the training of the prediction network model. Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning‐based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. The prediction results of the proposed method are optimal in the comprehensive measurement index F. Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases. [ABSTRACT FROM AUTHOR]

Currently, regardless of the algorithm used, motion planners for dealing with dynamic obstructions need to rely on high‐precision sensors and high performance processors. The requirements for hardware increase as the density of dynamic obstructions in an area becomes higher. Additionally, motion planners are more prone to errors in complex environments. The Rapidly‐exploring Random Tree (RRT ∗) algorithm only considers static obstructions and cannot effectively avoid densely populated regions of dynamic obstructions. This paper develops an improved RRT ∗ algorithm that is capable of avoiding densely populated regions of dynamic obstructions. In this algorithm, the cost function of the traditional RRT ∗ algorithm is modified based on the density of dynamic obstructions, allowing the planned path to bypass densely populated regions. The algorithm also introduces reasonable penalty terms to penalize segments that pass through densely populated regions, while maintaining asymptotic optimality of the traditional RRT ∗ algorithm. Numerical experiments reveal that the improved RRT ∗ algorithm is able to successfully avoid densely populated regions of dynamic obstructions with minimal time cost and exhibits better robustness during the path search process in comparison to the traditional RRT ∗ algorithm. Thus, the improved RRT ∗ algorithm possesses the ability to adapt to more complex areas for path planning. [ABSTRACT FROM AUTHOR]

Traditional short‐term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short‐time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO‐VMD) based on the optimization of the gray wolf search algorithm. It aims to decompose and reduce the noise of short‐time traffic flows. Meanwhile, it reduces the intricacy of data sequences and enhances the regularity pattern. To address the insufficient utilization of spatiotemporal features, this paper presents an innovative deep‐learning traffic prediction framework based on the stacking of multiple temporal trend‐aware graph attention (TGA) layers and gated temporal convolution (GTC) layers, which are called trend‐aware temporal graph neural network (TTGAN). TGA dynamically models the space‐time relationships of traffic data, and GTC models the temporal characteristics of traffic data. The experimental findings demonstrate that the MAPE model, as presented, achieves a reduction of 9% and 2% compared to the AGCRN and GWNET models, respectively, in the domain of deep spatiotemporal graph modeling. Data decomposition and noise reduction are necessary to achieve accurate results. This model has superior performance in terms of mean absolute error (MAE), coefficient of determination (R2), and explained variance score (EVAR). [ABSTRACT FROM AUTHOR]

A novel hybrid algorithm combining the 3‐D vector finite element method‐boundary integral method (FEM‐BIM) with the half‐space Green's function with the rough‐surface interface is developed in this paper to study the scattering from a 3‐D coated target above a perfectly electric conducting (PEC) rough surface. The half‐space Green's function is derived from the Kirchhoff approximation (KA). The 3‐D vector FEM is applied to model the coated object, and the BIM with the half‐space Green's function is selected as the exact radiation boundary conditions. The accuracy of our numerical code is validated by the numerical method. Some numerical results are provided to study the electromagnetic scattering characteristics from a coated sphere above a Gaussian rough surface. [ABSTRACT FROM AUTHOR]

In response to the challenges of path planning in complex scenarios, to overcome the influence of optimal path determination by the precision of grid map sizes, and to escape the uncertainty in solving by intelligent algorithms, this paper designs a method for obtaining an adjacency matrix based on node planning of shortest path diagrams with polygonal obstacles and then uses the Dijkstra algorithm to get the shortest path. For irregular curved obstacles, an edge straightening method is proposed. To enhance the applicability of the path, this paper introduces the constraint of minimum turning radius. It researches path smoothing under obstacle conditions based on arcs and straight lines, providing practical solutions for different scenarios. Considering the need to maintain a safe distance due to the size of the moving carrier and the deviation in trajectory tracking, this paper conducts an expansion analysis of obstacles. It obtains the trajectory on the arc after edge straight line fitting, followed by further smoothing treatment. The method proposed in this paper demonstrates excellent accuracy and robustness in path planning through simulation verification, proving its practicality and effectiveness in complex environments. [ABSTRACT FROM AUTHOR]

The rapid development of mobile Internet has promoted the rapid rise of cloud computing technology. Mobile terminal devices have greatly expanded the service capacity of mobile terminals by migrating complex computing tasks to run in the cloud. However, in the process of data exchange between mobile terminals and cloud computing centers, on the one hand, it consumes the limited power of mobile terminals, and on the other hand, it results in longer communication time, which negatively affects user QoE. Mobile cloud can effectively improve user QoE by shortening the data transmission distance, reducing the power consumption, and shortening the communication time at the same time. In this paper, we utilize the property that genetic algorithm can perform global search seeking the global optimal solution and construct a dynamic task scheduling model by combining the device-cloud link. The task scheduling model based on genetic algorithm and random scheduling algorithm is compared through comparison experiments, which show that the assignment time of the task scheduling model based on genetic algorithm is shortened by 11.82% to 48.51% and the energy consumption is reduced by 22.28% to 47.52% under different load conditions. [ABSTRACT FROM AUTHOR]

