Search Results (106)

The United States leads in corn production and consumption in the world with an estimated USD 50 billion per year. There is a pressing need for the development of novel and efficient techniques aimed at enhancing the identification and eradication of weeds in a manner that is both environmentally sustainable and economically advantageous. Weed classification for autonomous agricultural robots is a challenging task for a single-camera-based system due to noise, vibration, and occlusion. To address this issue, we present a multi-camera-based system with decision-level sensor fusion to improve the limitations of a single-camera-based system in this paper. This study involves the utilization of a convolutional neural network (CNN) that was pre-trained on the ImageNet dataset. The CNN subsequently underwent re-training using a limited weed dataset to facilitate the classification of three distinct weed species: Xanthium strumarium (Common Cocklebur), Amaranthus retroflexus (Redroot Pigweed), and Ambrosia trifida (Giant Ragweed). These weed species are frequently encountered within corn fields. The test results showed that the re-trained VGG16 with a transfer-learning-based classifier exhibited acceptable accuracy (99% training, 97% validation, 94% testing accuracy) and inference time for weed classification from the video feed was suitable for real-time implementation. But the accuracy of CNN-based classification from video feed from a single camera was found to deteriorate due to noise, vibration, and partial occlusion of weeds. Test results from a single-camera video feed show that weed classification accuracy is not always accurate for the spray system of an agricultural robot (AgBot). To improve the accuracy of the weed classification system and to overcome the shortcomings of single-sensor-based classification from CNN, an improved Dempster–Shafer (DS)-based decision-level multi-sensor fusion algorithm was developed and implemented. The proposed algorithm offers improvement on the CNN-based weed classification when the weed is partially occluded. This algorithm can also detect if a sensor is faulty within an array of sensors and improves the overall classification accuracy by penalizing the evidence from a faulty sensor. Overall, the proposed fusion algorithm showed robust results in challenging scenarios, overcoming the limitations of a single-sensor-based system. Full article

Considering the complexity of the metro pit construction environment, the existing risk early-warning methods cannot ensure high-precision early warning. A high-accuracy metro pit collapse risk fusion early-warning method is proposed in present study. The main contributions include (1) presenting a new input to the fusion model by optimizing the machine learning model through a multi-step rolling method, and then using the basic probability assignment values obtained from the cloud model as input to the fusion model and (2) developing an improved methodology to address the paradoxical results of the fusion of traditional Dempster–Shafer evidence theory when there is a high level of conflict in multi-source risk prediction data. The proposed method is successfully applied to the Guangzhou Metro station project. By analyzing the early-warning results of 240 moments in 6 monitoring points, compared with the single information source method and the traditional D-S method, the early-warning accuracy of this method is increased by 15.8% and 10.8% respectively, the false alarm rate is reduced by 6.3% and 5.5%, respectively, and the missed alarm rate is reduced by 9.5% and 5.3%, respectively. The high-accuracy fusion early-warning method proposed in this paper has good universality and effectiveness in the early warning of subway foundation pit collapse risk. Full article

Aero engines are complex coupled systems in which faults in one subsystem can propagate and affect the performance of others. Relying on single-source performance parameters is often insufficient for accurately assessing component degradation. Although multi-source fusion diagnosis methods, such as those based on Bayesian networks, have been widely applied, their diagnostic performance remains limited when prior knowledge is scarce. To address this challenge, this paper proposes a multi-source information fusion diagnosis method for aero engine fault detection based on Dempster–Shafer (D-S) evidence theory. Data from gas path and vibration subsystems are separately processed to extract fault features, and a decision-level fusion strategy is employed to achieve comprehensive diagnoses. A case study based on real operational data from a two-shaft aero engine demonstrates that the proposed method significantly improves diagnostic performance. Specifically, the Bayesian-network-based fusion method achieves a diagnostic confidence of 87.2% without prior knowledge and 91.2% with prior knowledge incorporated, whereas D-S evidence theory attains a higher fault confidence of 99.6% without requiring any prior information. Full article

