For the power control problem of a bidirectional voltage-source converter, because only one voltage vector is applied at each control period, the traditional model predictive direct power control strategy brings in high harmonic content. In this paper, a hybrid vector-modulation model predictive direct power control scheme is developed to address the high harmonic distortion issue under unbalanced grid conditions. Based on the instantaneous power theory, the expression for the power fluctuation is derived under unbalanced grid voltages. The new reference power under unbalanced grid voltages is represented by the α β components of the grid voltages and their orthogonal signals lagging by 9 0 ∘ . By means of the proposed strategy, neither the phase-locked loops nor the sequence extraction of the grid voltages are required. In addition, the optimal voltage vector is selected from eighteen alternative hybrid voltage vectors to further improve control performance. The effectiveness of the proposed strategy is verified through real-time simulations conducted on an RT-LAB hardware-in-the-loop platform.

Rapid prediction of unsteady flow fields around airfoils is crucial for aerodynamic design and optimization of aircraft. However, traditional wind tunnel experiments and numerical simulation methods incur high computational costs for flow field calculations. This paper proposes an improved method based on deep neural operator networks for rapidly predicting unsteady flow fields of different symmetric airfoils under high angle-of-attack conditions. The method uses incoming flow conditions and airfoil parameters as inputs, reconstructing the flow field through spatiotemporal coordinates to achieve joint prediction of pressure and velocity. Experimental results demonstrate that this method accurately captures the unsteady flow field characteristics of different symmetrical airfoils at various high angles of attack. The inference time for a single time-step case takes only about 0.6 s, which is significantly faster than traditional numerical simulation methods. The proposed method can substantially enhance computational efficiency while maintaining flow prediction accuracy, offering a viable new approach for rapid flow field prediction and aerodynamic design optimization of aircraft.

Modern vehicular systems rely heavily on suspension performance for ride comfort, handling stability, and safety. Conventional passive suspensions, while common, cannot adapt to changing road conditions. Active suspension systems overcome this limitation by using actuators and control algorithms to adjust suspension behavior in real time based on sensor feedback, making them especially valuable for vehicles operating in unpredictable environments. This paper explores the design and comparative evaluation of various active suspension control strategies, including H − ∞ synthesis, μ synthesis, and reinforcement learning-based methods. A quarter-car model with real-world parameters was used for simulation, implemented in both MATLAB and Python. The controllers were tested under multiple excitation profiles: a single hump, five consecutive humps, and standardized ISO 8608 road profiles that emulate realistic terrain variability such as random bumps and potholes. Each control strategy was assessed using performance metrics such as body acceleration and suspension deflection. The results demonstrate that all active suspension systems outperform passive suspensions, with the reinforcement learning controller providing the greatest overall reduction in body displacement.

This paper mainly focuses on the issue of multiple attribute two-sided matching decision-making with uncertain preference and unknown attribute weights under the consideration of prospect value factors and proposes a new decision-making method that incorporates Hamming similarity measure, prospect theory, and grey relational analysis. Firstly, the preference order vectors of both parties towards each other are determined using the multiple attribute two-sided matching object Hamming similarity programming models (MATSMOHSPM), and the initial uncertain preference order aggregation matrices are constructed accordingly. Then, the satisfaction degree calculation formula considering prospect value factors is established by using grey correlation coefficients and prospect theory, and a multiple attribute two-sided matching model with uncertain preference and unknown attribute weights is established. Based on this, a multiple attribute two-sided matching decision-making algorithm with uncertain preference considering prospect value factors is proposed subsequently. Finally, the feasibility and practicality of the algorithm are verified through an example of patent technology transfer. This paper provides a new solution to the problem of determining the initial aggregation matrix with uncertain preference and unknown attribute weights and improves and perfects the previous research on two-sided matching decision-making problems with uncertain preference considering psychological factors such as prospect value.

Freeform surfaces are widely used in advanced manufacturing due to their versatility, yet their complex geometries make characterization challenging. To do a surface texture analysis, it is often necessary to first remove the form of the surface. When the nominal form is unknown, the reference form can be approximated by filtration. If a triangular mesh represents the freeform surface, mesh smoothing is a type of filtration. Among various methods, Laplacian smoothing is the most employed for mesh smoothing due to its linear simplicity. Traditional Laplacian smoothing techniques use uniform, distance, or cotangent weights to approximate the discrete Laplace-Beltrami operator for each vertex. However, Laplacian smoothing has limitations, such as accuracy, shrinkage effects, and edge blurring. This study first investigates and compares Gaussian convolution and Laplacian smoothing techniques, aiming to integrate their advantages. Based on these findings, a novel approach is proposed to enhance Laplacian mesh smoothing by introducing a bilateral weighting scheme for each vertex. This enhancement keeps the simplicity of the Laplacian smoothing structure while mitigating the limitations of traditional weighting functions, offering improved performance in terms of accuracy and efficiency. The proposed method demonstrates potential for better estimating reference forms for freeform surfaces, which is the first step for surface characterization in advanced manufacturing.