The paper proposes a method to solve the time fractional Fokker–Planck equation using the generalized Hermite spectral method and Laplace transform. By the Laplace transform, the ordinary differential equation about the orthogonal expansion coefficient is transformed into a linear algebraic equation system, with the coefficient matrix being a tridiagonal lower triangular matrix. The orthogonal expansion coefficients of frequency domain components are obtained through this transformation. The approximate solution at any time is then obtained by using the numerical inverse Laplace transform with the Talbot algorithm. Numerical experiments have been carried out to demonstrate the high accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is extended by introducing fog computing nodes to reduce the communication delay. It reduces the transmission bandwidth and privacy leakage risk while improving the accuracy of jointly learned global and local models. Then, the histogram-based approximation calculation method is improved to adapt to the unbalanced data characteristics of wireless sensor networks. Finally, by introducing TOP-K gradient selection, the number of model parameter uploads is minimized, and the efficiency of model parameter interaction is improved. The experimental results show that this algorithm has superior detection performance and low energy consumption. It is also compared with other algorithms to demonstrate the high detection rate and low computational complexity of this algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the reentry phase of the Aerospaceplane is taken as the research object, and the performance parameters of the reusable rocket of a private company are analyzed. Aiming at the guidance and control scheme of the spacecraft returning to the reentry trajectory in the real environment, the original natural algorithm is optimized by considering various reentry flight constraints, and the improved original natural algorithm is used to optimize the reentry trajectory of the Aerospaceplane. We obtained two types of reentry trajectories in the presence of large flight-restricted areas, the "S-type" trajectory and the "spiral-type" trajectory, and obtained data on various influencing factors. The results showed that the basic state parameters of the spiral trajectory optimized using the improved original natural algorithm after adding constraints met the constraint conditions. The aerodynamic heating rate and overload of the spiral reentry trajectory were to some extent greater than those of the S-type trajectory. Under the increasingly stringent requirements of the aerospace environment, new requirements were put forward for the thermal protection system to meet the wider environmental situation. This paper uses the improved original natural algorithm for the first time and applies it to the field of aerospace reentry and entry and adds more constraints to this algorithm for computation. Besides, for the first time, the macroscopic nature of trajectory types is used as a comparative element for parameter comparison, providing a reference basis for selecting trajectory optimization directions from the macroscopic perspective of trajectory types. [ABSTRACT FROM AUTHOR]

In long‐range missions based on angle‐of‐arrival positioning, the angle measurement error of unmanned aerial vehicles is a major source of error. Therefore, reducing the unmanned aerial vehicle angle measurement error is crucial to achieve accurate remote positioning. In this paper, we propose a space vector–based method to correct the space vector of the target for the unmanned aerial vehicles when there are fewer than three available reference points, which in turn corrects the angular value of the target relative to the unmanned aerial vehicles. Simulation results show that when the distance between the reference point and the unmanned aerial vehicles is smaller than the distance between the target and the unmanned aerial vehicles, the azimuth measurement error can be reduced to 55% of the original error for the case of a single reference point, while the pitch angle measurement error remains almost unchanged. In the case of more than two reference points, the azimuth measurement error can be reduced to 1e5 and the pitch angle measurement error can be reduced to 30% of the original error. This method can be adapted to the rapid positioning task for high‐speed and high‐mobility targets without iteration, low computation, good correction effect, and the need of prior known data set reference. [ABSTRACT FROM AUTHOR]

In this paper, to improve the small signal stability of the power system, the optimal coordination of multi‐band power system stabilizers (MB‐PSSs) and static synchronous compensator (STATCOM) has been done. The study is conducted on a standard four machines and two areas power system. To create coordination, a modified version of the particle swarm algorithm as well as the integral time absolute error (ITAE) criterion are proposed. To evaluate the proposed stability method in different operating conditions and in the form of two scenarios, a three‐phase fault with a duration of 200 ms has been applied in the communication line between the two areas. After the simulation, the values of overshoot, undershoot, settling time, and ITAE criteria for the proposed stability method have been calculated and their values have been compared with the results of power system stabilizer (PSS) and MB‐PSS stabilization methods. The results indicate that the proposed combined method of MB‐PSS and STATCOM performed better than the other two methods in improving the dynamic stability of the power system and has the lowest value of the criteria. The values of ITAE for the PSS, MB‐PSS stabilization methods and the proposed MB‐PSS and STATCOM combined method in the first scenario are equal to 0.002354, 0.0.001496, and 0.001242 p.u. and in the second scenario it is equal to 0.0.007418, 0.004015, and 0.003435 p.u., respectively. [ABSTRACT FROM AUTHOR]

Power transformers are indispensable components for energy transmission and voltage regulation. Since the leakage impedance affects the short‐circuit current, magnetic leakage distribution, and manufacturing cost, the accurate calculation of transformer impedance is vital for transformer design. Generally, leakage impedance is mainly decided by the design parameters of the windings, both analytically and numerically. In most given literature studies, the leakage impedance was optimized and analyzed by adjusting the design parameters of the windings without considering the consequent influence on transformer loss and manufacturing costs. A multiobjective optimization model considering the leakage impedance, manufacturing cost, and operating loss of the windings is presented in this paper. The eXtreme Gradient Boosting (XGBoost) model is built using 2048 finite‐element analysis (FEA) samples and utilized as a leakage impedance predictor. XGBoost shows better accuracy and consumes much less time compared to analytical and FEA methods. Subsequently, the presented multiobjective model is optimized using multiobjective algorithms and the NSGA‐III shows the best performance among the NSGA‐II, MOPSO, and MODE. The results show that the leakage impedance is closer to the required value. Besides, the winding manufacturing cost and loss are meanly decreased by 4.7% and 4.1%, respectively, in the engineering case. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have been extensively researched and deployed in both military and civilian applications due to their tiny size, low cost, and great ease. Although UAVs working together on complicated jobs can significantly increase productivity and reduce costs, they can cause major issues with path planning. In complex environments, the path planning problem, which is a multiconstraint combinatorial optimization problem and hard to settle, requires considering numerous constraints and limitations and generates the best paths for each UAV to accomplish group tasks. In this paper, we study the path planning problem for multiple UAVs and propose a reinforcement learning algorithm: PERDE‐MADDPG based on prioritized experience replay (PER) and delayed update skills. First, we adopt a PER mechanism based on temporal difference (TD) error to enhance the efficiency of experience utilization and accelerate the convergence speed of the algorithm. Second, we use delayed updates in the process of updating network parameters to ensure stability in training multiple agents. Finally, we propose the PERDE‐MADDPG algorithm based on PER and delayed update skills, which is evaluated against the MATD3, MADDPG, and SAC methods in simulation scenarios to confirm its efficacy. [ABSTRACT FROM AUTHOR]