Influential node identification is an important and hot topic in the field of complex network science. Classical algorithms for identifying influential nodes are typically based on a single attribute of nodes or the simple fusion of a few attributes. However, these methods perform poorly in real networks with high complexity and diversity. To address this issue, a new method based on the Dempster–Shafer (DS) evidence theory is proposed in this paper, which improves the efficiency of identifying influential nodes through the following three aspects. Firstly, Dempster–Shafer evidence theory quantifies uncertainty through its basic belief assignment function and combines evidence from different information sources, enabling it to effectively handle uncertainty. Secondly, Dempster–Shafer evidence theory processes conflicting evidence using Dempster’s rule of combination, enhancing the reliability of decision-making. Lastly, in complex networks, information may come from multiple dimensions, and the Dempster–Shafer theory can effectively integrate this multidimensional information. To verify the effectiveness of the proposed method, extensive experiments are conducted on real-world complex networks. The results show that, compared to the other algorithms, attacking the influential nodes identified by the DS method is more likely to lead to the disintegration of the network, which indicates that the DS method is more effective for identifying the key nodes in the network. To further validate the reliability of the proposed algorithm, we use the visibility graph algorithm to convert the GBP futures time series into a complex network and then rank the nodes in the network using the DS method. The results show that the top-ranked nodes correspond to the peaks and troughs of the time series, which represents the key turning points in price changes. By conducting an in-depth analysis, investors can uncover major events that influence price trends, once again confirming the effectiveness of the algorithm. Full article

Geothermal resources are crucial for sustainable energy development, yet accurately detecting geothermal anomalies in complex terrains remains a significant challenge. This study develops a multi-source thermal infrared approach to enhance geothermal anomaly detection using Landsat 8 and ASTER land surface temperature (LST) data. The Yangbajing–Yangyi Basin in Tibet, characterized by high altitude and rugged topography, serves as the study area. Landsat 8 winter time-series data from 2013 to 2023 were processed on the Google Earth Engine (GEE) platform to generate multi-year average LST images. After water body removal and altitude correction, a local block thresholding method was applied to extract daytime geothermal anomalies. For nighttime data, ASTER LST products were analyzed using global, local block, elevation zoning, and fault buffer strategies to extract anomalies, which were then fused using Dempster–Shafer (D–S) evidence theory. A joint daytime–nighttime analysis identified stable geothermal anomaly regions, with results closely aligning with known geothermal fields and borehole distributions while predicting new potential anomaly zones. Additionally, a 21-year time-series analysis of MODIS nighttime LST data identified four significant thermal anomaly areas, interpreted as potential magma chambers, whose spatial distributions align with the identified anomalies. This multi-source approach highlights the potential of integrating thermal infrared data for geothermal anomaly detection, providing valuable insights for exploration in geologically complex regions. Full article

Osteoclasts (OCs) are important therapeutic targets in the treatment of osteoporosis. The aim of this study was to explore a novel therapeutic approach for osteoporosis using Arcyriaflavin A (ArcyA), a natural compound derived from the marine invertebrate Eudistoma sp. We systematically evaluated the effects of ArcyA on OC differentiation and function in mouse models using molecular biology assays, cellular function analyses and in vivo animal experiments. We also evaluated the efficacy of ArcyA in human cells. The TRAP staining results provide the first clear evidence of the drug’s inhibitory effect, whereby the administration of ArcyA led to a significant reduction in TRAP-positive cells compared to the control group at concentrations that were non-toxic to bone marrow macrophages. Meanwhile, a significant reduction in the number of multinucleated giant cells with more than ten nuclei was observed. Furthermore, similar TRAP staining results were reproduced in human OCs, suggesting that ArcyA has the same effect on OCs derived from human PBMCs. At the molecular level, ArcyA treatment resulted in the downregulation of genes relevant to OC differentiation (NFATc1, cFos and TNFrsf11α), fusion and survival (DCstamp and ATP6v0d2) and resorption function (CTSK, MMP9, integrin β3 and ACP5). A western blot analysis of the corresponding proteins (NFATc1, cFos, CTSK and integrin β3) further confirmed the PCR results. Furthermore, ArcyA-treated OCs produced significantly fewer resorption pits, indicating suppressed bone resorption activity. Consistent with this, in vivo experiments using an ovariectomy (OVX)-induced osteoporosis mouse model showed that ArcyA treatment significantly alleviated bone loss. Mice in the treatment groups had higher BV/TV values, and this therapeutic effect was enhanced in a dose-dependent manner. In addition, our research also showed that IκB could be a potential target for the inhibitory effect of ArcyA. In conclusion, these findings suggest that ArcyA has significant therapeutic potential for the treatment of osteoporosis by inhibiting osteoclastogenesis and bone resorption. Further studies are warranted to explore its clinical applications. Full article

