Search Results (26)

Aiming at the inherent defects of the Horned Lizard Optimization Algorithm (HLOA), such as insufficient global exploration capability, premature convergence to local optima, and inadequate balance between exploration and exploitation, this paper proposes an enhanced Improved Horned Lizard Optimization Algorithm (IHLOA) integrated with multi-strategy improvements. Firstly, the Fuch chaotic mapping is introduced for population initialization, which enhances the ergodicity and diversity of the initial population by leveraging the pseudo-random and aperiodic characteristics of chaotic sequences, laying a high-quality foundation for subsequent optimization searches. Secondly, the golden sine strategy is embedded into the iterative update process to dynamically adjust the search step size and direction. This strategy utilizes the periodic amplitude variation in the sine function and the golden section coefficient to balance the global exploration for potential optimal regions and local exploitation for refined optimization, thereby accelerating convergence speed while avoiding local stagnation. Finally, the orthogonal crossover strategy is incorporated in the late iteration stage to promote effective information interaction between parent and offspring populations. By means of chromosome segment exchange and elitist retention mechanisms, this strategy reduces dimensional search blind spots and further enhances the algorithm’s ability to capture high-quality solutions. Comprehensive experimental evaluations are conducted based on classical benchmark test functions and eight state-of-the-art meta-heuristic algorithms. The results demonstrate that the IHLOA outperforms comparative algorithms in terms of optimization accuracy, convergence speed, and stability across 30-D, 50-D, and 80-D scenarios. For practical path planning applications, the IHLOA achieves remarkable performance improvements: in single-goal path planning, it reduces the path length by 2.54–87.64% compared with benchmark algorithms; in multi-goal path planning, it realizes a 1.24–7.99% reduction in path length and an 11.91% average reduction in the number of turning points relative to the original HLOA. Additionally, the IHLOA exhibits excellent robustness and adaptability in dynamic obstacle environments, effectively shortening the path length and reducing robot stuck times. This research not only enriches the improvement framework of meta-heuristic algorithms but also provides a high-efficiency optimization solution for mobile robot path planning in complex environments. Full article

The Bering Sea and its surrounding waters are commercially and ecologically important ecosystems. Knowledge of phytoplankton phenology is crucial for understanding ecosystem dynamics. However, estimates of phenological parameters of spring phytoplankton bloom are sparse for this region. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) daily data from 2003–2024 to assess the climatology of phenological parameters. A combination of data regriding, spatial interpolation, and temporal smoothing was applied. Three methods of spatial interpolation for missing data acquisition are compared: iterative first-order neighbor, inverse distance weighted interpolation, and data interpolating empirical orthogonal functions (DINEOF). We suggest that the first outcompetes the other two methods when compared to initial data. Date of the bloom initiation, bloom peak, chlorophyll-a maximum, and duration of the bloom before its peak are evaluated. The spatial distribution of mentioned phenological parameters is presented and discussed. We show that bloom starts early in Bristol Bay, in the narrow band along the eastern shelf, along the Kamchatka Peninsula, and south of the Aleutians and Alaska Peninsula. In the deep Bering Sea, bloom starts surprisingly later considering the latitude of the region. The main reason for this may be the wind mixing during the spring. The first phase of the bloom is generally longer in the deep southern areas (up to 60 days) and shorter in the northern shelf areas (less than 2 weeks in some cases). Full article

In this paper, a wireless network architecture is considered that combines double intelligent reflecting surfaces (IRSs), energy harvesting (EH), and non-orthogonal multiple access (NOMA) with cooperative relaying (C-NOMA) to leverage the performance of non-line-of-sight (NLoS) communication mainly and incorporate energy efficiency in next-generation networks. To optimize the phase shifts of both IRSs, we employ a machine learning model that offers a low-complexity alternative to traditional optimization methods. This lightweight learning-based approach is introduced to predict effective IRS phase shift configurations without relying on solver-generated labels or repeated iterations. The model learns from channel behavior and system observations, which allows it to react rapidly under dynamic channel conditions. Numerical analysis demonstrates the validity of the proposed architecture in providing considerable improvements in spectral efficiency and service reliability through the integration of energy harvesting and relay-based communication compared with conventional systems, thereby facilitating green communication systems. Full article