This paper is concerned with two generalized inverse eigenvalue problems for a kind of pseudo‐Jacobi matrix. From a non‐Hermite matrix, an r × r Jacobi matrix, two distinct real eigenvalues, and part of the corresponding eigenvectors, an n × n pseudo‐Jacobi matrix is constructed. Furthermore, an n × n pseudo‐Jacobi matrix can be made by two different eigenpairs and a positive definite diagonal matrix. It is shown that a unique pseudo‐Jacobi matrix can be recovered from partial eigenpairs and certain special mixed eigendata. Two algorithms are provided for the reconstruction of such a pseudo‐Jacobi matrix, and illustrative numerical examples are presented to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 × 224) were input into an Xception transfer learning model with a modified output. Both Xception's architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we develop the theoretical provisions of symmetric cryptographic algorithms based on the polynomial residue number system for the first time. The main feature of the proposed approach is that when reconstructing the polynomial based on the method of undetermined coefficients, multiplication is performed not on the found base numbers but on arbitrarily selected polynomials. The latter, together with pairwise coprime residues of the residue class system, serve as the keys of the cryptographic algorithm. Schemes and examples of the implementation of the developed polynomial symmetric encryption algorithm are presented. The analytical expressions of the cryptographic strength estimation are constructed, and their graphical dependence on the number of modules and polynomial powers is presented. Our studies show that the cryptanalysis of the proposed algorithm requires combinatorial complexity, which leads to an NP-complete problem. [ABSTRACT FROM AUTHOR]

Despite the extensive research on developing robust image inpainting algorithms in recent years, there are almost no objective metrics for the quality assessment of inpainted images currently. Inspired by the feature coherence in the inpainted image and the human visual perception mechanism, this paper proposes an image inpainting quality assessment (IIQA) that takes into account both visual saliency and structural features. First, the quality issues associated with image inpainting are categorized into three aspects: incoherent structure, unreasonable texture, and other results that are inconsistent with human visual perception. These quality problems are further expressed as "regions of interest" and extracted by the visual saliency method using the natural statistics model. Subsequently, the structural features are computed based on the nonlinear diffusion of the horizontal and vertical gradient field of the inpainted image. Finally, the IIQA metric incorporates brightness, gradient similarity, structural similarity, and visual saliency is established. The quality evaluation process is conducted by comparing each patch within the inpainted region with its best match from the known region. The quantitative experimental results demonstrate the effectiveness of the proposed method, especially for images with structural discontinuity. A comparative study also shows that the Spearman rank order correlation coefficient of our method achieves 0.875 on certain databases, which outperforms existing IIQA metrics. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) has inexpensive, small, and less energy sensor nodes, which are allocated in random ways in particular areas for measuring the phenomenon or events in that field. In recent days, WSN has played a vital role in various applications, like industrial monitoring, medical treatments, agricultural monitoring, and military operations. However, the security challenges and network lifetime are the main issues in the existing methods. In order to overcome these issues, the Taylor-Spotted Cat Optimization (Taylor-SCO) approach is devised in this paper. Here, the Cluster Heads (CHs) are selected based on the developed optimization method, named Taylor CSO. Moreover, the delay, distance, and energy parameters are considered for effective Cluster Head Selection (CHS). Here, route maintenance is also done for increasing network lifetime and reducing complexities. In addition, the Modified K-Vertex Disjoint Paths Routing (KVDPR) model is established for routing. The modification of KVDPR is carried out using several factors, such as link reliability, throughput, and various trust factors. Moreover, the developed Taylor-SCO algorithm is developed by combining the Spotted Hyena Optimizer (SHO), Cat Swarm Optimization (CSO) algorithm, and Taylor series. The Taylor-SCO achieved better performance with energy consumption, trust, and throughput of 0.00037 J, 0.51, and 793160 kbps. [ABSTRACT FROM AUTHOR]

