Search Results (701)

The study of the Earth’s magnetosphere through in situ observations is an important step in understanding the evolution of the Sun–Earth interaction. In this context, the long-term observation of the Earth’s magnetotail using a scientific probe in a high elliptical orbit is a challenging mission scenario due to the alignment of the magnetotail direction with the Sun–Earth line, which requires a continuous rotation of the apse line of the spacecraft’s geocentric orbit. This aspect makes the mission scenario particularly suitable for space vehicles equipped with propellantless propulsion systems, such as the classic solar sails which convert the solar radiation pressure into propulsive acceleration without propellant expenditure. However, a continuous rotation of the apse line of the osculating orbit can be achieved using a more conventional solar electric thruster, which introduces an additional constraint on the duration of the scientific mission due to the finite mass of the propellant stored on board the spacecraft. This paper analyzes the potential of a typical CubeSat equipped with a commercial miniaturized electric thruster in performing the rotation of the apse line of a geocentric orbit suitable for the in situ observation of the Earth’s magnetotail. The paper also analyzes the impact of the size of a thruster array on the flight performance for an assigned value of the payload mass and the science orbit’s characteristics. In particular, this work illustrates the optimal guidance laws that allow us to maximize the duration of the scientific mission for an assigned CubeSat’s configuration. In this sense, this paper expands the literature regarding the study of this interesting mission scenario by extending the study to conventional propulsion systems that use a propellant to provide a continuous and steerable thrust vector. Full article

The model predictive-torque-control strategy of a permanent magnet synchronous motor (PMSM) has many advantages such as a fast dynamic response and the ease of implementation. However, when the permanent magnet motor has a large number of pole pairs or operates at high-speed, due to constraints such as the inverter switching frequency, sampling time, and algorithm execution time, the motor carrier ratio (the ratio of control frequency to operating frequency) becomes relatively low. The discrete model derived from and based on the forward Euler method has a large model error when the carrier ratio decreases, which leads to voltage vector misjudgment and inaccurate duty cycle calculation, thus leading to the decline of control performance. Meanwhile, the shortcomings of the traditional model predictive-torque-control strategy limit the steady-state performance. In response to the above issues, this paper proposes an improved model predictive-torque-control strategy suitable for low-carrier-ratio conditions. The strategy consists of an improved discrete model that considers rotor-angle-position variations and a model prediction algorithm. It also analyzes the sensitivity of model predictive control to parameter changes and designs an online parameter optimization algorithm. Compared with the traditional forward Euler method, the improved discrete model proposed in this paper has obvious advantages under low-carrier-ratio conditions; at the same time, the parameter optimization process enhances the parameter robustness of the model prediction algorithm. Moreover, the proposed model predictive-torque-control strategy has high torque tracking accuracy. The experimental results verify the feasibility and effectiveness of the proposed strategy. Full article

This paper introduces a nonlinear Lyapunov-based Finite-Time State-Dependent Differential Riccati Equation (FT-SDDRE) control scheme, considering actuator saturation constraints and ensuring that the control system operates within safe operational limits designed for satellite reconfiguration and formation-keeping in low Earth orbit (LEO) missions. This control approach addresses the challenges of reaching the relative position and velocity vectors within a defined timeframe amid various orbital perturbations. The proposed approach guarantees precise formation control by utilizing a high-fidelity relative motion model that incorporates all zonal harmonics and atmospheric drag, which are the primary environmental disturbances in LEO. Additionally, the article presents an optimization methodology to determine the most efficient State-Dependent Coefficient (SDC) form regarding fuel consumption. This optimization process minimizes energy usage through a hybrid genetic algorithm and simulated annealing (HGASA), resulting in improved performance. In addition, this paper includes a sensitivity analysis to identify the optimized SDC parameterization for different satellite reconfiguration maneuvers. These maneuvers encompass radial, along-track, and cross-track adjustments, each with varying baseline distances. The analysis provides insights into how different parameterizations affect reconfiguration performance, ensuring precise and efficient control for each type of maneuver. The finite-time controller proposed here is benchmarked against other forms of SDRE controllers, showing reduced error margins. To further assess the control system’s effectiveness, an input saturation constraint is integrated, ensuring that the control system operates within safe operational limits, ultimately leading to the successful execution of the mission. Full article