Fatigue crack defects in metallic materials significantly reduce the remaining useful life (RUL) of parts. However, much of the existing research has focused on identifying single-millimeter-scale cracks using individual nonlinear ultrasonic responses. The identification of subtle parameters from complex ultrasonic responses of micro-crack groups remains a significant challenge in the field of nondestructive testing. We propose a novel multi-harmonic nonlinear response fusion identification method integrated with a deep learning (DL) model to identify the subtle parameters of micro-crack groups. First, we trained a one-dimensional convolutional neural network (1D CNN) with various time-domain signals obtained from finite element method (FEM) models and analyzed the sensitivity of different harmonic nonlinear responses to various subtle parameters of micro-crack groups. Then, high harmonics were fused to perform a decoupled identification of multiple subtle parameters. We enhanced the Dempster–Shafer (DS) evidence theory used in decision fusion by accounting for different sensitivities, achieving an identification accuracy of 93.73%. Building on this, we assigned sensor weights based on our proposed new conflict measurement method and further conducted decision fusion on the decision results from multiple ultrasonic sensors. Our proposed method achieves an identification accuracy of 95.68%. Full article

With the rapid development of electronic business, there has been a surge in incidents of trademark infringement, making it imperative to improve the accuracy of trademark retrieval systems as a key measure to combat such illegal behaviors. Evidently, the textual information encompassed within trademarks substantially influences the precision of search results. Considering the diversity of trademark text and the complexity of its design elements, accurately locating and analyzing this text poses a considerable challenge. Against this background, this research has developed an original self-prompting text removal model, denoted as “Self-prompting Trademark Text Removal Based on Multi-scale Texture Aggregation” (abbreviated as MTF-STTR). This model astutely applies a text detection network to automatically generate the required input cues for the Segment Anything Model (SAM) while incorporating the technological benefits of diffusion models to attain a finer level of trademark text removal. To further elevate the performance of the model, we introduce two innovative architectures to the text detection network: the Integrated Differentiating Feature Pyramid (IDFP) and the Texture Fusion Module (TFM). These mechanisms are capable of efficiently extracting multilevel features and multiscale textual information, which enhances the model’s stability and adaptability in complex scenarios. The experimental validation has demonstrated that the trademark text erasure model designed in this paper achieves a peak signal-to-noise ratio as high as 40.1 dB on the SCUT-Syn dataset, which is an average improvement of 11.3 dB compared with other text erasure models. Furthermore, the text detection network component of the designed model attains an accuracy of up to 89.9% on the CTW1500 dataset, representing an average enhancement of 10 percentage points over other text detection networks. Full article

In recent years, prefabricated buildings have developed rapidly in China. Compared with traditional buildings, prefabricated buildings require higher capabilities from partners in various aspects. However, due to the early stage of development of prefabricated buildings in China, the level of various enterprises varies greatly. How to evaluate partners scientifically and objectively is a realistic problem that needs to be solved urgently. In order to achieve economies of scale and promote the sustainable development of prefabricated buildings, this study proposes a novel evaluation model for strategic partner selection based on the cloud model and improved Dempster–Shafer (D-S) evidence theory. First, using a literature review and field research method, a strategic partner selection index system is developed that can reflect the characteristics of prefabricated buildings. To address the fuzziness and randomness of the traditional membership function, the cloud model is applied to calculate the membership value between the test samples and the benchmark cloud, which is subsequently transformed into basic probability distribution in the evidence theory. Furthermore, to mitigate the paradox of evidence fusion often encountered in traditional evidence theory, this model combines both the subjective and objective weights of evidence by game theory, and the conflicting evidence is corrected and fused according to the combination weight. Additionally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to further optimize the strategic partners of prefabricated buildings. Finally, the optimal order obtained from the case analysis is S₁ > S₂ > S₄ > S₃ > S₅, and the evaluation results are consistent with the actual situation, which verifies the effectiveness and superiority of the proposed model in resolving the evidence conflict and selecting strategic partners. The research results have certain reference significance for optimizing the selection mechanism of prefabricated building strategic partners and guiding partners to establish long-term and stable cooperative relations. Full article