The goods collection and delivery process often relates to distribution logistics problems. The task is to deliver goods from warehouses to customers under specific circumstances. Efforts to optimize the process are largely aimed at reducing overall costs of goods transportation. Among the prominent algorithms for solving the basic type of the delivery (or collection) problem, which includes a single depot and a homogeneous vehicle fleet, is the algorithm developed by Clarke and Wright in 1964. This algorithm minimizes transportation costs by maximizing the savings achieved through merging multiple routes into one. This paper primarily aims to solve the pickup and delivery problem where the goods must be delivered and empty packaging collected in a single process. The request of a customer can be routed from the depot or from another customer. Similarly, the destination of the request may be the depot or another customer. Unlike the original version of the Clarke and Wright algorithm, the initial routes are created to satisfy delivery orders, and therefore, the same customer can occur in multiple routes. Consequently, a situation may arise in which two routes containing one or more common vertices must be combined during the calculation. Furthermore, these vertices need not be the outermost vertices of the routes. This situation cannot be addressed by using the original version of the Clarke and Wright algorithm, and that is why we propose its modification. Merging routes through inner vertices means that the cost savings depend on the configurations of the routes, and therefore, they cannot be calculated a priori. Instead, the dynamic savings matrix must be used. [ABSTRACT FROM AUTHOR]

Cables are important components of long-span bridge structures, whose operation is significantly affected by cable force changes. Nowadays, cable force testing is performed by physical methods; that is, sensors are installed on the cable structure to monitor its force changes. Obviously, this strategy requires an extensive amount of time to achieve cable force calculation, which makes it impossible to monitor the force of the cable structure in real time. Meanwhile, smartphones have attracted extensive attention in the field of structural health monitoring (SHM) because of their higher cost-effectiveness than accelerometers, which include price and lifespan. Besides, many people own a smartphone, which leads to the possibility of a wider range of applications. Therefore, this paper presents a framework for the rapid estimation of the cable force of long-span bridges based on smartphones-captured video and a template matching algorithm. First, the empirical mode decomposition (EMD) method with wavelet decomposition (WD) method, that is, the EMDWD model, is constructed to extract the vibration signal of the bridge cable by eliminating the effects of smartphone vibration and environmental noise on the measured dynamic displacement, thus effectively improving the accuracy of data processing. In addition, the vibration identification model of bridge cable based on a template matching algorithm is established, and the deformation curve of cable is obtained. Finally, the frequency of bridge suspender is calculated by the Fourier transform method (FFT), and the cable force is estimated based on the smartphone-captured video. [ABSTRACT FROM AUTHOR]

In recent years, solar photovoltaic (PV) energy, as a clean energy source, has received widespread attention and experienced rapid growth worldwide. However, the rapid growth of PV power deployment also brings important challenges to the maintenance of PV panels, and in order to solve this problem, this paper proposes an innovative algorithm based on PA-YOLO. First, we propose to use PA-YOLO's asymptotic feature pyramid network (AFPN) instead of YOLOv7's backbone network to support direct interactions of nonadjacent layers and avoid large semantic gaps between nonadjacent layers. For the occlusion problem of dense targets in the dataset, we introduce a repulsive loss function, which successfully reduces the occurrence of false detection situations. Finally, we propose a customized convolutional block equipped with an EMA mechanism to enhance the perceptual and expressive capabilities of the model. Experimental results on the dataset show that our proposed model achieves excellent performance with an average accuracy (mAP) of 94.5%, which is 6.8% higher than YOLOv7. In addition, our algorithm also succeeds in drastically reducing the model size from 71.3 MB to 48.4 MB, which well demonstrates the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have the advantages of resilience, global optimization capacity, and coding flexibility, making them helpful in tackling difficult optimization issues. The enhanced wind-driven optimization (CHTWDO) that was proposed in this paper coupled the chaotic map approach and the hyperbolic tangent with the T-distribution mutation method. The initial air particles are evenly distributed in the system space through a tent mapping strategy. Meanwhile, the variation probability of the hyperbolic tangent model and the T-distribution variation method are used to improve the comprehensive performance of the algorithm. In this way, the global search accuracy and the ability of avoiding the extreme value of the algorithm can be taken into account. Combining the three strategies, CHTWDO had higher global search accuracy and a stronger ability to jump out of local extremum. Comparing with the eight meta-heuristic algorithms (including WDO) and the single strategy improved WDO on 24 test functions, the experimental results show that CHTWDO with two improved strategies has better convergence precision and faster convergence speed. Statistical tests such as Friedman's and Wilcoxon's rank-sum tests are used to determine significant differences between these comparison algorithms. Finally, CHTWDO also obtains the best results on four classical optimization problems in engineering applications, which verifies its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

The development of the internet of things (IoT) and 6G has given rise to numerous computation-intensive and latency-sensitive applications, which can be represented as directed acyclic graphs (DAGs). However, achieving these applications poses a huge challenge for user equipment (UE) that are constrained in computational power and battery capacity. In this paper, considering different requirements in various task scenarios, we aim to optimize the execution latency and energy consumption of the entire mobile edge computing (MEC) system. The system consists of single UE and multiple heterogeneous MEC servers to improve the execution efficiency of a DAG application. In addition, the execution reliability of a DAG application is viewed as a constraint. Based on the strong search capability and Pareto optimality theory of the cuckoo search (CS) algorithm and our previously proposed improved multiobjective cuckoo search (IMOCS) algorithm, we improve the initialization process and the update strategy of the external archive, and propose a reliability-constrained multiobjective cuckoo search (RCMOCS) algorithm. According to the simulation results, our proposed RCMOCS algorithm is able to obtain better Pareto frontiers and achieve satisfactory performance while ensuring execution reliability. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement. [ABSTRACT FROM AUTHOR]