This paper relies on orthogonal metric spaces related to cyclic self-mappings and some of their relevant properties. The involved binary relation is not symmetric, and then the term pseudo-orthogonality will be used for the relation used in the article to address the established results on cyclic self-mappings. Firstly, some orthogonal binary relations are given through examples to fix some ideas to be followed in the main body of the article. It is seen that the orthogonal elements of the orthogonal sets are not necessarily singletons. Secondly, “ad hoc” specific orthogonality binary relations are also described through examples related to the investigation of stability and controllability problems in dynamic systems. The main objective of this paper is to investigate the properties of cyclic single-valued self-mappings on the union of any finite number $p\ge 2$ of nonempty closed subsets of a metric space in a cyclic disposal under an “ad hoc” defined pseudo-orthogonality condition. Such a condition is defined on certain subsequences, referred to as pseudo-orthogonal sequences, rather than on the whole generated sequences under the self-mapping. It basically consists of a cyclic, in general iteration-dependent, contractive condition just for such subsequences which, on the other hand, are not forced as a constraint to be fulfilled by the whole sequences. Furthermore, the whole sequences in which those sequences are contained are allowed to be locally non-contractive or even locally expansive. The boundedness and the convergence properties of distances between pseudo-orthogonal subsequences and sequences are investigated under the condition that one of the subsets has a unique best proximity point to its adjacent subset in the cyclic disposal to which the pseudo-orthogonal subsequences converge. The pseudo-orthogonal metric subspace of the given metric space is proved to be complete although the whole metric space is not assumed to be complete. The pseudo-orthogonal element is seen to be a set of best proximity points, one per subset of the cyclic disposal, although it is not required for all the best proximity sets to be singletons. It is proved that the pseudo-orthogonal subsequences converge to a limit cycle, consisting of a best proximity point per subset of the cyclic disposal, which is also the pseudo-orthogonal element. The whole sequences are also proved to be bounded and the distances between their elements in adjacent subsets are also proved to converge to the distance between adjacent subsets. In the event that the metric space is a uniformly convex Banach space, it suffices that one of the subsets of the cyclic disposal be boundedly compact with its best proximity set being a singleton. In this case, the pseudo-orthogonal sequences converge to their best proximity set to their adjacent subset provided that such a best proximity set is a singleton. Full article

Owing to the intrinsic sparsity of GNSS signals in the correlation domain, compressed sensing (CS) is attractive for the rapid acquisition of high-dynamic GNSS signals. However, the compressed measurement-associated noise folding inherently amplifies the pre-measurement noise, leading to an inevitable degradation of acquisition performance. In this paper, a novel CS-based GNSS signal acquisition algorithm is, for the first time, proposed with the efficient suppression of the amplified measurement noise and low computational complexities. The offline developed code phase and frequency bin-compressed matrices in the correlation domain are utilized to obtain a real-time observed matrix, from which the correlation matrix of the GNSS signal is rapidly reconstructed via a denoised back-projection and a non-iterative shrinkage-thresholding (NIST) operation. A detailed theoretical analysis and extensive numerical explorations are undertaken for the algorithm computational complexity, the achievable acquisition performance, and the algorithm performance robustness to various Doppler frequencies. It is shown that, compared with the classic orthogonal matching pursuit (OMP) reconstruction, the NIST reconstruction gives rise to a 3.3 dB improvement in detection sensitivity with a computational complexity increase of <10%. Moreover, the NIST-reconstructed CS acquisition algorithm outperforms the conventional CS acquisition algorithm with frequency serial search (FSS) in terms of both the acquisition performance and the computational complexity. In addition, a variation in the detection sensitivity is observed as low as 1.3 dB over a Doppler frequency range from 100 kHz to 200 kHz. Full article

The study of the mechanical properties of moderately consolidated sandstones is crucial for engineering safety assessments. As an effective research tool, the discrete element method (DEM) encounters challenges during the modeling phase, such as a large number of micromechanical parameters, low modeling efficiency, and unclear coupling mechanisms among multiple parameters. To address these issues, this paper proposes a calibration method for the micromechanical parameters of DEM models for moderately consolidated sandstones. By integrating orthogonal experimental design with a multivariate analysis of variance, the influence of micromechanical parameters on macroscopic mechanical properties is quantified, and a parameter prediction model is constructed using an intelligent dynamic regression selection mechanism, significantly improving the efficiency and accuracy of micromechanical parameter calibration. The results show that the macroscopic elastic modulus E is primarily controlled by the effective modulus ($\overline{E}$), stiffness ratio (k), and particle size ratio (R_max/R_min), following a linear relationship. The influence of the particle size ratio decreases significantly once it exceeds a threshold value. The macroscopic uniaxial compressive strength (UCS) is dominated by cohesion ($\overline{c}$) and tensile strength (${\overline{\mathrm{\sigma }}}_c$), exhibiting a polynomial relationship, where a stronger synergistic effect is generated when both parameters are at higher levels. Poisson’s ratio ($\mu$) is significantly correlated only with the stiffness ratio (k), following a logarithmic relationship. An iterative correction method for micromechanical parameter calibration is proposed. The errors between the three groups of simulation results and laboratory test results are all less than 10%, and the crack distribution patterns show a high degree of consistency. The findings of this study provide a theoretical foundation and technical means for exploring the mechanical behavior and damage mechanism of moderately consolidated sandstones. Full article