Objective: It is hypothesized that the way nano- and micro-hybrid polymer-based composites are structured and cured impacts the way they respond to aging. Material and methods: A polymer–ceramic interpenetrating network composite (Vita Enamic/VE), an industrially polymerized (Brillinat CriosST/BC), and an in situ light-cured composite with discrete inorganic fillers (Admira Fusion5/AF5) were selected. Specimens (308) were either cut from CAD/CAM blocks (VE/BC) or condensed and cured in white polyoxymethylene molds (AF5) and subjected to four different aging conditions (n = 22): (a) 24 h storage in distilled water at 37 °C; (b) 24 h storage in distilled water at 37 °C followed by thermal cycling for 10,000 cycles 5/55 °C (TC); (c) TC followed by storage in a 75% ethanol–water solution; and (d) TC followed by a 3-week demineralization/remineralization cycling. CAD/CAM samples were also measured dry before the aging process. Three-point bending test, quantitative and qualitative fractography, instrumented indentation test (IIT), SEM, and reliability analyses were used. Uni- and multifactorial ANOVA, Tukey’s post hoc test, and Weibull analysis were performed for statistical analysis. Results: A significant (p < 0.001) and very strong effect of the parameter material was observed (η_P² > 0.9). VE exhibited two to three times higher elastic moduli and hardness parameters compared to BC and AF5, which were comparable. Strength was highest in BC but was accompanied by high beam deformation. The effect of aging was comparatively smaller and was more evident in the IIT parameters than in the flexural strength or modulus. Reliability was high (m > 15) in VE and BC, regardless of aging protocol, while it was significantly reduced in AF5 following aging protocols b-d. Conclusions: TC was the method of artificial aging with a significant impact on the measured parameters, while demineralization/remineralization cycling had little or no impact. Clinical relevance: The degradation of composites occurred irrespective of the structuring and curing method and manifested in a low deterioration in the measured properties. Full article

DC converter stations have a high voltage level, a long transmission distance, and complex internal equipment, and contain power electronic devices, which seriously endanger the stable operation of the system itself and the active distribution network at the receiving end when faults occur. Accurate fault analysis and diagnosis are critical to the safe and stable operation of power systems. Traditional fault diagnosis methods often rely on a single source of information, leading to issues such as insufficient information utilization and incomplete diagnostic scope when applied to DC transmission systems. To address these problems, a fault diagnosis method for converter stations based on preliminary identification of the fault range and the fusion of evidence information of the switch signal and electrical quantity is proposed. First, the preprocessing of converter station sequential event recording (SER) events and a statistical analysis of event characteristics are completed to initially determine the range of the fault.Then, a fuzzy Petri net model and a BP neural network model are constructed on the basis of the fault data from a real-time digital simulation system (RTDS), and the corresponding evidence information of the switch signal and electrical quantity are obtained via iterative inference and deep learning methods. Finally, on the basis of D-S evidence theory, a comprehensive diagnosis result is obtained by fusing the switch and electric evidence information. Taking the fault data of a DC converter station as an example, the proposed method is analyzed and compared with the traditional method, which is based on single information. The results show that the proposed method can reliably and accurately identify fault points in the protected area of the converter station. Full article