The quality of collision detection algorithm directly affects the performance of the whole simulation system. To address the low efficiency and low accuracy in detecting the collisions of flexible cloths in virtual environments, this paper proposes an oriented bounding box (OBB) algorithm with a simplified model, tree structure for a root-node double bounding box, and continuous collision detection algorithm incorporating an OpenNN-based neural network optimization. First, for objects interacting with the cloths with more complex modeling, the model is simplified with a surface simplification algorithm based on the quadric error metrics, and the simplified model is used to construct an OBB. Second, a bounding box technique commonly used for collision detection is improved, and a root-node double bounding box algorithm is proposed to reduce the construction time for the bounding box. Finally, neural networks are used to optimize the continuous collision detection algorithm, as neural networks can efficiently process large amounts of data and remove disjoint collision pairs. An experiment shows that the construction of an OBB using the simplified model is almost identical to that of the original model, but the taken to construct the OBB is reduced by a factor of approximately 2.7. For the same cloth, it takes 5.51%–11.32% less time to run the root-node double bounding box algorithm than the traditional-hybrid bounding box algorithm. With an average removal rate nearly identical to that of the traditional filtering method, the elapsed time is reduced by 7%–11% by using the continuous collision detection algorithm based on an OpenNN neural network optimization. The simulation results are realistic and in line with the requirements for real-time cloth simulations. [ABSTRACT FROM AUTHOR]

With the emergence and vigorous development of 5G technology, there is a significant surge in network usage and traffic, resulting in heightened complexity within network and IT environments. This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). This function grants network operators the flexibility to either employ their own data analytics methodologies based on machine learning (ML) and deep learning (DL) into their networks. In this paper, we present a dataset named "NWDAF-NFPP" for network function performance time series prediction, collected from a laboratory at China Telecom. The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. The availability of this dataset provides valuable resources for researchers to conduct time series prediction research on network element performance. Following data collection, a total of six models were employed for network element performance prediction, encompassing both machine learning and deep learning approaches. This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. This comparative analysis will provide valuable insights into the strengths and limitations of each model, aiding in informed decision-making for network optimization and management strategies in the future. [ABSTRACT FROM AUTHOR]

In this paper, a variable-structure multimodel (VSMM) filtering algorithm based on the long short-term memory (LSTM) regression-deep Q network (L-DQN) is proposed to accurately track strong maneuvering targets. The algorithm can map the selection of the model set to the selection of the action label and realize the purpose of a deep reinforcement-learning agent to replace the model switching in the traditional VSMM algorithm by reasonably designing a reward function, state space, and network structure. At the same time, the algorithm introduces a LSTM algorithm, which can compensate the error of tracking results based on model history information. The simulation results show that compared with the traditional VSMM algorithm, the proposed algorithm can quickly capture the maneuvering of the target, the response time is short, the calculation accuracy is significantly improved, and the range of adaptation is wider. Precise tracking of maneuvering targets was achieved. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods. [ABSTRACT FROM AUTHOR]

The Pareto dominance-based evolutionary algorithms can effectively address multiobjective optimization problems (MOPs). However, when dealing with many-objective optimization problems with more than three objectives (MaOPs), the Pareto dominance relationships cannot effectively distinguish the nondominated solutions in high-dimensional spaces. With the increase of the number of objectives, the proportion of dominance-resistant solutions (DRSs) in the population rapidly increases, which leads to insufficient selection pressure. In this paper, to address the challenges on MaOPs, a knee point-driven many-objective evolutionary algorithm with adaptive switching mechanism (KPEA) is proposed. In KPEA, the knee points determined by an adaptive strategy are introduced for not only mating selection but also environmental selection, which increases the probability of generating excellent offspring. In addition, to remove dominance-resistant solutions (DRSs) in the population, an interquartile range method is adopted, which enhances the selection pressure. Moreover, a novel adaptive switching mechanism between angle-based selection and penalty for selecting solutions is proposed, which is aimed at achieving a balance between convergence and diversity. To validate the performance of KPEA, it is compared with five state-of-the-art many-objective evolutionary algorithms. All algorithms are evaluated on 20 benchmark problems, i.e., WFG1-9, MaF1, and MaF4-13 with 3, 5, 8, and 10 objectives. The experimental results demonstrate that KPEA outperforms the compared algorithms in terms of HV and IGD in most of the test instances. [ABSTRACT FROM AUTHOR]

Among the directional angle calculation models of Bluetooth 5.1, the rectangular hollow array offers the advantage of shorter sampling time due to its fewer elements compared to traditional planar arrays. However, the antimultipath algorithms suitable for traditional planar arrays cannot be applied to rectangular hollow arrays. Therefore, this study proposes a virtual array filling algorithm, wherein four virtual matrices are inserted into the hollow matrix to transform the array into a uniform rectangular array. This algorithm ensures translation invariance of the rectangular array, enabling the application of the antimultipath coherent source algorithm to a rectangular hollow array. An algorithm for reconstructing Toeplitz matrices in two‐dimensional uniform planar arrays is also proposed. Through the analysis of the spatial spectrum and angle estimation results of various algorithms, the effectiveness of the signal angle of arrival estimation theory is verified. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization model for the train operation scheme of the Airport Express Line (AEL) based on the expected arrival time of passengers by the introduction of the train departure time to cope with the time-dependent passenger flow and provide better prompt train service according to passengers' demand. Considering factors such as train sections, station arrangement, passenger capacity, departure time windows, passenger flow conservation, and boarding and disembarkation processes, this paper also aims to find the optimal combination of the passengers' total travel time and the train operation cost. A set of alternative train options is introduced to simplify the model and convert integer variables related to train pairs into 0-1 variables. The elaborately designed simulated annealing algorithm mainly focuses on the key elements of strategies like initial solution generation, neighborhood solution construction, and the allocation of passenger flows, tailored to the model's unique features and the time-dependent passenger flow. Neighborhood solution strategies include the increase or haut of train operations and the adjustment of the number of stops, which refines the solution space and boosts the process efficiency of the heuristic algorithm. Additionally, the model and algorithm proposed in this paper are practiced during the peak hour of Nanjing Metro Line S1 for empirical validation. The research findings demonstrate that the optimized train operation scheme is better synchronized with the fluctuating number of time-dependent passenger flows and exhibits notable improvement in computational efficiency and convergence., Author(s): Jin He [1,2]; Yinzhen Li (corresponding author) [1]; Yuhong Chao [3]; Ruhu Gao [1,2] 1. Introduction In the context of rapid urbanization, the optimization problem of the Airport Express, [...]

