Search Results (14,017)

The temperature and emissivity separation (TES) algorithm is currently adopted to retrieve the land surface temperature (LST) and emissivity (LSE) from Moderate Resolution Imaging Spectroradiometer (MODIS) images (i.e., the MOD/MYD21 product). Unfortunately, the TES algorithm often yields anomalous LSE spectra for graybodies with low spectral contrast. The MODIS TES algorithm does not effectively address this issue. To overcome this limitation, refined TES algorithms, including the optimized smoothing for temperature emissivity separation (OSTES) and the temperature and emissivity separation with nonlinear constraint (TESNC), have been proposed. Although these algorithms offer theoretical improvements, their performance has not been systematically validated using real MODIS data. This study evaluates the performance of three TES algorithms (MODIS TES, OSTES, and TESNC) in retrieving LST&E from MODIS data over six lakes on the Qinghai–Tibet Plateau, which serve as representative examples of low-spectral-contrast surfaces. Three years (2018–2020) of MODIS data from six lakes on the Qinghai–Tibet Plateau were collected to retrieve LST&E using three TES algorithms. Using the quality-controlled MODIS LST product (MOD11) as a benchmark, the TESNC algorithm achieved the highest accuracy, with bias and RMSE values of 0.18 K and 0.22 K, respectively, compared with the bias and RMSE values of 0.51 K and 0.53 K for the MODIS TES algorithm and 0.58 K and 0.60 K for the OSTES algorithm, respectively. In terms of LSE, the TESNC algorithm achieved an RMSE within 0.005 for all bands, demonstrating superior accuracy over the other algorithms. Overall, the TESNC algorithm significantly improved the accuracy of LST&E retrieval from MODIS for graybodies with low spectral contrast. This study is the first to systematically evaluate refined TES algorithms using real MODIS data over graybodies. The findings provide valuable insights for improving the MODIS LST&E product and advancing the retrieval of LST&E for low-spectral-contrast surfaces. Full article

Unmanned aerial vehicles (UAVs) have shown significant advantages in disaster relief, emergency communication, and Integrated Sensing and Communication (ISAC). However, the escalating demand for UAV spectrum is severely restricted by the scarcity of available spectrum, which in turn significantly limits communication performance. Additionally, the openness of the wireless channel poses a serious threat, such as wiretapping and jamming. Therefore, it is necessary to improve the security performance of the system. Recently, Reconfigurable Intelligent Surfaces (RIS), as a highly promising technology, has been integrated into Cognitive UAV Network. This integration enhances the legitimate signal while suppressing the eavesdropping signal. This paper investigates a RIS-assisted Cognitive UAV Network with multiple corresponding receiving users as cognitive users (CUs) in the presence of malicious eavesdroppers (Eav), in which the Cognitive UAV functions as the mobile aerial Base Station (BS) to transmit confidential messages for the users on the ground. Our primary aim is to attain the maximum secrecy bits by means of jointly optimizing the transmit power, access scheme of the CUs, the RIS phase shift matrix, and the trajectory. In light of the fact that the access scheme is an integer, the original problem proves to be a mixed integer non-convex one, which falls into the NP-hard category. To solve this problem, we propose block coordinate descent and successive convex approximation (BCD-SCA) algorithms. Firstly, we introduce the BCD algorithm to decouple the coupled variables and convert the original problem into four sub-problems for the non-convex subproblems to solve by the SCA algorithm. The results of our simulations indicate that the joint optimization scheme we have put forward not only achieves robust convergence but also outperforms conventional benchmark approaches. Full article

In the context of the rapid development of the global economy, promoting corporate economic development while taking into account sustainable development has gradually become the focus of attention of countries around the world. The ESG performance reflects the differences in the assessment of enterprises’ sustainable development potential by capital market information intermediaries. These differences affect the internal governance and external financing of enterprises, thereby influencing corporate green innovation. This research is based on 1500 Shanghai-Shenzhen A-share listed companies in China from 2012 to 2022. Using green technology innovation quantity (GINUM) and green technology innovation quality (GICIT) as the measures of corporate green innovation capabilities, and by constructing a DiD model and a benchmark regression model, the dynamic relationship between ESG performance and green innovation is explored. At the same time, the mediation effect model is introduced to examine the impact of ESG performance on corporate green innovation capabilities from three perspectives: financing constraints, management’s green development awareness, and employee innovation efficiency. In addition, endogenous analysis methods and robustness test methods are employed to further ensure the reliability of the research results. The research findings show that ESG performance can significantly promote corporate green innovation capabilities. Heterogeneity analysis reveals that ESG performance significantly enhances the green technology innovation capabilities of enterprises, especially among non-state-owned small and medium-sized enterprises (SMEs) and enterprises in the eastern region. The regression coefficients for GINUM and GICIT are 0.019, 0.021, 0.084, and 0.086, respectively, all of which are statistically significant at the 1% level. The mechanism analysis shows that in terms of alleviating financing constraints, enhancing management’s green development awareness, and improving employee innovation efficiency, the regression coefficients of ESG performance for GINUM and GICIT are −1.559, −1.953, 0.018, 0.011, 0.427, and 0.495, respectively, indicating a certain promoting effect. The results of this study enrich and expand the relevant research on the relationship between ESG and corporate green innovation capabilities to a certain extent. This research is expected to provide some new practical directions for promoting green innovation capabilities within the ESG framework. Full article

