Search Results (15)

As industrialization and economic growth accelerate, PM_2.5 pollution has become a critical environmental concern. Predicting PM_2.5 concentration is challenging due to its nonlinear and complex temporal dynamics, limiting the accuracy and robustness of traditional machine learning models. To enhance prediction accuracy, this study focuses on Ma’anshan City, China and proposes a novel hybrid model (QMEWOA-QCAM-BiTCN-BiLSTM) based on an “optimization first, prediction later” approach. Feature selection using Pearson correlation and RFECV reduces model complexity, while the Whale Optimization Algorithm (WOA) optimizes model parameters. To address the local optima and premature convergence issues of WOA, we introduce a quantum-enhanced multi-strategy improved WOA (QMEWOA) for global optimization. A Quantum Causal Attention Mechanism (QCAM) is incorporated, leveraging Quantum State Mapping (QSM) for higher-order feature extraction. The experimental results show that our model achieves a MedAE of 1.997, MAE of 3.173, MAPE of 10.56%, and RMSE of 5.218, outperforming comparison models. Furthermore, generalization experiments confirm its superior performance across diverse datasets, demonstrating its robustness and effectiveness in PM_2.5 concentration prediction. Full article

In fields such as military reconnaissance, forest fire prevention, and autonomous driving at night, there is an urgent need for high-precision three-dimensional reconstruction in low-light or night environments. The acquisition of remote sensing data by RGB cameras relies on external light, resulting in a significant decline in image quality and making it difficult to meet the task requirements. The method based on lidar has poor imaging effects in rainy and foggy weather, close-range scenes, and scenarios requiring thermal imaging data. In contrast, infrared cameras can effectively overcome this challenge because their imaging mechanisms are different from those of RGB cameras and lidar. However, the research on three-dimensional scene reconstruction of infrared images is relatively immature, especially in the field of infrared binocular stereo matching. There are two main challenges given this situation: first, there is a lack of a dataset specifically for infrared binocular stereo matching; second, the lack of texture information in infrared images causes a limit in the extension of the RGB method to the infrared reconstruction problem. To solve these problems, this study begins with the construction of an infrared binocular stereo matching dataset and then proposes an innovative perspective projection positional encoding-based transformer method to complete the infrared binocular stereo matching task. In this paper, a stereo matching network combined with transformer and cost volume is constructed. The existing work in the positional encoding of the transformer usually uses a parallel projection model to simplify the calculation. Our method is based on the actual perspective projection model so that each pixel is associated with a different projection ray. It effectively solves the problem of feature extraction and matching caused by insufficient texture information in infrared images and significantly improves matching accuracy. We conducted experiments based on the infrared binocular stereo matching dataset proposed in this paper. Experiments demonstrated the effectiveness of the proposed method. Full article

Italian ryegrass is a high-quality forage grass, and a full understanding of the changes in its microbiome and metabolome during aerobic exposure can prolong its aerobic stability and improve its utilization value. Italian ryegrass silage was prepared with deionized water (CK), Lactobacillus rhamnosus BDy3-10 (LR), Lactobacillus buchneri TSy1-3 (LB), and a mixture of these two lactic acid bacteria (M). The silage was maintained at ambient temperature for 60 days followed by aerobic exposure. The results show that the Italian ryegrass silage in the LB and M groups exhibited aerobic stability for up to 19 days. A total of 1881 chemicals were identified in Italian ryegrass silage. These metabolites are associated with bacterial communities, especially Lactobacillus. The addition of lactic acid bacteria resulted in a common differential metabolic pathway compared to CK: “phenylpropanoid biosynthesis”. “Flavone and flavonol biosynthesis” was the significant differential metabolic pathway between LB and LR. Inoculation with LB significantly increased the concentrations of lactic acid, acetic acid, vitexin, and luteolin. In conclusion, lactic acid bacteria (LAB) additives affect the microbial community and metabolites of silage. The application of LB inoculants is a feasible way to obtain well-fermented Italian ryegrass silage and improve aerobic stability, even at higher moisture content levels. Full article

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites. Full article

The proficient application of multistage fracturing methods enhances the status of the Duvernay shale formation as a highly esteemed shale reservoir on a global scale. Nevertheless, the challenge is in accurately characterizing unconventional fracture behavior and predicting shale productivity due to the complex distributions of natural fractures, pre-existing faults, and reservoir heterogeneity. The present study puts forth a Geo-Engineering approach to comprehensively investigate the Duvernay shale reservoir in the vicinity of Crooked Lake. To begin with, on the basis of the experimental results and well-logging interpretations, a high-quality petrophysical and geomechanical model is constructed. Subsequently, the establishment of an unconventional fracture model (UFM) takes into account the heterogeneity of the reservoir and the interactions between hydraulic fractures and pre-existing natural fractures/faults and is further validated by 18,040 microseismic events. Finally, the analysis of well productivity is conducted by numerical simulations, revealing that the agreement between the simulated and observed production magnitudes exceeds 89%. This paper will guide the efficient development of increasingly important unconventional shale resources. Full article

