Frothers facilitate the reduction of bubbles size by preventing bubbles coalescence and produce more stable froths.The collision probability of the bubbles and particles substantially increases by decreasing bubble si...Frothers facilitate the reduction of bubbles size by preventing bubbles coalescence and produce more stable froths.The collision probability of the bubbles and particles substantially increases by decreasing bubble size.For the same volume system,fewer bubbles result from a distribution of large-sized bubbles,and more bubbles result from a distribution of small-sized bubbles.In this research,fundamental two-phase frother characterization parameters were aimed to link with three-phase coal and talc flotation behavior.For this purpose,the effect of single and dual frother systems on inhibiting bubble coalescence was investigated with methyl isobutyl carbinol(MIBC),isooctanol(2 ethyl hexanol),pine oil,and Dowfroth 250.Based on the results of single frothers,isooctanol at the lowest critical coalescence concentration(CCC)value of 6×10^(−6) achieved the smallest bubbles with Sauter mean diameter of 0.80 mm.By blending Dowfroth 250 and pine oil,the bubbles size decreased significantly,reaching 0.45 mm.While the highest recoveries in coal flotation were obtained in single and frother blends where the bubbles size was measured as the smallest in two-phase system,and such a relationship was not found for talc flotation.展开更多
For the ultimate strength model test evaluation of large ship structures, the distortion model with non-uniform ratio between the main size and the plate thickness size is usually adopted. It is the key to carry out s...For the ultimate strength model test evaluation of large ship structures, the distortion model with non-uniform ratio between the main size and the plate thickness size is usually adopted. It is the key to carry out scale model test to establish a distortion model similar to the real ship structure under combined load. A similarity criterion for ship distortion model under the combined action of bending moment and surface pressure was proposed, and the scale effect for the criterion was verified by a se ries of numerical analysis and model tests. The results show that the similarity criterion for ship distor tion model under combined loads has a certain scale effect. For the model tests of ship cabin struc tures, it is suggested that the scale range between the plate thickness scale and the main dimension scale should be controlled within 2:1, which can be used as a reference for distortion model design and ultimate strength test of large-scale ship structures.展开更多
Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditi...Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditional cleanup methods and the challenges in detecting small targets,an improved YOLOv5 object detection model was proposed in this study.In order to enhance the model’s sensitivity to small targets and mitigate the impact of redundant information on detection performance,a bi-level routing attention mechanism was introduced and embedded into the backbone network.Additionally,a multi-scale detection head was incorporated into the model,allowing for more comprehensive coverage of floating garbage of various sizes through multi-scale feature extraction and detection.The Focal-EIoU loss function was also employed to optimize the model parameters,improving localization accuracy.Experimental results on the publicly available FloW_Img dataset demonstrated that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of precision and recall,achieving a mAP(mean average precision)of 86.12%,with significant improvements and faster convergence.展开更多
In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm...In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.展开更多
In order to solve the problems of color bias and visual deviation caused by inaccurate estimation of transmittance and atmospheric light in image defogging,a new algorithm based on multi-scale morphological reconstruc...In order to solve the problems of color bias and visual deviation caused by inaccurate estimation of transmittance and atmospheric light in image defogging,a new algorithm based on multi-scale morphological reconstruction with adaptive transmittance and atmospheric light correction was proposed.Firstly,the algorithm used the open operation under morphological reconstruction to replace the minimum filter operation in the dark channel,and used the morphological edge to set the scale of the open operation structure elements,and constructed a multi-scale open operation fusion dark channel.After morphological noise reduction,the exact initial transmittance was obtained.According to the relationship between brightness and saturation difference and transmittance,an adaptive transmittance correction model was fitted with Gaussian function to correct the initial transmittance of the sky fog map.Then the local atmospheric light was improved according to the image brightness information and morphology closure operation.Finally,the proposed algorithm was combined with the atmospheric scattering model to obtain an accurate fog free image.The experimental results showed that the proposed algorithm was suitable for fog image restoration under various scenes,the restoration effect was good,and the brightness was suitable.展开更多