This work investigates the bi-objective integrated scheduling of job shop problems and material handling robots with setup time. The objective is to minimize the maximum completion time and the mean of earliness and tardiness simultaneously. First, a mathematical model is established to describe the problems. Then, different meta-heuristics and their variants are developed to solve the problems, including genetic algorithms, particle swarm optimization, and artificial bee colonies. To improve the performance of algorithms, seven local search operators are proposed. Moreover, two reinforcement learning algorithms, Q-learning and SARSA, are designed to help the algorithm select appropriate local search operators during iterations, further improving the convergence of algorithms. Finally, based on 82 benchmark cases with different scales, the effectiveness of the suggested algorithms is evaluated by comprehensive numerical experiments. The experimental results and discussions show that the genetic algorithm with SARSA is more competitive than its peers. Full article

Virtual Machine Introspection (VMI) is a powerful technology used to detect and analyze malicious software inside Virtual Machine (VMs) from outside. Asynchronously accessing the VM’s memory can be insufficient for efficiently monitoring what is happening inside of a VM. Active VMI introduces breakpoints to intercept VM execution at relevant points. Especially for frequently visited breakpoints, and even more so for production systems, it is crucial to keep their performance overhead as low as possible. In this paper, we provide a systematization of existing VMI breakpoint implementation variants, propose workloads to quantify the different performance penalties of breakpoints, and implement them in the benchmarking application bpbench. We used this benchmark to measure that, on an Intel Core i5 7300U, SmartVMI’s breakpoints take around 81 µs to handle, and keeping the breakpoint invisible costs an additional 21 µs per read access. The availability of bpbench facilitates the comparison of disparate breakpoint mechanisms and their performance optimization with immediate feedback. Full article

Borehole trajectory optimization is a key issue in oil and gas drilling engineering. The traditional wellbore trajectory design method faces great challenges in optimizing the trajectory length and complexity, and it is difficult to meet the actual engineering requirements. In this paper, the three-stage wellbore trajectory optimization problem is studied, and a multi-objective optimization model including two objective functions of trajectory length and trajectory complexity is constructed. In this paper, an improved multi-objective particle swarm optimization algorithm is proposed, which combines the clustering strategy to improve the diversity of solutions, and enhances the local search ability and global convergence performance of the algorithm through the elite learning strategy. In order to verify the performance of the algorithm, comparative experiments were carried out using classical multi-objective benchmark functions. The results showed that the improved algorithm is superior to the traditional method in terms of diversity and convergence of solutions. Finally, the proposed algorithm was applied to the actual three-stage wellbore trajectory optimization problem. In summary, the research results of this paper provide theoretical support and engineering practice methods for wellbore trajectory optimization, and serve as an important reference for further improving the efficiency and quality of wellbore trajectory design. Full article

Reliable inflation forecasts are essential for both business operations and macroeconomic policy making. This study explores the potential of using machine learning (ML) techniques to improve the accuracy of human forecasts of inflation. Specifically, we develop and examine ML-centered forecast adjustment procedures where advanced ML techniques are employed to predict and thus mitigate the errors of human forecasts, akin to how an AI-powered spell and grammar checker helps to prevent mistakes in human writing. Our empirical exercises demonstrate the benefits of several popular ML techniques, such as the elastic net, LASSO, and ridge regressions, and provide evidence of their ability to improve both our own benchmark inflation forecasts and those reported by the frequent participants in the US Survey of Professional Forecasters. The forecast adjustment procedures proposed in this paper are conceptually appealing, widely applicable, and empirically effective in reducing forecast bias and improving forecast accuracy. Full article