Due to the complex hydrogeological conditions and water hazards in coal mines, there are multiple indexes, complexities, incompatibilities, and uncertainty issues in the risk evaluation process of coal-mine water hazards. To accurately evaluate the risk of coal-mine water hazards, a comprehensive evaluation method based on extension theory, game theory, and Dempster–Shafer (DS) evidence theory is proposed. Firstly, a hierarchical water-hazard risk-evaluation index system is established, and then matter-element theory in extension theory is used to establish a matter-element model for coal-mine water-hazard risk. The membership relationship between various evaluation indexes and risk grades of coal-mine water-hazard risk is quantified using correlation functions of extension set theory, and the quantitative results are normalized to obtain basic belief assignments (BBAs) of risk grades for each index. Then, the subjective weights of evaluation indexes are calculated using the order relation analysis (G1) method, and the objective weights of evaluation indexes are calculated using the entropy weight (EW) method. The improved combination weighting method of game theory (ICWMGT) is introduced to determine the combination weight of each evaluation index, which is used to correct the BBAs of risk grades for each index. Finally, the fusion of DS evidence theory based on matrix analysis is used to fuse BBAs, and the rating with the highest belief fusion result is taken as the final evaluation result. The evaluation model was applied to the water-hazard risk evaluation of Sangbei Coal Mine, the evaluation result was of II grade water-hazard risk, and it was in line with the actual engineering situation. The evaluation result was compared with the evaluation results of three methods, namely the expert scoring method, the fuzzy comprehensive evaluation method, and the extension method. The scientificity and reliability of the method adopted in this paper were verified through this method. At the same time, based on the evaluation results, in-depth data mining was conducted on the risk indexes of coal-mine water hazards, and it was mainly found that 11 secondary indexes are the focus of coal-mine water-hazard risk prevention and control, among which seven indexes are the primary starting point for coal-mine water-hazard risk prevention and control. The groundwater index in particular has the most prominent impact. These results can provide a theoretical basis and scientific guidance for the specific water-hazard prevention and control work of coal mines. Full article

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in the DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second-level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes the BPA to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming six fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that there is a 67.5%, 83.5%, 76.8%, 83%, 79.6%, and 84.1% probability of detecting fire when it occurs, respectively, and identifies fire occurrence in approximately 2.4 s, an improvement from 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety. Full article

As the preferred technology in the current energy storage field, lithium-ion batteries cannot completely eliminate the occurrence of thermal runaway (TR) accidents. It is of significant importance to employ real-time monitoring and warning methods to perceive the battery’s safety status promptly and address potential safety hazards. Currently, the monitoring and warning of lithium-ion battery TR heavily rely on the judgment of single parameters, leading to a high false alarm rate. The application of multi-parameter early warning methods based on data fusion remains underutilized. To address this issue, the evaluation of lithium-ion battery safety status was conducted using the cloud model to characterize fuzziness and Dempster–Shafer (DS) evidence theory for evidence fusion, comprehensively assessing the TR risk level. The research determined warning threshold ranges and risk levels by monitoring voltage, temperature, and gas indicators during lithium-ion battery overcharge TR experiments. Subsequently, a multi-parameter fusion approach combining cloud model and DS evidence theory was utilized to confirm the risk status of the battery at any given moment. This method takes into account the fuzziness and uncertainty among multiple parameters, enabling an objective assessment of the TR risk level of lithium-ion batteries. Full article

Ship detection from synthetic aperture radar (SAR) imagery is crucial for various fields in real-world applications. Numerous deep learning-based detectors have been investigated for SAR ship detection, which requires a substantial amount of labeled data for training. However, SAR data annotation is time-consuming and demands specialized expertise, resulting in deep learning-based SAR ship detectors struggling due to a lack of annotations. With limited labeled data, semi-supervised learning is a popular approach for boosting detection performance by excavating valuable information from unlabeled data. In this paper, a semi-supervised SAR ship detection network is proposed, termed a Multi-Teacher Dempster-Shafer Evidence Fusion Net-work (MTDSEFN). The MTDSEFN is an enhanced framework based on the basic teacher–student skeleton frame, comprising two branches: the Teacher Group (TG) and the Agency Teacher (AT). The TG utilizes multiple teachers to generate pseudo-labels for different augmentation versions of unlabeled samples, which are then refined to obtain high-quality pseudo-labels by using Dempster-Shafer (D-S) fusion. The AT not only serves to deliver weights of its own teacher to the TG at the end of each epoch but also updates its own weights after each iteration, enabling the model to effectively learn rich information from unlabeled data. The combination of TG and AT guarantees both reliable pseudo-label generation and a comprehensive diversity of learning information from numerous unlabeled samples. Extensive experiments were performed on two public SAR ship datasets, and the results demonstrated the effectiveness and superiority of the proposed approach. Full article