Bi-objective optimization seeks to obtain Pareto optimal solutions that balance two trade-off objectives, providing guidance for decision making in various fields, particularly in the field of transportation. The novelty of this study lies in two aspects. On the one hand, considering that Pareto optimal solutions are often numerous, finding the full set of Pareto optimal solutions is often computationally challenging and unnecessary for practical purposes. Therefore, we shift the focus of bi-objective optimization to finding a subset of Pareto optimal solutions whose resulting set of nondominated objective vectors is the same as, or at least a good approximation of, the full set of nondominated objective vectors for the problem. In particular, we elaborate three methods for generating a near-optimal subset of Pareto optimal solutions, including the revised $ϵ$-constraint method, the improved revised $ϵ$-constraint method, and the augmented $ϵ$-constraint method. More importantly, the near-optimality of the Pareto optimal solution subset obtained by these methods is rigorously analyzed and proved from a mathematical point of view. This study helps to offer theoretical support for future studies to find the subset of Pareto optimal solutions, which reduces the unnecessary workload and improves the efficiency of solving bi-objective optimization problems while guaranteeing a pre-specified tolerance level. Full article

Inferring a complete 3D shape from a single-view image is an ill-posed problem. The proposed methods often have problems such as insufficient feature expression, unstable training and limited constraints, resulting in a low accuracy and ambiguity reconstruction. To address these problems, we propose a prior-guided adaptive probabilistic network for single-view 3D reconstruction, called PAPRec. In the training stage, PAPRec encodes a single-view image and its corresponding 3D prior into image feature distribution and point cloud feature distribution, respectively. PAPRec then utilizes a latent normalizing flow to fit the two distributions and obtains a latent vector with rich cues. PAPRec finally introduces an adaptive probabilistic network consisting of a shape normalizing flow and a diffusion model in order to decode the latent vector as a complete 3D point cloud. Unlike the proposed methods, PAPRec fully learns the global and local features of objects by innovatively integrating 3D prior guidance and the adaptive probability network under the optimization of a loss function combining prior, flow and diffusion losses. The experimental results on the public ShapeNet dataset show that PAPRec, on average, improves CD by 2.62%, EMD by 5.99% and F1 by 4.41%, in comparison to several state-of-the-art methods. Full article

The agricultural equipment market has the characteristics of rapid demand changes and high demand for machine models, etc., so multi-variety, small-batch, and customized production methods have become the mainstream of agricultural machinery enterprises. The flexible job shop scheduling problem (FJSP) in the context of agricultural machinery and equipment manufacturing is addressed, which involves multiple resources including machines, workers, and automated guided vehicles (AGVs). The aim is to optimize two objectives: makespan and the maximum continuous working hours of all workers. To tackle this complex problem, a Multi-Objective Discrete Grey Wolf Optimization (MODGWO) algorithm is proposed. The MODGWO algorithm integrates a hybrid initialization strategy and a multi-neighborhood local search to effectively balance the exploration and exploitation capabilities. An encoding/decoding method and a method for initializing a mixed population are introduced, which includes an operation sequence vector, machine selection vector, worker selection vector, and AGV selection vector. The solution-updating mechanism is also designed to be discrete. The performance of the MODGWO algorithm is evaluated through comprehensive experiments using an extended version of the classic Brandimarte test case by randomly adding worker and AGV information. The experimental results demonstrate that MODGWO achieves better performance in identifying high-quality solutions compared to other competitive algorithms, especially for medium- and large-scale cases. The proposed algorithm contributes to the research on flexible job shop scheduling under multi-resource constraints, providing a novel solution approach that comprehensively considers both workers and AGVs. The research findings have practical implications for improving production efficiency and balancing multiple objectives in agricultural machinery and equipment manufacturing enterprises. Full article

Conventional adaptive beamformers usually suffer from serious performance degradation when the receive array is imperfect and unknown sporadic interferences appear. To enhance robustness against array imperfections and simultaneously suppress sporadic interferences, this paper studies robust adaptive beamforming (RAB) with accurate sidelobe level (SLL) control, where the imperfect array steering vector (SV) is expressed as a spherical uncertainty set. Under the maximum signal-to-interference-plus-noise ratio (SINR) criterion and robust SLL constraints, we formulate the resultant RAB into a second-order cone programming problem, which is computationally prohibitive due to numerous robust quadratic SLL constraints. To tackle this issue, we provide a subspace approximation-based method to approximate the whole sidelobe space, thus replacing all robust SLL constraints with a single subspace constraint. Moreover, we leverage the Gauss–Legendre quadrature-based scheme to generate the sidelobe space in a computationally efficient manner. Additionally, we give an explicit approach for determining the norm upper bound of SV uncertainty sets under various imperfection scenarios, addressing the challenge of obtaining this upper bound in practice.Simulation results showed that the proposed subspace approximation-based RAB beamformer had a better SINR performance than typical counterparts and was much more computationally efficient. Full article