To improve soldiers' combat capability, weapon arms have a good development prospect. However, due to special work scenarios and tasks, new requirements are exerted on. Based on the fast-expanding random tree algorithm (RRT), path algorithm optimization (RRT-H) is proposed for the path planning of weapon arms. Overall path optimization is achieved by reducing the local path length with a closer path point planning to the obstacle. In a complex environment, the RRT-H algorithm can avoid local traps by guiding the new path extension direction and exploring multiple different paths in the map. The superiority of this algorithm is verified with 2D plane obstacle avoidance and pathfinding simulation experiments. Compared to RRT ∗ , RRT ∗ smart, and information RRT ∗ , the RRT-H can obtain high-quality calculation results in a shorter time. After setting degrees of freedom (DOF) as that of variables, the algorithm is applied to the 4-DOF weapon arm, which confirms an effective reduction to the 4-DOF weapon arm's motion costs. [ABSTRACT FROM AUTHOR]

Background: The global aging population has raised concerns about the fair distribution of elderly care resources. China, as the largest developing country, has made efforts to address population aging challenges. However, equitable distribution of elderly care resources remains a concern. This study analyzed the spatial layout and accessibility of elderly care service facilities (ECSFs) in Xiamen City to improve resource allocation and enhance the elderly care system in China. The findings provide valuable insights for other developing countries and regions seeking to improve their own elderly care resource allocation. Methods: Xiamen City was chosen as the research area due to its unique geographical location and advanced information technology infrastructure. The study examined the spatial distribution, accessibility, and equity of ECSFs. Using Python, point of interest (POI) data were collected and evaluated using the kernel density method and two‐step floating catchment area method. Spatial autocorrelation analysis identified areas of aggregation and dispersion in ECSF accessibility. Results: Xiamen currently has 660 ECSFs, including nursing homes, adult day care centers, home care agencies, and rural elderly care homes. The analysis revealed spatial disparities, with ECSFs clustering primarily in the central area of Xiamen Island. Significant differences in accessibility were found among the four types of ECSFs. Spatial autocorrelation analysis identified cold and hot spot areas, indicating variations in accessibility across regions. Conclusion: Xiamen City has made progress in allocating elderly care resources and constructing service facilities. However, equity in resource allocation remains a concern. Areas with limited accessibility were identified, leading to unequal access to elderly care resources and timely physical care. To address these challenges, decision‐making departments should consider increasing facilities, improving transportation, enhancing macro planning, and improving facility service quality and accessibility. These measures will optimize ECSF accessibility and promote equitable distribution of elderly care resources. [ABSTRACT FROM AUTHOR]

Image processing is a promising technique for enhancing images or extracting useful information from them. One commonly used density‐based clustering algorithm is DBSCAN (Density‐Based Spatial Clustering of Applications with Noise). However, DBSCAN struggles with satellite images due to their large sizes, often resulting in excessively long computation times. This research proposes an improved version of DBSCAN called "Enhanced DBSCAN‐based Histogram" (EDBSCAN‐H) to address these issues. EDBSCAN‐H enhances DBSCAN by incorporating a histogram‐based approach to better manage large datasets and reduce computation time. The key improvement lies in using the histogram of the input image and measuring the distance between data objects and histogram points to determine whether a region is dense or sparse, thereby selecting suitable parameters. EDBSCAN‐H introduces an additional parameter, ε2, alongside the original DBSCAN parameters ε₁ and MinPts. This enhancement allows EDBSCAN‐H to achieve improved performance across various metrics for clustering spatial data images. [ABSTRACT FROM AUTHOR]

Hybrid metaheuristics is one of the most exciting improvements in optimization and metaheuristic algorithms. A current research topic combines two algorithms to provide a more advanced solution to optimization problems. The present study applies a new approach called HWOA‐TTA which means a hybrid of the whale optimizer algorithm (WOA) and tiki‐taka algorithm (TTA). The hybrid WOA‐TTA combines the exploitation phase of WOA with the exploration phase of TTA. Two stages in the hybridized model are suggested. First, the WOA exploitation phase incorporates the TTA mechanism. Second, a new approach is included in the research phase to enhance the result with each iteration to a set of unconstrained benchmark test functions and engineering design applications. To verify the performance of the improved algorithm, thirteen benchmark functions have been used to compare HWOA‐TTA with the classical intelligent population algorithms (PSO, TTA, and WOA). The hybrid algorithm is applied to two well‐known engineering mathematical models. The experiments show that the HWOA‐TTA outperforms other algorithms. [ABSTRACT FROM AUTHOR]

