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- 2018
- Fruit defects
- Jujube
- Principal component analysis
- Hyperspectral imaging
- Optoelectronic Information Science and Engineering
- Southern Taiwan University of Science and Technology
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Rapid Alloy Development of Extremely High-Alloyed Metals Using Powder Blends in Laser Powder Bed Fusion
摘要： The design of new alloys by and for metal additive manufacturing (AM) is an emerging field of research. Currently, pre-alloyed powders are used in metal AM, which are expensive and inflexible in terms of varying chemical composition. The present study describes the adaption of rapid alloy development in laser powder bed fusion (LPBF) by using elemental powder blends. This enables an agile and resource-efficient approach to designing and screening new alloys through fast generation of alloys with varying chemical compositions. This method was evaluated on the new and chemically complex materials group of multi-principal element alloys (MPEAs), also known as high-entropy alloys (HEAs). MPEAs constitute ideal candidates for the introduced methodology due to the large space for possible alloys. First, process parameters for LPBF with powder blends containing at least five different elemental powders were developed. Secondly, the influence of processing parameters and the resulting energy density input on the homogeneity of the manufactured parts were investigated. Microstructural characterization was carried out by optical microscopy, electron backscatter diffraction (EBSD), and energy-dispersive X-ray spectroscopy (EDS), while mechanical properties were evaluated using tensile testing. Finally, the applicability of powder blends in LPBF was demonstrated through the manufacture of geometrically complex lattice structures with energy absorption functionality.
关键词： multi-principal element alloys,high-entropy alloys,additive manufacturing,rapid alloy development,powder blends,laser powder bed fusion
更新于2025-11-21 11:01:37
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Enhancing multispectral remote sensing data interpretation for historical gold mines in Egypt: a case study from Madari gold mine
摘要： In the last decade, most of the outcrops around the historic gold mines in Egypt had been damaged by the local miners, a situation that complicated remote sensing-based exploration research activities. Madari gold mine area was no more fortunate than other mines in the region. This study identifies a new integrated remote sensing workflow that emphasizes the spectral variations related to differences in chemical and mineralogical compositions of the investigated rock units and deemphasizes the spectral variations introduced by the local miners. All combinations of ratio images are first generated from Landsat 8 Operational Land Imager (OLI) data, then a suite of ratio images that best differentiates between the investigated units is selected, and finally the selected ratio images were stacked to substitute the original image bands in the further processing techniques. The PCA was then applied to the selected ratio images within the stack. Subsequently, a statistical analysis of the eigenvector matrix for each of the PC bands was conducted to select the optimum PC bands and a Principal Component False Color Composite image (PC-FCC) was created from the three selected PC bands. The PC-FCC image (PC3, PC11, PC4 in RGB) was chosen as a result of subtracting the average PC eigenvector negative weights from the average positive eigenvectors weights. Not only was the PC-FCC image used to distinguish the main rock units in the damaged area, but also, to identify the areas with intense alteration zones.
关键词： Eastern Desert,Principal component analysis (PCA),Landsat 8 (OLI),Madari gold mine,Egypt,Ratio images
更新于2025-09-23 15:23:52
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Non-destructive assessment of the oxidative stability of intact macadamia nuts during the drying process by near-infrared spectroscopy
摘要： We have developed a rapid non-destructive method to assess the oxidative stability of intact macadamia nuts using near-infrared spectroscopy (NIRS). Intact macadamia nuts of the cultivars HAES 344 'Kau', HAES 660 'Keaau', IAC 4-12 B, and IAC Campinas B were harvested and immediately oven-dried for 4 days at 30 °C, 2 days at 40 °C, and 1 day at 60 °C to achieve 1.5% kernel moisture content. At each drying step nuts were withdrawn and their moisture content, peroxide value (PV), and acidity index (AI) determined. The best partial least square model for PV prediction was obtained using the Savitzky-Golay (SG) second derivative resulting in a standard error of prediction (SEP) of 0.55 meq·kg?1 and a coefficient of determination (R2 C) of 0.57. The best AI prediction-model result was obtained using the SG second derivative (SEP = 0.14%, R2 C = 0.29). Based on the maximum quality limits of 3 meq·kg?1 for PV and 0.5% for AI, the SEP values represented 18% and 28%, respectively. Therefore, the prediction method can be considered useful since the errors are lower than the quality limits. Thus, NIRS can be used to assess the oxidative stability of intact macadamia kernels.