In this paper, we consider the issue of the physical layer security (PLS) problem between two nodes, i.e., transmitter (Alice) and receiver (Bob), in the presence of an eavesdropper (Eve) in a near-field communication (NFC) system. Notably, massive multiple-input multiple-output (MIMO) arrays significantly increase array aperture, thereby rendering the eavesdroppers more inclined to lurk near the transmission end. This situation necessitates using near-field channel models to more accurately describe channel characteristics. We consider two schemes with imperfect channel estimation information (CSI). The first scheme involves a conventional multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) setup, where Alice simultaneously transmits information signal and artificial noise (AN). In the second scheme, Bob operates in a full-duplex (FD) mode, with Alice transmitting information signal while Bob emits AN. We then jointly design beamforming and AN vectors to degrade the reception signal quality at Eve, based on the signal-to-interference-plus-noise ratio (SINR) of each node. To tackle the power minimization problem, we propose an iterative algorithm that includes an additional constraint to ensure adherence to specified quality-of-service (QoS) metrics. Additionally, we decompose the robust optimization problem of the two schemes into two sub-problems, with one that can be solved using generalized Rayleigh quotient methods and the other that can be addressed through semi-definite programming (SDP). Finally, our simulation results confirm the viability of the proposed approach and demonstrate the effectiveness of the protection zone for NFC systems operating with CSI. Full article

The increased demand for electricity is inevitable due to transport sector electrification. A major part of this demand is from electric vehicle (EV) charging on a large scale, which is now a growing concern for the grid power distribution system. The lack of insight into grid energy demand by EVs makes it difficult to manage these consumptions on a large scale. For any grid load management application to be effective in minimizing the impact of uncontrolled charging, there is a need to gain insight into EV energy demand. To address this issue, this study presents data-driven modeling of EV charging sessions based on machine learning (ML) techniques. The purpose of using ML as an approach is to provide insight for estimating future energy demand and minimizing the impact of EV charging on the grid. To achieve the aim of this study, firstly, we investigated the impact of large-scale charging of EVs on the grid. Based on this, we formulated an objective function, expressed as a sum of utility functions when EVs charge on the grid with constraints imposed on voltage levels and charging power. Secondly, we employed a graphical modeling approach to study the temporal distribution of EV energy consumption based on real-world datasets from EV charging sessions. Thirdly, using ML regression models, we predicted EV energy consumption using four different models of fine tree, linear regression, linear SVM (support vector machine), and neural network. We used 5-fold cross-validation to protect against overfitting and evaluated the performances of these models using regression analysis metrics. The results from our predictions showed better accuracy when compared with the results from the work of other authors. Full article

We investigate the cooperative non-orthogonal multiple access (CNOMA) transmission through a full-duplex (FD) decode-and-forward (DaF) mode relay and propose two sub-optimal beamforming schemes for this CNOMA FD relay channel. For the optimization metric, we use the end-to-end information rate based on the mutual information from information theory. In addition to the pure CNOMA relay channel, the proposed beamforming schemes are applied to the security attack case as well, where an unauthorized eavesdropper tries to overhear the CNOMA transmission. The FD operation incurs the self-interference (SI) at the relay and the DaF mode along with CNOMA transmission forces the weakest link among the links toward three involved nodes to determine the end-to-end throughput. These facts lay the foundation for the designing and optimization of the beamforming vectors at the access point (AP) and at the relay. The first proposed sub-optimal optimization algorithm for the beamformer relies on the quadratically constrained quadratic problem (QCQP) in its central part, and this OCQP is iteratively applied with different interference level values at the near CNOMA user as the constraint term until some conditions for the design objectives are met. In addition to the first algorithm, a zero-forcing-based beamforming algorithm is proposed for a reference scheme. The proposed two algorithms are slightly modified to address the security-attacked CNOMA FD relay channel when a illegal user overhears the legitimate transmission. Simulation results are presented to advocate for the efficiency of the proposed algorithms for the CNOMA channel both with and without a security attack from an eavesdropper. Full article