Process discovery approaches analyze the business data to automatically uncover structured information, known as a process model. The quality of a process model is measured using quality dimensions, completeness (replay fitness), preciseness, simplicity, and generalization. Traditional process discovery algorithms usually output a single process model. A single model may not accurately capture the observed behavior and overfit the training data. We have formed the process discovery problem in a multiobjective framework that yields several candidate solutions for the end user who can pick a suitable model based on the local environmental constraints (possibly varying). We consider the Binary Differential Evolution approach in a multiobjective framework for the task of process discovery. The proposed method employs dichotomous crossover/mutation operators. The parameters are tuned using grey relational analysis combined with the Taguchi approach. We have compared the proposed approach with the well‐known single‐objective algorithms and state‐of‐the‐art multiobjective evolutionary algorithm—Nondominated Sorting Genetic Algorithm (NSGA‐II). Additional comparison via computing a weighted average of the quality dimensions is also undertaken. Results show that the proposed algorithm is computationally efficient and produces diversified candidate solutions that score high on the fitness functions. It is shown that the process models generated by the proposed approach are superior to or at least as good as those generated by the state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

The main objective of this research project is to demonstrate a valuable method for scheduling an SSSC damping controller installed in a single‐machine infinite bus (SMIB) power system. Over time, a damping controller has been used in power systems to reduce low‐frequency oscillation. The configuration challenge associated with this configuration is regarded as an optimization problem. A modified optimization algorithm (ZOA) that can be used to adjust the controller parameters is constructed by modifying the original ZOA algorithm. The suggested study distinguishes between the mZOA method, traditional ZOA, and conventional PSO algorithms. Based on the simulation findings, it can be inferred that the modified approach that has been suggested is the most effective method for defining the said damping controller by considering the percentage improvement in the objective function value. [ABSTRACT FROM AUTHOR]

The rapid increase in e-commerce and the emergence of combined passenger/freight systems in urban areas have raised the question of how best to integrate public transport services into door-to-door deliveries. This paper develops a variant of the pickup and delivery problem, called the two-echelon pickup and delivery problem using public transport (2E-PDP-PT). In the 2E-PDP-PT, the transportation network is split into two echelons. Different vehicles are utilized across the first and second echelons to ensure distribution efficiency. Parcels are delivered by public transport with free capacity or via trucks between satellites in the first echelon, and logistics vehicles are operated in the second echelon. The satellites are located at the echelon borders to transfer commodities between echelons. The 2E-PDP-PT aims to minimize total delivery costs and improve public transport capacity utilization. We formulate a new mathematical model based on a space-time network and adopt an adaptive large neighborhood search (ALNS) algorithm for the 2E-PDP-PT. The effectiveness of the ALNS algorithm is validated using newly generated small-scale instances. Furthermore, we investigate large-scale instances based on the Beijing Yizhuang transportation network. The computations show that an average total delivery cost savings of 4.5% is feasible. In addition, we analyze the impact of demand distributions and compare the ALNS algorithm and the LNS algorithm. Finally, we conclude that dynamically integrating public transport into freight transport services can benefit both logistics companies and public transport operators., Author(s): Shuai Wang [1]; Xiaoning Zhu [1]; Pan Shang (corresponding author) [1]; Wenqian Liu [1]; Xiao Lin [2]; Lóránt Tavasszy [2] 1. Introduction The substantial growth of e-commerce contributes to [...]

The multiverse optimizer (MVO) is increasingly recognized across various scientific disciplines for its robust search capabilities that enhance decision‐making in diverse tasks. Despite its strengths, MVO often encounters limitations due to premature convergence, reducing its overall efficiency. To combat this, the study introduces an enhanced version of MVO, termed the improved MVO (iMVO), which incorporates an opposition‐based learning (OBL) strategy to overcome this limitation. The effectiveness of iMVO is assessed through a series of tests involving both classical and IEEE CEC 2021 benchmark functions, demonstrating competitive performance against established algorithms. Moreover, the applicability of iMVO to real‐world challenges is validated through its successful deployment in civil engineering tasks, particularly in optimizing truss designs and managing time‐cost tradeoffs. The results highlight iMVO's stability and its promising potential for global optimization scenarios. [ABSTRACT FROM AUTHOR]

Functions in nonlinear programing optimization are sometimes not easily differentiable. When such kinds of functions are present in the optimization problem, then the process of obtaining the solution can sometimes become quite lengthy. Traditional and classical methods cannot be used to solve such optimization problems because such methods assume the functions to be differentiable. Smoothing of nondifferentiable functions is thus required to tackle such difficulties. In this study, we introduce the concept of constructing a technique to smooth such nondifferentiable functions. We begin with the smoothing of the penalty function. On the basis of it, we come up with an algorithm to find the best way to solve an optimization problem with inequality constraints. We also talk about how to figure out the error for a certain smooth penalty function. We also provide numerical examples to demonstrate that the suggested approach is practical and usable. [ABSTRACT FROM AUTHOR]