Predicting seafood consumption behavior is essential for fishing companies to adjust their production plans and marketing strategies. To achieve accurate predictions, this paper introduces a model for forecasting seafood consumption behavior based on an interpretable machine learning algorithm. Additionally, the Shapley Additive exPlanation (SHAP) model and the Accumulated Local Effects (ALE) plot were integrated to provide a detailed analysis of the factors influencing Shanghai residents’ intentions to purchase salmon. In this study, we constructed nine regression prediction models, including ANN, Decision Tree, GBDT, Random Forest, AdaBoost, XGBoost, LightGBM, CatBoost, and NGBoost, to predict the consumers’ intentions to purchase salmon and to compare their predictive performance. In addition, Bayesian optimization algorithm is used to optimize the hyperparameters of the optimal regression prediction model to improve the model prediction accuracy. Finally, the SHAP model was used to analyze the key factors and interactions affecting the consumers’ willingness to purchase salmon, and the Accumulated Local Effects plot was used to show the specific prediction patterns of different influences on salmon consumption. The results of the study show that salmon farming safety and ease of cooking have significant nonlinear effects on salmon consumption; the BO-CatBoost nonlinear regression prediction model demonstrates superior performance compared to the benchmark model, with the test set exhibiting RMSE, MSE, MAE, R² and TIC values of 0.155, 0.024, 0.097, 0.902, and 0.313, respectively. This study can provide technical support for suppliers in the salmon value chain and help their decision-making to adjust their corporate production plan and marketing activities Full article

Although there do exist a few aeroacoustic studies on harmful artificial phenomena related to the usage of non-uniform Cartesian grids in lattice Boltzmann methods (LBM), a thorough quantitative comparison between different categories of grid arrangement is still missing in the literature. In this paper, several established schemes for hierarchical grid refinement in lattice Boltzmann simulations are analyzed with respect to spurious aeroacoustic emissions using a weakly compressible model based on a D3Q19 athermal velocity set. In order to distinguish between various sources of spurious phenomena, we deploy both the classical Bhatnagar–Gross–Krook and other more recent collision models like the hybrid recursive-regularization operator, the latter of which is able to filter out detrimental non-hydrodynamic mode contributions, inherently present in the LBM dynamics. We show by means of various benchmark simulations that a cell-centered approach, either with a linear or uniform explosion procedure, as well as a vertex-centered direct-coupling method, proves to be the most suitable with regards to aeroacoustics, as they produce the least amount of spurious noise. Furthermore, it is demonstrated how simple modifications in the selection of distribution functions to be reconstructed during the communication step between fine and coarse grids affect spurious aeroacoustic artifacts in vertex-centered schemes and can thus be leveraged to positively influence stability and accuracy. Full article

Metaheuristic algorithms are favored for solving a variety of problems due to their inherent simplicity, ease of implementation, and effective problem-solving capabilities. This study proposes four new hybrid approaches using swarm-based metaheuristic algorithms. Two of these new approaches are HHHOWOA1 and HHHOWOA2, based on the hybridization of Harris Hawks Optimization (HHO) with the Whale Optimization Algorithm (WOA), and the others are HHHOWOA1PSO and HHHOWOA2PSO, based on the hybridization of HHHOWOA1 and HHHOWOA2 with particle swarm optimization (PSO). An evaluation of these four innovative approaches is conducted on 23 benchmark functions, and their results are compared to those reported in the literature under equivalent parameter settings. Among the four approaches, HHHOWOA1 and HHHOWOA2PSO have demonstrated more favorable results. According to the literature, the HHHOWOA1 and HHHOWOA2PSO approaches achieve the most optimal results, either better or with the same average fitness values in 15 of the 23 functions and in 18 of the 23 functions, respectively. Moreover, the proposed approaches have been applied to three engineering problems, and the optimum values obtained are compared to the literature. Ultimately, the proposed approaches have proven effective in providing competitive solutions for the majority of optimization problems. Full article

Uniform design is a powerful and robust experimental methodology that is particularly advantageous for multidimensional numerical integration and high-level experiments. As its applications expand across diverse disciplines, the theoretical foundation of uniform design continues to evolve. In real-world scenarios, experimental factors are often subject to one or more linear constraints, which pose challenges in constructing efficient designs within constrained high-dimensional experimental spaces. These challenges typically require sophisticated algorithms, which may compromise uniformity and robustness. Addressing these constraints is critical for reducing costs, improving model accuracy, and identifying global optima in optimization problems. However, existing research primarily focuses on unconstrained or minimally constrained hypercubes, leaving a gap in constructing designs tailored to arbitrary linear constraints. This study bridges this gap by extending the inverse Rosenblatt transformation framework to develop innovative methods for constructing uniform designs over arbitrary hyperplanes and hyperspheres within unit hypercubes. Explicit construction formulas for these constrained domains are derived, offering simplified calculations for practitioners and providing a practical solution applicable to a wide range of experimental scenarios. Numerical simulations demonstrate the feasibility and effectiveness of these methods, setting a new benchmark for uniform design in constrained experimental regions. Full article

