Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible pr...
详细信息
The methane combustion with hydrogen addition can effectively reduce carbon emissions in the iron and steel making industry,while the combustion mechanism is still poorly *** oxy-fuel combustion of methane with hydrog...
详细信息
The methane combustion with hydrogen addition can effectively reduce carbon emissions in the iron and steel making industry,while the combustion mechanism is still poorly *** oxy-fuel combustion of methane with hydrogen addition in a 0.8 MW oxy-natural gas combustion experimental furnace was numerically studied to investigate six different combustion *** results show that the 28-step chemical reaction mechanism is the optimal recommendation for the simulation balancing the numerical accuracy and computational *** the hydrogen enrichment increases in fuel,the highest flame temperature ***,the chemical reaction accelerates with enlarging the peak of the highest flame temperature and intermediate OH *** the hydrogen enrichment reaches 75 vol.%,the flame front is the farthest,and the flame high-temperature zone occupies the largest proportion corresponding to the most vigorous chemical reactions in the same oxygen supply.
High pressure grinding rolls(HPGR)mills are an energy efficient comminution device widely used in the cement and mineral processing *** roll wear and particle breakage near edges causes significant variation in grindi...
详细信息
High pressure grinding rolls(HPGR)mills are an energy efficient comminution device widely used in the cement and mineral processing *** roll wear and particle breakage near edges causes significant variation in grinding pressure along the axial *** study aimed to quantify the edge effect on mill performance through discrete element method(DEM)*** DEM model,coupled with a multi-body dynamics(MBD)model for the motion of the floating roll and a particle fracture model,was calibrated and validated by the experimental data from a lab-scale HPGR mill The simula-tions showed that the edge effect had the most significant impact on particle-particle compressive force and product size(characterised by the median particle size dso),followed by particle-roll force,and the least on *** roll length amplified the edge effect,causing larger variations in throughput,particle-roll force,and product size,while increasing roll size mitigated the edge effect,resulting in a more uniform product sizes and particle-wall *** the other hand,varying grinding pressure had a minimal impact on the edge effect.A unified equation was proposed to quantify changes from parabolic to trapezoidal *** proposed unified equation offers a new approach to predict changes in the wear and particle size profiles.
Keyhole instability during laser welding and laser powder bed fusion (LPBF) can cause keyhole collapse and pore formation. Using high-speed x-ray imaging, we demonstrate that the flow vortex–induced protrusion on the...
Strict control of powder properties,especially particle size distribution(PSD),is critical in the laser powder bed fusion(LPBF)process to ensure the quality of the fabricated *** work shows that reducing the powder si...
详细信息
Strict control of powder properties,especially particle size distribution(PSD),is critical in the laser powder bed fusion(LPBF)process to ensure the quality of the fabricated *** work shows that reducing the powder size could improve the ductility of LPBF fabricated *** tensile elongation increased from 4.1%to 8.8%when AlSi10Mg powder sizes(D50 value)decreased from 76μm to 16μm in the as-fabricated *** results showed that fine powder size stimulates epitaxial grain growth due to excessive *** presence of epitaxial columnar grains makes the crack path across inner melt pools more frequent and travels from the coarse melt pool zones to the fine melt pool *** work suggests that powder size is a crucial factor to be considered in maintaining repeatable mechanical properties in LPBF processes,particularly when ductility and ductility-dominated properties are critical.
Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible pr...
详细信息
ISBN:
(数字)9798350309249
ISBN:
(纸本)9798350309256
Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL
Brain tumor volume quantification is not possible with magnetic resonance imaging (MRI) non-invasive imaging systems. Usually, the brain MR imaging modality is based on four modalities T1, T1ce (contrast-enhanced), T2...
详细信息
The current study examines the impact of discrete wavelet transforms (DWT) and their success rate in the fingerprint images classification. The analyzed fingerprint images belong to the FVC2004 database. Two datasets ...
详细信息
Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast c...
详细信息
Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.61
暂无评论