A new text matching model based on dynamic gated sparse attention feature distillation with a faithful semantic preservation strategy is proposed to address the fact that text matching models are susceptible to interference from weakly relevant information and that they find it difficult to obtain key features that are faithful to the original semantics, resulting in a decrease in accuracy. Compared to the traditional attention mechanism, with its high computational complexity and difficulty in discarding weakly relevant features, this study designs a new dynamic gated sparse attention feature distillation method based on dynamic gated sparse attention, aiming to obtain key features. Weakly relevant features are obtained through the synergy of dynamic gated sparse attention, a gradient inversion layer, a SoftMax function, and projection theorem literacy. Among these, sparse attention enhances weakly correlated feature capture through multimodal dynamic fusion with adaptive compression. Then, the projection theorem is used to identify and discard the noisy features in the hidden layer information to obtain the key features. This feature distillation strategy, in which the semantic information of the original text is decomposed into key features and noise features, forms an orthogonal decomposition symmetry in the semantic space. A new variety of faithful semantic preservation strategies is designed to make the key features faithful to the original semantic information. This strategy introduces an interval loss function and calculates the angle between the key features and the original hidden layer information with the help of cosine similarity in order to ensure that the features reflect the semantics of the original text. This can further update the iterative key features and thus improve the accuracy. The strategy builds a feature fidelity verification mechanism with a symmetric core of bidirectional considerations of semantic accuracy and correspondence to the original text. The experimental results show that the accuracies are 89.10% and 95.01% in the English datasets MRPC and Scitail, respectively; 87.8% in the Chinese dataset PAWX; and 80.32% and 80.27% in the Ant Gold dataset, respectively. Meanwhile, the accuracies in the KUAKE-QTR dataset and Macro-F1 are 70.10% and 68.08%, respectively, which are better than other methods. Full article

This paper proposes a source encoding rate control and cross-layer data stream allocation scheme for uplink millimeter-wave (mmWave) multi-user massive MIMO (MU-mMIMO) orthogonal frequency division multiplexing (OFDM) hybrid beamforming video communication systems. Unlike most previous studies that focus on the downlink scenario, our proposed scheme optimizes the uplink transmission while also addressing the limitation of prior works that only consider single-data-stream users. A key distinction of our approach is the integration of cross-layer resource allocation, which jointly considers both the physical layer channel state information (CSI) and the application layer video rate-distortion (RD) function. While traditional methods optimize for spectral efficiency (SE), our proposed method directly maximizes the peak signal-to-noise ratio (PSNR) to enhance video quality, aligning with the growing demand for high-quality video communication. We introduce a novel iterative cross-layer dynamic data stream allocation scheme, where the initial allocation is based on conventional physical-layer data stream allocation, followed by iterative refinement. Through multiple iterations, users with lower PSNR can dynamically contend for data streams, leading to a more balanced and optimized resource allocation. Our approach is a general framework that can incorporate any existing physical-layer data stream allocation as an initialization step before iteration. Simulation results demonstrate that the proposed cross-layer scheme outperforms three conventional physical-layer schemes by 0.4 to 1.14 dB in PSNR for 4–6 users, at the cost of a 1.8 to 2.3× increase in computational complexity (requiring 3.6–5.8 iterations). Full article