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR’s monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR’s monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from −73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR. Full article

As more and more new power electronics with high switching frequencies are used in power-electronic equipment on ships and other platforms, the wideband radiation that power-electronic equipment gives off could affect sensitive equipment on the platform. Therefore, it is crucial to accurately model and characterize its wideband radiation properties to predict the wideband field distribution near such equipment. The traditional equivalent dipole method is commonly used to model power-electronic equipment at a single frequency. It is difficult to apply this method to wideband applications, and the precision of equivalent dipole array modeling at each frequency is insufficient. Additionally, acquiring the near-field phase data necessary for comparable modeling through practical measurement is frequently difficult. To solve the above problems, this paper proposes an equivalent dipole hybrid modeling method for power-electronic equipment’s broadband radiation characteristics. Starting with the near-field data at finite frequency points in a wideband, the method employs a global optimization algorithm to find the best equivalent dipole array, which characterizes the power-electronic equipment’s radiation characteristics. Furthermore, the interpolation technique is used to predict the wideband radiation properties of power-electronic equipment. Finally, test and numerical examples demonstrate the method’s accuracy and effectiveness. Full article

Music genre classification is significant to users and digital platforms. To enhance the classification accuracy, this study proposes a hybrid model based on VMD-IWOA-XGBOOST for music genre classification. First, the audio signals are transformed into numerical or symbolic data, and the crucial features are selected using the maximal information coefficient (MIC) method. Second, an improved whale optimization algorithm (IWOA) is proposed for parameter optimization. Third, the inner patterns of these selected features are extracted by IWOA-optimized variational mode decomposition (VMD). Lastly, all features are put into the IWOA-optimized extreme gradient boosting (XGBOOST) classifier. To verify the effectiveness of the proposed model, two open music datasets are used, i.e., GTZAN and Bangla. The experimental results illustrate that the proposed hybrid model achieves better performance than the other models in terms of five evaluation criteria. Full article

The contents of free and protein-bound advanced glycation end-products (AGEs) including N^ε-carboxymethyllysine (CML) and N^ε-carboxyethyllysine (CEL), along with glyoxal (GO), methylglyoxal (MGO), chemical components, and salt in commercially prepared and prefabricated fish products were analyzed. Snack food classified as commercially prepared products exhibited higher levels of GO (25.00 ± 3.34–137.12 ± 25.87 mg/kg of dry matter) and MGO (11.47 ± 1.39–43.23 ± 7.91 mg/kg of dry matter). Variations in the contents of free CML and CEL increased 29.9- and 73.0-fold, respectively. Protein-bound CML and CEL in commercially prepared samples were higher than those in raw prefabricated ones due to the impact of heat treatment. Levels of GO and MGO demonstrated negative correlations with fat (R = −0.720 and −0.751, p < 0.05) in commercially prepared samples, whereas positive correlations were observed (R = 0.526 and 0.521, p < 0.05) in raw prefabricated ones. The heat-induced formation of protein-bound CML and CEL showed a negative correlation with the variations of GO and MGO but was positively related to protein levels in prefabricated products, suggesting that GO and MGO may interact with proteins to generate AGEs during heating. The influence of NaCl on the formation of GO and MGO exhibited variations across different fish products, necessitating further investigation. Full article

The demand for eye-safe 1550 nm pulsed semiconductor laser-emission modules is increasing in the field of active laser detection, owing to their long range and high precision. The high power and narrow pulse of these modules can significantly improve the distance and accuracy of active-laser detection. Here, we propose an equivalent circuit model of a multiquantum-well laser based on the structure of a laser device. We developed a design method for 1550 nm pulsed semiconductor laser-emission modules according to the equivalent circuit model of an InGaAlAs laser. In this method, the module design was divided into laser chip and laser-driver levels for optimization and simulation. At the chip level, a high-output power laser chip with optimal cavity length and optical facet coating coefficients was obtained. At the laser-driver level, the model was applied to a drive circuit to provide direct narrow optical pulses. Finally, a laser-emission module was fabricated based on the optimal design results. In addition to the power-current features of the actual laser, the critical voltage of the emission module and laser pulses were tested. By comparing the test and simulation results, the effectiveness of the proposed method was confirmed. Full article