关键词： principal component analysis,peroxide value,Macadamia integrifolia Maiden & Betche,acidity index
更新于2025-09-23 15:23:52
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PV shading fault detection and classification based on I-V curve using principal component analysis: Application to isolated PV system
摘要： Health monitoring and diagnosis of photovoltaic (PV) systems is becoming crucial to maximise the power production, increase the reliability and life service of PV power plants. Operating under faulty conditions, in particular under shading, PV plants have remarkable shape of current-voltage (I-V) characteristics in comparison to reference condition (healthy operation). Based on real electrical measurements (I-V), the present work aims to provide a very simple, robust and low cost Fault Detection and Classi?cation (FDC) method for PV shading faults. At ?rst, we extract the features for di?erent experimental tests under healthy and shading conditions to build the database. The features are then analysed using Principal Component Analysis (PCA). The accuracy of the data classi?cation into the PCA space is evaluated using the confusion matrix as a metric of class separability. The results using experimental data of a 250 Wp PV module are very promising with a successful classi?cation rate higher than 97% with four di?erent con?gurations. The method is also cost e?ective as it uses only electrical measurements that are already available. No additional sensors are required.
关键词： Fault classi?cation,Principal component analysis,Fault detection,I-V curves,PV shading faults
更新于2025-09-23 15:23:52
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Total polyphenol quantitation using integrated NIR and MIR spectroscopy: A case study of Chinese dates ( <i>Ziziphus jujuba</i> )
摘要： Polyphenols are the foremost measure of phytochemicals in Chinese dates due to their many potential health benefits such as averting cancers, reducing the risk of coronary artery disease, diuretic activity, myocardial stimulant, coronary dilator and muscle relaxant. To quantitate the polyphenols in Chinese dates using a data fusion approach with near‐infrared (NIR) and mid‐infrared (MIR) spectroscopy. A total of 80 Chinese dates samples were used for data acquisition from both NIR and MIR spectroscopy. The efficient spectral intervals were extracted by the synergy interval partial least square (Si‐PLS) algorithm as input variables for NIR‐MIR fusion model. A genetic algorithm (GA) was used to construct the model based on NIR‐MIR fusion. The performance of the developed models was evaluated using correlation coefficients of calibration (R2) and prediction (r2), root mean square error of prediction (RMSEP), bias and residual prediction deviation (RPD). The data fusion model based on the GA was superior compared to NIR and MIR build model. The optimal GA‐fusion model yielded R2 = 0.9621, r2 = 0.9451, RPD = 2.44, calibration set bias = 0.004 and prediction set bias = 0.061, computing only 15 variables. These findings reveal that integration of NIR and MIR is possible for the prediction of total polyphenol content in Chinese dates.
关键词： spectroscopy techniques,polyphenols,genetic algorithms,principal component analysis,spectral interval selection
更新于2025-09-23 15:23:52
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[Lecture Notes in Computational Vision and Biomechanics] Computer Aided Intervention and Diagnostics in Clinical and Medical Images Volume 31 || A Hybrid Fusion of Multimodal Medical Images for the Enhancement of Visual Quality in Medical Diagnosis
摘要： In the ?eld of medical imaging, Multimodal Medical Image Fusion (MIF) is a method of extracting complementary information from diverse source images from different modalities such as Magnetic Resonance Imaging, Computed Tomography, Single Photon Emission Computed Tomography, and Positron Emission Tomography and coalescing them into a resultant image. Image fusion of multimodal medical images is the present-day studies in the ?eld of medical imaging, biomedical research, and radiation medicine and is widely familiar by medical and engineering ?elds. In medical image fusion, single method of fusion is not pro?cient as it always lags in information while comparing with other available techniques. Hence, fusion for hybrid image is used to perform the image processing by applying multiple fusion rules. The integration of these results was obtained together as a single image. In proposed system, Shearlet Transform (ST) and Principal Component Analysis (PCA) are used to apply integrated fusion. The fusion technique is applied for CT that is Computed Tomography and Magnetic Resonance Imaging (MRI) images, where these images are ?rst transformed using the Shearlet Transform and PCA is applied to the transformed images. Finally, the fusion image is acquired using Inverse Shearlet transform (IST). The proposed system performance is evaluated by using speci?c metrics, and it is demonstrated that the outcome of proposed integrated fusion performs better when compared to existing fusion techniques.