This study introduces an innovative, non-contact method for classifying the hardness of austenitic stainless steels (grade AISI 304) based on their intrinsic magnetic fields. Utilizing a 3 × 3 matrix sensor system, this research captures weak magnetic fields to produce precise 2D magnetic field maps of the samples. A key advancement is the application of a modified GoogleNet convolutional neural network, optimized with the stochastic gradient descent with momentum algorithm, which achieves exceptional classification accuracy, ranging from 95% to 100%, and median accuracies of 97.5% to 99%. This method stands out by revealing a novel correlation between annealing temperature and magnetic field strength, particularly a pronounced decline in magnetic properties at temperatures near 1000 °C. This observation underscores the sensitivity of magnetic profiles to heat treatments, offering a groundbreaking approach to material characterization. By enabling reliable, efficient, and fully automated hardness evaluation based on magnetic signatures, this work has the potential to transform materials engineering and manufacturing, setting a new benchmark for non-destructive material analysis techniques. Full article

The challenge in reusing high-impact recorders lies in developing an efficient and accurate failure prediction model under small-sample conditions. To address this issue, this study proposes an IPSO-SVM model. First, the particle swarms in the IPSO algorithm were grouped based on their exploration and exploitation functions, and dynamic inertia weight mechanisms were designed accordingly. The grouping ratio was dynamically adjusted during iterations to enhance optimization performance. Tests using benchmark functions verified that this approach improves convergence accuracy and stability compared to conventional PSO algorithms. Subsequently, the 5-fold cross-validation accuracy of the SVM model was used as the fitness value, and the IPSO algorithm was employed to optimize the penalty and kernel parameters of the SVM model. Trained on high-impact experimental data, the IPSO-SVM model achieved a prediction accuracy of 90.5%, outperforming the PSO-SVM model’s 85%. These results demonstrate the potential of the IPSO-SVM model in addressing failure prediction challenges under small-sample conditions. Full article

Cloth-changing Person Re-Identification (CC-ReID) poses a significant challenge in tracking pedestrians across cameras while accounting for changes in clothing appearance. Despite recent progress in CC-ReID, existing methods predominantly focus on learning the unique biological features of pedestrians, often overlooking constraints that promote the learning of cloth-agnostic features. Addressing this limitation, we propose a Robust Fine-grained Learning Network (RFLNet) to effectively learn robust cloth-agnostic features by leveraging fine-grained semantic constraints. Specifically, we introduce a four-body-part attention module to enhance the learning of detailed pedestrian semantic features. To further strengthen the model’s robustness to clothing variations, we employ a random erasing algorithm, encouraging the network to concentrate on cloth-irrelevant attributes. Additionally, we design a fine-grained semantic loss to guide the model in learning identity-related, detailed semantic features, thereby improving its focus on cloth-agnostic regions. Comprehensive experiments on widely used CC-ReID benchmarks demonstrate the effectiveness of RFLNet. Our method achieves state-of-the-art performance, including a 0.7% increase in mAP on PRCC and a 1.6% improvement in rank-1 accuracy on DeepChange. Full article

To address the issues of insufficient samples, limited scene diversity, missing perspectives, and low resolution in existing UAV-based pedestrian detection datasets, this paper proposes a novel UAV-based pedestrian detection benchmark dataset named the Novel Surveillance View (NSV). This dataset encompasses diverse scenes and pedestrian information captured from multiple perspectives, and introduces an innovative data mining approach that leverages tracking and optical flow information. This approach significantly improves data acquisition efficiency while ensuring annotation quality. Furthermore, an improved pedestrian detection method is proposed to overcome the performance degradation caused by significant perspective changes in top-down UAV views. Firstly, the View-Agnostic Decomposition (VAD) module decouples features into perspective-dependent and perspective-independent branches to enhance the model’s generalization ability to perspective variations. Secondly, the Deformable Conv-BN-SiLU (DCBS) module dynamically adjusts the receptive field shape to better adapt to the geometric deformations of pedestrians. Finally, the Context-Aware Pyramid Spatial Attention (CPSA) module integrates multi-scale features with attention mechanisms to address the challenge of drastic target scale variations. The experimental results demonstrate that the proposed method improves the mean Average Precision (mAP) by 9% on the NSV dataset, thereby validating that the approach effectively enhances pedestrian detection accuracy from UAV perspectives by optimizing perspective features. Full article