Image segmentation, which has become a research hotspot in the field of image processing and computer vision, refers to the process of dividing an image into meaningful and non-overlapping regions, and it is an essential step in natural scene understanding. Despite decades of effort and many achievements, there are still challenges in feature extraction and model design. In this paper, we review the advancement in image segmentation methods systematically. According to the segmentation principles and image data characteristics, three important stages of image segmentation are mainly reviewed, which are classic segmentation, collaborative segmentation, and semantic segmentation based on deep learning. We elaborate on the main algorithms and key techniques in each stage, compare, and summarize the advantages and defects of different segmentation models, and discuss their applicability. Finally, we analyze the main challenges and development trends of image segmentation techniques. Full article

The changes in protein degradation (TCA-soluble peptides), Schiff bases, dicarbonyl compounds (glyoxal-GO, methylglyoxal-MGO) and two typical advanced glycation end-products (AGEs) including N^ε-carboxymethyllysine (CML), N^ε-carboxyethyllysine (CEL) levels in ground pork supplemented with sucrose (4.0%) were investigated under nine freeze–thaw cycles and subsequent heating (100 °C/30 min). It was found that increase in freeze–thaw cycles promoted protein degradation and oxidation. The addition of sucrose further promoted the production of TCA-soluble peptides, Schiff bases and CEL, but not significantly, ultimately leading to higher levels of TCA-soluble peptides, Schiff bases, GO, MGO, CML, and CEL in the ground pork with the addition of sucrose than in the blank groups by 4%, 9%, 214%, 180%, 3%, and 56%, respectively. Subsequent heating resulted in severe increase of Schiff bases but not TCA-soluble peptides. Contents of GO and MGO all decreased after heating, while contents of CML and CEL increased. Full article

Image fusion technology can process multiple single image data into more reliable and comprehensive data, which play a key role in accurate target recognition and subsequent image processing. In view of the incomplete image decomposition, redundant extraction of infrared image energy information and incomplete feature extraction of visible images by existing algorithms, a fusion algorithm for infrared and visible image based on three-scale decomposition and ResNet feature transfer is proposed. Compared with the existing image decomposition methods, the three-scale decomposition method is used to finely layer the source image through two decompositions. Then, an optimized WLS method is designed to fuse the energy layer, which fully considers the infrared energy information and visible detail information. In addition, a ResNet-feature transfer method is designed for detail layer fusion, which can extract detailed information such as deeper contour structures. Finally, the structural layers are fused by weighted average strategy. Experimental results show that the proposed algorithm performs well in both visual effects and quantitative evaluation results compared with the five methods. Full article

In recent years, image fusion has been a research hotspot. However, it is still a big challenge to balance the problems of noiseless image fusion and noisy image fusion. In order to improve the weak performance and low robustness of existing image fusion algorithms in noisy images, an infrared and visible image fusion algorithm based on optimized low-rank matrix factorization with guided filtering is proposed. First, the minimized error reconstruction factorization is introduced into the low-rank matrix, which effectively enhances the optimization performance, and obtains the base image with good filtering performance. Then using the base image as the guide image, the source image is decomposed into the high-frequency layer containing detail information and noise, and the low-frequency layer containing energy information through guided filtering. According to the noise intensity, the sparse reconstruction error is adaptively obtained to fuse the high-frequency layers, and the weighted average strategy is utilized to fuse the low-frequency layers. Finally, the fusion image is obtained by reconstructing the pre-fused high-frequency layer and the pre-fused low-frequency layer. The comparative experiments show that the proposed algorithm not only has good performance for noise-free images, but more importantly, it can effectively deal with the fusion of noisy images. Full article

Lidar-based localization doesn’t have high accuracy in open scenarios with few features, and behaves poorly in robot kidnap recovery. To address this problem, an improved Particle Filter localization is proposed who could achieve robust robot kidnap detection and pose error compensation. UAPF adaptively updates the covariance by Jacobian from Ultra-wide Band information instead of predetermined parameters, and determines whether robot kidnap occurs by a novel criterion called KNP (Kidnap Probability). Besides, pose fusion of ranging-based localization and PF-based localization is conducted to decrease the uncertainty. To achieve more accurate ranging-based localization, linear regression of ranging data adopts values of maximum probability rather than average distances. Experiments show UAPF can achieve robot kidnap recovery in less than 2 s and position error is less than 0.1 m in a hall of 40 by 15 m, when the currently prevalent lidar-based localization costs more than 90 s and converges to wrong position. Full article