关键词： Image fusion,Medical image,Shearlet Transform (ST),Principal Component Analysis (PCA)
更新于2025-09-23 15:23:52
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Detection of the adulteration of extra virgin olive oil by near-infrared spectroscopy and chemometric techniques
摘要： Introduction and Objectives: Due to the value of extra virgin olive oil (EVOO), adulteration has become an important issue in the industry, which has created demand for quick and inexpensive fraud detection testing. In contrast to many current food fraud detection methods, near-infrared spectroscopy (NIRS) can be inexpensive and convenient by minimizing sample preparation and measurement times. In this study, we developed a method using NIRS and chemometrics to detect adulteration of EVOO with other edible oil types that does not require sample preparation and can be completed in less than 10 min. Methods, Results, and Discussions: First, a single EVOO was adulterated with corn oil from 2.7% to 25% w/w. Spectra for the unadulterated sample and its adulterated counterparts were measured. A principal component analysis (PCA) scores plot showed separation between the adulterated mixtures and the unadulterated sample, which demonstrated that the developed method could detect as low as 2.7% w/w adulteration if an unadulterated sample of the oil in question is provided. To study adulteration detection without an unadulterated sample for reference, the spectra of unadulterated samples and samples adulterated with corn, sunflower, soybean, and canola oils were measured. A PCA with soft independent modelling of class analogy was used for adulteration detection. Lower limits of adulteration detection for corn, sunflower, soybean, and canola oils were found to be approximately 20%, 20%, 15%, and 10%, respectively. Conclusions: These results demonstrate that the developed method can be used to rapidly screen for adulterated olive oils.
关键词： principal component analysis,olive oil adulteration,chemometrics,NIR,spectroscopy
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyperspectral Anomaly Detection Using Compressed Columnwise Robust Principal Component Analysis
摘要： This paper proposes a compressed columnwise robust principal component analysis (CCRPCA) method for hyperspectral anomaly detection. The CCRPCA improves the regular RPCA by using the Hadamard random projection and constraining the columnwise structure of sparse anomaly matrix. The Hadamard random projection reduces the computational cost of the hyperspectral data, and the columnwise sparse structure alleviates negative effects from the anomalies on the columns of the background. The sparse anomaly matrix and the background matrix are estimated by optimizing a convex program, and the anomalies are estimated from nonzero columns of the compressed sparse matrix. Preliminary experiment result from the San Diego dataset shows that the CCRPCA outperforms four state-of-the-art detection methods in both the receiver operating characteristic curve and the area under curve.
关键词： anomaly detection,Hyperspectral imagery,columnwise robust principal component analysis,Hadamard random projection
更新于2025-09-23 15:23:52
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Image Enhancement Using Patch-Based Principal Energy Analysis
摘要： The visual quality of a captured image is often degraded by complicated lighting conditions in various real-world environments. This quality deterioration probably leads to the significant performance drop in many algorithms of computer vision, which require high-visibility inputs for precise results. In this paper, a novel method for image enhancement is proposed with the principal energy analysis. Specifically, based on the key observation that the illumination component is dominant over a small local region, the corresponding energy is efficiently separated from the scene reflectance by exploiting the subspace analysis. Owing to this clear separation, the illumination component can be easily adjusted independent of the reflectance layer for better visual aesthetics. In contrast to previous methods that still suffer from the exaggerated or conservative restoration yielding the loss of details and defects of halo artifacts, the proposed scheme has a good ability to enhance the image contrast while successfully preserving the color attribute of the original scene. Moreover, the proposed method is conceptually simple and easy to implement. Experimental results demonstrate the effectiveness of the proposed method even under diverse lighting conditions, e.g., low light, casting shadow, uneven illuminations, and so on, and the superiority of the proposed method over previous approaches introduced in the literature.
关键词： Quality deterioration,principal energy analysis,subspace analysis,illumination component,image enhancement
更新于2025-09-23 15:23:52
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[IEEE 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) - Bangkok, Thailand (2018.10.21-2018.10.24)] 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) - Optimal PCA-EOC-KNN Model for Detection of NS1 from Salivary SERS Spectra
摘要： Non Structural Protein 1 (NS1) has recently been known as an alternative biomarker for diseases caused by flavivirus. It has been clinically acknowledged for early detection of dengue infection, since NS1 presence in blood can be as early as the first day of infection. Surface Enhanced Raman Spectroscopy (SERS) is an improvement to Raman spectroscopy, which amplifies the intensity of Raman scattering so to be usable. This also enables SERS to detect molecular structure up to a single molecule. As such, it is favorable amongst researchers investigating disease biomarker. Algorithm k-nearest neighbor (kNN) is a strategy to classify an unknown based on learning data, nearest to the class. Our work here intends to determine the optimal nearest neighbor number, distance rule and classifier rule for PCA-EOC-KNN model for automated detection of NS1 fingerprint from SERS spectra of adulterated saliva. Results show that PCA-EOC-KNN classifier performs with accuracy, precision, sensitivity and specificity above 90%, using Consensus classifier rule, Euclidean or Correlation or Cosine distance rule and k-value of 1, 3 and 5.
关键词： k-Nearest Neighbour (kNN),Nonstructural Protein 1 (NS1),Principal Component Analysis (PCA)
更新于2025-09-23 15:22:29