Precision and non-damaging harvesting is a key direction for the development of mechanized fruit harvesting technologies. Blueberries, with their soft texture and delicate skin, present significant challenges for achieving precise and non-damaging mechanical harvesting. This paper proposes an intelligent recognition and prediction method based on machine vision. The method uses image recognition technology to extract the physical characteristics of blueberries, such as diameter and thickness, and estimates fruit hardness in real-time through a predictive model. The gripping force of the mechanical claw is dynamically adjusted to ensure non-destructive harvesting. Firstly, a chimpanzee optimization algorithm (ChOA) was used to optimize a prediction model that established a mapping relationship between fruit diameter, thickness, weight, and fruit hardness. The radial basis network optimized by the chimpanzee optimization algorithm (ChOA-RBF) model was compared with a non-optimized model, and the results showed that the ChOA-RBF prediction model has significant advantages in predicting fruit hardness. Next, an orthogonal experiment further verified the model, showing that the prediction error between the model’s values and actual values was less than 5%. Additionally, considering practical applications, a simple and efficient two-parameter method was proposed, removing the weight parameter and predicting fruit hardness using only diameter and thickness. Although the two-parameter method increases the prediction error by 0.36% compared to the three-parameter method, it reduces the number of convergence steps by 71 and shortens the computation time by one-third, significantly improving iteration speed. Finally, further crushing experiments showed that using the two-parameter method for hardness prediction through parameter extraction via visual recognition resulted in a relative error of less than 8%, with an average relative error of 3.91%. The error falls within the acceptable range for the safety factor design. This method provides a novel solution for the non-damaging mechanized harvesting of soft fruits. Full article

In the control-based approach of medical treatment of various illnesses such as diabetes mellitus, certain angiogenic cancers, or in anesthesia, the starting point used to be some “patient model” on the basis of which the appropriate administration of the drugs can be designed. The identification of the “patient model’s parameters” is always a hard and sometimes unsolvable mathematical task. Furthermore, these parameters have wide variability between patients. In principle, either robust or adaptive techniques can be used to tackle the problem of modeling imprecisions. In this paper, the potential application of a variant of Fixed Point Iteration-Based Adaptive Controllers was investigated in model-based control. The main point was the introduction of a “parameter estimation error significance metric” through the use of which the individual model parameter estimation can be avoided, and even the consequences of the deficiencies of the approximate model as a whole can be estimated. The adaptive controller forces the system to track the prescribed nominal trajectory; therefore, it brings about the “actual control situation” in which the consequences of the estimation errors are of interest. One component of the adaptive control is a “rotational block” that creates a multidimensional orthogonal (rotation) matrix that rotates arrays of identical Frobenius norms into each other. Since in a recent publication under review it was proved that the angle of the necessary rotation satisfies the mathematical criteria of metrics in a metric space, even in quite complicated nonlinear and multidimensional cases, this simple value can serve as a metric for this purpose. To exemplify the method, an under-actuated nonlinear system of 2 degree of freedom and relative order 4 was controlled by a special adaptive backstepping controller that was designed on a purely kinematic basis. From this point of view, it has a strong relationship with the PID controllers. This simple model was rich enough to exemplify parameters that require precise identification because their error produces quite significant consequences, and other parameters that do not require very precise identification. It was found that the method provided the dynamic models with reliable parameter sensitivity estimation metrics. Full article

Unmanned Aerial Vehicles (UAVs) provide exceptional flexibility, making them ideal for mitigating communication disruptions in disaster-affected or high-demand areas. When functioning as communication base stations, UAVs can adopt either orthogonal or non-orthogonal multiple access schemes. However, traditional Orthogonal Multiple Access (OMA) techniques are constrained by limited user access capacity and system throughput, necessitating the study of non-orthogonal access mechanisms for UAV-assisted communication systems. While much of the research on non-orthogonal multiple access focuses on Non-Orthogonal Multiple Access (NOMA), Rate-Splitting Multiple Access (RSMA), a novel non-orthogonal technique, offers superior throughput performance compared to NOMA. This paper, therefore, investigates the optimization of UAV-assisted downlink communication systems based on RSMA. We first develop a mathematical model of the system and decompose the primary optimization problem into multiple subproblems according to parameter types. To solve these subproblems, we propose an optimization algorithm that combines the Augmented Lagrange Method (ALM) with the Artificial Fish Swarm Algorithm (AFSA). The optimization algorithm is further enhanced by incorporating dynamic step size and visual strategies, as well as memory behaviors to improve convergence speed and optimization accuracy. To address linear equality constraints, we introduce a correction factor to modify the behavior of the artificial fish. The final optimization is achieved through cross-iterative solutions. Simulation results show that the system throughput under the RSMA strategy can be improved by 13.30% compared with NOMA, validating the effectiveness and superiority of RSMA in UAV-assisted communication systems. Full article