The extent to which active noise cancelation (ANC), when combined with hearing assistance, can improve speech intelligibility in noise is not well understood. One possible source of benefit is ANC's ability to reduce the sound level of the direct (i.e., vent-transmitted) path. This reduction lowers the "floor" imposed by the direct path, thereby allowing any increases to the signal-to-noise ratio (SNR) created in the amplified path to be "realized" at the eardrum. Here we used a modeling approach to estimate this benefit. We compared pairs of simulated hearing aids that differ only in terms of their ability to provide ANC and computed intelligibility metrics on their outputs. The difference in metric scores between simulated devices is termed the "ANC Benefit." These simulations show that ANC Benefit increases as (1) the environmental sound level increases, (2) the ability of the hearing aid to improve SNR increases, (3) the strength of the ANC increases, and (4) the hearing loss severity decreases. The predicted size of the ANC Benefit can be substantial. For a moderate hearing loss, the model predicts improvement in intelligibility metrics of >30% when environments are moderately loud (>70 dB SPL) and devices are moderately capable of increasing SNR (by >4 dB). It appears that ANC can be a critical ingredient in hearing devices that attempt to improve SNR in loud environments. ANC will become more and more important as advanced SNR-improving algorithms (e.g., artificial intelligence speech enhancement) are included in hearing devices. [ABSTRACT FROM AUTHOR]

An automated flower thinning system, when combined with machine vision, has the potential to reduce the labor force, improve efficiency, and lower costs. This combination represents the future of agricultural machinery development. The primary objective of automatic flower thinning is to determine the flowering density of fruit trees under natural light conditions. In this study, we introduce a flower recognition algorithm that uses pixel values as an independent variable to recognize flower categories by constructing a nonlinear regression model. Initially, the RGB pixel values of elements in the training set are extracted. Similar pixel values are clustered together to reduce the amount of computation, and representative elements are selected to construct a nonlinear classification function, known as the regression function. The coefficients in the classifier are determined by transforming the problem into an unconstrained optimization problem using the least square method. The optimal solution is then found as the coefficient value in the classifier. The classification function calculates the function value of the RGB pixel value for each input entity to determine whether it belongs to the flower entity. Finally, the developed algorithm is used to classify the flower graphic elements of the measured pictures, and the efficiency of the algorithm is verified. [ABSTRACT FROM AUTHOR]

In today's data‐driven world, we are constantly exposed to a vast amount of information. This information is stored in various information systems and is used for analysis and management purposes. One important approach to handle these data is through the process of clustering or categorization. Clustering algorithms are powerful tools used in data analysis and machine learning to group similar data points together based on their inherent characteristics. These algorithms aim to identify patterns and structures within a dataset, allowing for the discovery of hidden relationships and insights. By partitioning data into distinct clusters, clustering algorithms enable efficient data exploration, classification, and anomaly detection. In this study, we propose a novel centroid‐based clustering algorithm, namely, the morphological accuracy clustering algorithm (MAC algorithm). The proposed algorithm uses a morphological accuracy measure to define the centroid of the cluster. The empirical results demonstrate that the proposed algorithm achieves a stable clustering outcome in fewer iterations compared to several existing centroid‐based clustering algorithms. Additionally, the clusters generated by these existing algorithms are highly susceptible to the initial centroid selection made by the user. [ABSTRACT FROM AUTHOR]

With the advancement of image steganography, coverless image steganography has gained widespread attention due to its ability to hide information without modifying the carrier of images. However, existing coverless image steganography methods often require both communicating parties to transmit an amount of additional information including image blocks' locations or a large number of parameters, which will raise a serious suspicion. In light of this issue, we propose a robust coverless image steganography algorithm based on Speeded-Up Robust Features (SURF). Firstly, the proposed method allows both communicating parties to independently create multiple coverless image datasets (CIDs) using random seeds. Then, a mapping rule is designed for creating one-to-one correspondence between hash sequences and images in CIDs. Finally, the secret information will be carried by the images whose hash sequences are equal to the secret segments. At the receiver side, the robust SURF of images is utilized to retrieve the secret information. Experimental results demonstrate that the proposed algorithm outperforms other methods in terms of capacity, robustness, and security. Furthermore, it is worth noting that the proposed method eliminates the need to transmit a large amount of additional information, which is a significant security issue in existing coverless image steganography algorithms. [ABSTRACT FROM AUTHOR]

We present a deep learning-based method that corrects motion artifacts and thus accelerates data acquisition and reconstruction of magnetic resonance images. The novel model, the Motion Artifact Correction by Swin Network (MACS-Net), uses a Swin transformer layer as the fundamental block and the Unet architecture as the neural network backbone. We employ a hierarchical transformer with shifted windows to extract multiscale contextual features during encoding. A new dual upsampling technique is employed to enhance the spatial resolutions of feature maps in the Swin transformer-based decoder layer. A raw magnetic resonance imaging dataset is used for network training and testing; the data contain various motion artifacts with ground truth images of the same subjects. The results were compared to six state-of-the-art MRI image motion correction methods using two types of motions. When motions were brief (within 5 s), the method reduced the average normalized root mean square error (NRMSE) from 45.25% to 17.51%, increased the mean structural similarity index measure (SSIM) from 79.43% to 91.72%, and increased the peak signal-to-noise ratio (PSNR) from 18.24 to 26.57 dB. Similarly, when motions were extended from 5 to 10 s, our approach decreased the average NRMSE from 60.30% to 21.04%, improved the mean SSIM from 33.86% to 90.33%, and increased the PSNR from 15.64 to 24.99 dB. The anatomical structures of the corrected images and the motion-free brain data were similar. [ABSTRACT FROM AUTHOR]