Hypoglycemia is a serious complication in diabetes patients, especially for those on insulin treatment. It is typically characterized by abnormally low sugar levels in the body. The common symptoms of hypoglycemia are found to be heart palpitations, anxiety, excessive sweating, shakiness, fatigue, and tremors. In some serious cases, it might lead to seizures or death. Sometimes, intense insulin treatment can lead to hypoglycemia unawareness. It is frequent in people having diabetes for a long duration or those having frequent hypoglycemic episodes. This complication results in the failure of detecting the early symptoms/warnings of low blood sugar levels. Feature extraction is performed on the ECG signal to extract the mean averaged ST level, heart rate and QRS complex. The frequency of the accelerometer signals is extracted. Around 195 ECG recordings from standard data sets, of multiple categories, were tested for validating the system developed. It is observed that only 1.03 percentage of false positives recorded by the system. The design of the proposed hypoglycemia detection system is especially for people of high age group who are highly prone to hypoglycemia and helpless at the same time. The proposed system aims to detect such episodes for them and alert the caregiver.

This paper presents an analytical model for a two-level three-phase four-wire grid-connected voltage source converter (TGC-VSC) controlled by digital pulse-width modulation (DPWM). This model relies on a phase-locked loop (PLL)-less approach to investigate the performance of the dead-beat (DB) and proportional-integral-resonant (PIR) controller in the presence of unbalanced conditions on the alternating current (AC) low-voltage side of the distribution system. A single-phase digital triple-loop control system has been employed for each phase of the TGC-VSC, which operates as a grid-forming inverter (voltage source) or grid-following inverter (current source) coupled to an AC power grid with three phases and neutral. The main aim of this work is to evaluate the previous single-phase approach of the three-loop control system applied in this work to a three-phase four-wire system, with the main difference being the connection to the grid without an PLL and the application of an PIR controller in the third control loop instead of an proportional-integral (PI). Alternatively, the latter can inject active power into the grid to help flatten the demand-side power curve of the industry, reducing operating costs. Therefore, the aim is to test the aforementioned digital controllers in a grid-connected inverter. The controllers are validated using MATLAB/Simulink simulations, yielding the steady-state voltage and current errors ϵ v jo = 1.62 % and ϵ i jo = 3.44 % in grid-forming, and ϵ v jo = 0.10 % , ϵ i jo = 6.55 % and ϵ i jg = 2.86 % in grid-following inverter configurations.

Rare event classification in multivariate time series is a critical yet challenging task across different industries. Traditional methods often struggle to capture the nonlinear and nonstationary dynamics inherent in complex multivariate time series data, limiting their ability to accurately detect rare events. To address these limitations, this study introduces the Vector Visibility Graph-based Graph Attention Network (VVG-GAT) framework, a novel approach that leverages the power of Vector Visibility Graphs (VVG) to represent multivariate time series as directed, weighted graphs. By encoding both temporal and inter-variable dependencies, the VVG facilitates the application of advanced graph neural networks, particularly the Graph Attention Network (GAT), to address the challenges of rare event classification and early detection. The proposed framework was evaluated on a real-world case study involving a pulp-and-paper manufacturing process characterized by rare paper break events. Experimental results demonstrated that the VVG-GAT framework significantly outperformed traditional models across several metrics. The study further highlights the potential of incorporating VVG-derived network statistics as additional features for machine learning and deep learning models. The VVG-GAT framework represents a significant advancement in rare event classification for multivariate time series generated from complex systems, providing a new solution with broad applicability across various domains.

This study focuses on controlling a compliant gripper using least-squares support vector regression (LS-SVR) combined with a particle swarm optimization (PSO) algorithm. The compliant gripper is designed to grip small objects with high precision. However, repeated use can lead to reduced precision due to the hysteresis inherent in the gripper’s mechanism. To address this, an algorithm developed to mitigate the effect of hysteresis is seen to improve control accuracy. The algorithm is further designed to control the end-effector position of the gripper. Simulation results show that applying LS-SVR with the PSO algorithm enhances gripping precision. After implementing the control algorithm, gripping displacement was compared across three configurations: no controller, a conventional proportional–integral (PI) controller, and the proposed LS-SVR with PSO. The root mean square error (RMSE) decreased significantly to 5.13 × 10−2 mm with the proposed controller, compared to 7.03 × 10−2 mm without a controller and 6.69 × 10−2 mm with PI control. These results demonstrate that the LS-SVR with PSO significantly improves the precision of the compliant gripper.

The autonomous navigation capability of USVs in complex water environments relies on Simultaneous Localization and Mapping (SLAM) technology. However, water surface perturbations and reflections generate numerous dynamic invalid feature points, causing low localization accuracy and poor robustness in VSLAM. To this end, we propose a monocular VSLAM algorithm considering dynamic invalid features of the water surface for USV, named WS-SLAM. Firstly, the ORBSLAM3 framework is improved by adding the PP-LiteSeg semantic segmentation thread to distinguish between dynamic invalid feature points of the water surface and feature points on shore. Secondly, semantic information is used to generate a mask to cull dynamic invalid feature points of the water surface, reducing the impact of water current perturbation on visual localization. Then, further culling of potentially residual dynamic feature points on shore is performed in conjunction with the epipolar geometric constraint. Finally, the camera position is estimated using only static feature points. The experimental results based on the public USVInland dataset show that WSSLAM improves the positioning accuracy by an average of 43.47% in straight line sailing compared with ORBSLAM3. It can effectively inhibit the position drift caused by the dynamic invalid features of the water surface and has higher positioning accuracy and stronger robustness.