Non-orthogonal multiple access (NOMA) provides higher spectral efficiency and access to more users than orthogonal multiple access. However, the issue of resource allocation in NOMA is dynamic and produces a high computation burden when using traditional methods. In this paper, a symmetry-aware double deep Q network (DDQN) algorithm in deep reinforcement learning is employed to allocate power to users in NOMA while guaranteeing quality of service for the weakest users. The research process is divided into two parts. Firstly, users in the communication system are grouped using a method that synergistically considers gain difference and similarity, exploiting symmetrical properties within the user groups. Secondly, the DDQN algorithm is used to allocate power to multiple users in a NOMA system, which utilizes the inherent symmetry in the signal-to-interference noise ratio of each user as an objective function. By recognizing and leveraging these symmetrical patterns, the algorithm can dynamically adjust the power allocation to optimize system performance. Finally, the proposed algorithm is compared with conventional NOMA power allocation algorithms and shows significant improvements in system performance. The results of the convergence function show that the algorithm proposed in this paper can converge in approximately 1800 iterations, which effectively solves the problem of large arithmetic and complex processes existing in the traditional method. Full article

The estimation of space-varying geological parameters is often not computationally affordable for high-dimensional subsurface reservoir modeling systems. The adjoint method is generally regarded as an efficient approach for obtaining analytical gradient and, thus, proceeding with the gradient-based iteration algorithm; however, the infeasible memory requirement and computational demands strictly prohibit its generic implementation, especially for high-dimensional problems. The autoregressive neural network (aNN) model, as a nonlinear surrogate approximation, has gradually received increasing popularity due to significant reduction of computational cost, but one prominent limitation is that the generic application of aNN to large-scale reservoir models inevitably poses challenges in the training procedure, which remains unresolved. To address this issue, model-order reduction could be a promising strategy, which enables us to train the neural network in a very efficient manner. A very popular projection-based linear reduction method, i.e., propel orthogonal decomposition (POD), is adopted to achieve dimensionality reduction. This paper presents an architecture of a projection-based autoregressive neural network that efficiently derives an easy-to-use adjoint model by the use of an auto-differentiation module inside the popular deep learning frameworks. This hybrid neural network proxy, referred to as POD-aNN, is capable of speeding up derivation of reduced-order adjoint models. The performance of POD-aNN is validated through a synthetic 2D subsurface transport model. The use of POD-aNN significantly reduces the computation cost while the accuracy remains. In addition, our proposed POD-aNN can easily obtain multiple posterior realizations for uncertainty evaluation. The developed POD-aNN emulator is a data-driven approach for reduced-order modeling of nonlinear dynamic systems and, thus, should be a very efficient modeling tool to address many engineering applications related to intensive simulation-based optimization. Full article

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for “GWO embedding IS”), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs’ positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér–Rao lower bound (CRLB), even at low SNR levels. Full article

In fluid mechanics, modal decomposition, deeply intertwined with the concept of symmetry, is an essential data analysis method. It facilitates the segmentation of parameters such as flow, velocity, and pressure fields into distinct modes, each exhibiting symmetrical or asymmetrical characteristics in terms of amplitudes, frequencies, and phases. This technique, emphasizing the role of symmetry, is pivotal in both theoretical research and practical engineering applications. This paper delves into two dominant modal decomposition methods, infused with symmetry considerations: Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD). POD excels in dissecting flow fields with clear periodic structures, often showcasing symmetrical patterns. It utilizes basis functions and time coefficients to delineate spatial modes and their evolution, highlighting symmetrical or asymmetrical transitions. In contrast, DMD effectively analyzes more complex, often asymmetrical structures like turbulent flows. By performing iterative analyses on the flow field, DMD discerns symmetrical or asymmetrical statistical structures, assembling modal functions and coefficients for decomposition. This method is adapted to extracting symmetrical patterns in vibration frequencies, growth rates, and intermodal coupling. The integration of modal decomposition with symmetry concepts in fluid mechanics enables the effective extraction of fluid flow features, such as symmetrically or asymmetrically arranged vortex configurations and trace evolutions. It enhances the post-processing analysis of numerical simulations and machine learning approaches in flow field simulations. In engineering, understanding the symmetrical aspects of complex flow dynamics is crucial. The dynamics assist in flow control, noise suppression, and optimization measures, thus improving the symmetry in system efficiency and energy consumption. Overall, modal decomposition methods, especially POD and DMD, provide significant insights into the symmetrical and asymmetrical analysis of fluid flow. These techniques underpin the study of fluid mechanics, offering crucial tools for fluid flow control, optimization, and the investigation of nonlinear phenomena and propagation modes in fluid dynamics, all through the lens of symmetry. Full article