The Hopfield Recurrent Neural Network (HRNN) is a single-point descent metaheuristic that uses a single potential solution to explore the search space of optimization problems, whose constraints and objective function are aggregated into a typical energy function. The initial point is usually randomly initialized, then moved by applying operators, characterizing the discrete dynamics of the HRNN, which modify its position or direction. Like all single-point metaheuristics, HRNN has certain drawbacks, such as being more likely to get stuck in local optima or miss global optima due to the use of a single point to explore the search space. Moreover, it is more sensitive to the initial point and operator, which can influence the quality and diversity of solutions. Moreover, it can have difficulty with dynamic or noisy environments, as it can lose track of the optimal region or be misled by random fluctuations. To overcome these shortcomings, this paper introduces a population-based fuzzy version of the HRNN, namely Gaussian Takagi–Sugeno Hopfield Recurrent Neural Network (G-TS-HRNN). For each neuron, the G-TS-HRNN associates an input fuzzy variable of d values, described by an appropriate Gaussian membership function that covers the universe of discourse. To build an instance of G-TS-HRNN(s) of size s, we generate s n-uplets of fuzzy values that present the premise of the Takagi–Sugeno system. The consequents are the differential equations governing the dynamics of the HRNN obtained by replacing each premise fuzzy value with the mean of different Gaussians. The steady points of all the rule premises are aggregated using the fuzzy center of gravity equation, considering the level of activity of each rule. G-TS-HRNN is used to solve the random optimization method based on the support vector model. Compared with HRNN, G-TS-HRNN performs better on well-known data sets. Full article

Actuator saturation and time delay are practical issues in practical control systems, significantly affecting their performance and stability. This paper addresses, for the first time, the stabilization problem of fractional-order (FO) nonlinear systems under these two practical constraints. Two primary methodologies are employed: the vector Lyapunov function method, integrated with the M-matrix approach, and the second one is the Lyapunov-like function method, which incorporates diffusive realization and the Lipchitz condition. An optimization framework is proposed to design stabilizing controllers based on the derived stability conditions. The proposed methods are validated numerically through their application to the FO Lorenz and Liu systems, demonstrating their effectiveness in handling actuator saturation and time delay. Full article

Magnetization vector inversion (MVI) is an effective method for simultaneously determining the distribution of magnetization intensity and direction without knowing the direction of magnetization beforehand. Nevertheless, the presence of serious non-uniqueness in MVI imposes challenges in achieving accurate and reliable results. To improve the accuracy of MVI, we propose a method that incorporates a modulus constraint, informed by an analysis of the model constraints in two different frameworks. We employ a sparse operator on the magnetization magnitude and obtain an explicit expression for the magnetization components, establishing correlation constraints among them. Synthetic test results show that this method can achieve models with clear boundaries and consistent magnetization directions. Furthermore, the application of a sparse operator to the gradient’s modulus of the magnetization magnitude helps recover inclined structures. However, the dispersed magnetization directions suggest that we should also constrain the magnetization direction, simultaneously. The inversion of magnetic data measured over the Zaohuohexi iron-polymetallic deposit in Qinghai Province, northwest China, verified the proposed approach’s effectiveness. Full article

Fuzzy matrices play a crucial role in fuzzy logic and fuzzy systems. This paper investigates the problem of supervised learning fuzzy matrices through sample pairs of input–output fuzzy vectors, where the fuzzy matrix inference mechanism is based on the max–min composition method. We propose an optimization approach based on stochastic gradient descent (SGD), which defines an objective function by using the mean squared error and incorporates constraints on the matrix elements (ensuring they take values within the interval [0, 1]). To address the non-smoothness of the max–min composition rule, a modified smoothing function for max–min is employed, ensuring stability during optimization. The experimental results demonstrate that the proposed method achieves high learning accuracy and convergence across multiple randomly generated input–output vector samples. Full article

Visible near-infrared (Vis-NIR) spectroscopy is widely used for rapid soil element detection, but calibration models are often limited by instrument-specific constraints, including varying laboratory conditions and sensor configurations. To address this, we propose a novel calibration transfer method that eliminates the conventional requirement of designating ‘master’ and ‘slave’ devices. Instead, spectral data from two spectrometers are fused to create a master spectrum, while independent spectral data serve as slave spectra. We developed an ensemble stacking model, incorporating partial least squares regression (PLSR), support vector regression (SVR), and ridge regression (Ridge) in the first layer, with BoostForest (BF) as the second layer, trained on the fused master spectrum. This model was further integrated with three calibration transfer methods: direct standardization (DS), piecewise direct standardization (PDS), and spectral space transfer (SST), to enable seamless application across slave spectra. Applied to soil total nitrogen (TN) detection, the method achieved an R²_P of 0.842, RMSEP of 0.017, and RPD of 2.544 on the first slave spectrometer, and an R²_P of 0.830, RMSEP of 0.018, and RPD of 2.452 on the second. These results demonstrate the method’s ability to simplify calibration processes while enhancing cross-instrument prediction accuracy, supporting robust and generalizable cross-instrument applications. Full article