The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning al...The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.展开更多
In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of ea...In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.展开更多
基金Project(ID42787)supported by the Istanbul Technical University,BAP(Scientific Research Project)Department,Turkey。
文摘Frothers facilitate the reduction of bubbles size by preventing bubbles coalescence and produce more stable froths.The collision probability of the bubbles and particles substantially increases by decreasing bubble size.For the same volume system,fewer bubbles result from a distribution of large-sized bubbles,and more bubbles result from a distribution of small-sized bubbles.In this research,fundamental two-phase frother characterization parameters were aimed to link with three-phase coal and talc flotation behavior.For this purpose,the effect of single and dual frother systems on inhibiting bubble coalescence was investigated with methyl isobutyl carbinol(MIBC),isooctanol(2 ethyl hexanol),pine oil,and Dowfroth 250.Based on the results of single frothers,isooctanol at the lowest critical coalescence concentration(CCC)value of 6×10^(−6) achieved the smallest bubbles with Sauter mean diameter of 0.80 mm.By blending Dowfroth 250 and pine oil,the bubbles size decreased significantly,reaching 0.45 mm.While the highest recoveries in coal flotation were obtained in single and frother blends where the bubbles size was measured as the smallest in two-phase system,and such a relationship was not found for talc flotation.
文摘For the ultimate strength model test evaluation of large ship structures, the distortion model with non-uniform ratio between the main size and the plate thickness size is usually adopted. It is the key to carry out scale model test to establish a distortion model similar to the real ship structure under combined load. A similarity criterion for ship distortion model under the combined action of bending moment and surface pressure was proposed, and the scale effect for the criterion was verified by a se ries of numerical analysis and model tests. The results show that the similarity criterion for ship distor tion model under combined loads has a certain scale effect. For the model tests of ship cabin struc tures, it is suggested that the scale range between the plate thickness scale and the main dimension scale should be controlled within 2:1, which can be used as a reference for distortion model design and ultimate strength test of large-scale ship structures.
文摘Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditional cleanup methods and the challenges in detecting small targets,an improved YOLOv5 object detection model was proposed in this study.In order to enhance the model’s sensitivity to small targets and mitigate the impact of redundant information on detection performance,a bi-level routing attention mechanism was introduced and embedded into the backbone network.Additionally,a multi-scale detection head was incorporated into the model,allowing for more comprehensive coverage of floating garbage of various sizes through multi-scale feature extraction and detection.The Focal-EIoU loss function was also employed to optimize the model parameters,improving localization accuracy.Experimental results on the publicly available FloW_Img dataset demonstrated that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of precision and recall,achieving a mAP(mean average precision)of 86.12%,with significant improvements and faster convergence.
文摘In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.
基金supported by National Natural Science Foundation of China(No.61561030)College Industry Support Plan Project of Gansu Provincial Department of Education(No.2021CYZC-04)Educational Reform Fund of Lanzhou Jiaotong University(No.JG201928)。
文摘In order to solve the problems of color bias and visual deviation caused by inaccurate estimation of transmittance and atmospheric light in image defogging,a new algorithm based on multi-scale morphological reconstruction with adaptive transmittance and atmospheric light correction was proposed.Firstly,the algorithm used the open operation under morphological reconstruction to replace the minimum filter operation in the dark channel,and used the morphological edge to set the scale of the open operation structure elements,and constructed a multi-scale open operation fusion dark channel.After morphological noise reduction,the exact initial transmittance was obtained.According to the relationship between brightness and saturation difference and transmittance,an adaptive transmittance correction model was fitted with Gaussian function to correct the initial transmittance of the sky fog map.Then the local atmospheric light was improved according to the image brightness information and morphology closure operation.Finally,the proposed algorithm was combined with the atmospheric scattering model to obtain an accurate fog free image.The experimental results showed that the proposed algorithm was suitable for fog image restoration under various scenes,the restoration effect was good,and the brightness was suitable.
基金supported by the Shaanxi Province Natural Science Basic Research Plan Project(2023-JC-YB-244).
文摘The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.
文摘In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.
