Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional imag...Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.展开更多
Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance.To quantify the mineral dissemination and pore space distribution of an ore particle,a...Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance.To quantify the mineral dissemination and pore space distribution of an ore particle,a cylindrical copper oxide ore sample(I center dot 4.6 mm x 5.6 mm)was scanned using high-resolution X-ray computed tomography(HRXCT),a nondestructive imaging technology,at a spatial resolution of 4.85 mu m.Combined with three-dimensional(3D)image analysis techniques,the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated.In addition,the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques.Furthermore,the pore phase features,including the pore size distribution,pore surface area,pore fractal dimension,pore centerline,and the pore connectivity,were investigated quantitatively.The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated,with a large surface area and low connectivity.This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.展开更多
Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently d...Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.展开更多
Manufacturing-robust imaging systems leveraging computational optics hold immense potential for easing manufacturing constraints and enabling the development of cost-effective,high-quality imaging solutions.However,co...Manufacturing-robust imaging systems leveraging computational optics hold immense potential for easing manufacturing constraints and enabling the development of cost-effective,high-quality imaging solutions.However,conventional approaches,which typically rely on data-driven neural networks to correct optical aberrations caused by manufacturing errors,are constrained by the lack of effective tolerance analysis methods for quantitatively evaluating manufacturing error boundaries.This limitation is crucial for further relaxing manufacturing constraints and providing practical guidance for fabrication.We propose a physics-informed design paradigm for manufacturing-robust imaging systems with computational optics,integrating a physics-informed tolerance analysis methodology for evaluating manufacturing error boundaries and a physics-informed neural network for image reconstruction.With this approach,we achieve a manufacturing-robust imaging system based on an off-axis three-mirror freeform all-aluminum design,delivering a modulation transfer function exceeding 0.34 at the Nyquist frequency(72 lp/mm)in simulation.Notably,this system requires a manufacturing precision of only 0.5λin root mean square(RMS),representing a remarkable 25-fold relaxation compared with the conventional requirement of 0.02λin RMS.Experimental validation further confirmed that the manufacturing-robust imaging system maintains excellent performance in diverse indoor and outdoor environments.Our proposed method paves the way for achieving high-quality imaging without the necessity of high manufacturing precision,enabling practical solutions that are more cost-effective and time-efficient.展开更多
As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the...As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the precise data processing pipeline designed for wide-field,CMOS-based devices,including the removal of instrumental effects,astrometry,photometry,and flux calibration.When applying this pipeline to approximately3000 observations taken in the Field 02(f02)region by MST,the results demonstrate a remarkable astrometric precision of approximately 70–80 mas(about 0.1 pixel),an impressive calibration accuracy of approximately1 mmag in the MST zero points,and a photometric accuracy of about 4 mmag for bright stars.Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs,highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys,like the SiTian project.展开更多
Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced ima...Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.展开更多
In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers a...In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.展开更多
BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is one of the most lethal malignancies with high mortality and short survival time.Computed tomography(CT)plays an important role in the diagnosis,staging and treatment...BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is one of the most lethal malignancies with high mortality and short survival time.Computed tomography(CT)plays an important role in the diagnosis,staging and treatment of pancreatic tumour.Pancreatic cancer generally shows a low enhancement pattern compared with normal pancreatic tissue.AIM To analyse whether preoperative enhanced CT could be used to predict postoperative overall survival in patients with PDAC.METHODS Sixty-seven patients with PDAC undergoing pancreatic resection were enrolled retrospectively.All patients underwent preoperative unenhanced and enhanced CT examination,the CT values of which were measured.The ratio of the preoperative CT value increase from the nonenhancement phase to the portal venous phase between pancreatic tumour and normal pancreatic tissue was calculated.The cut-off value of ratios was obtained by the receiver operating characteristic(ROC)curve of the tumour relative enhancement ratio(TRER),according to which patients were divided into low-and high-enhancement groups.Univariate and multivariate analyses were performed using Cox regression based on TRER grouping.Finally,the correlation between TRER and clinicopathological characteristics was analysed.RESULTS The area under the curve of the ROC curve was 0.768(P<0.05),and the cut-off value of the ROC curve was calculated as 0.7.TRER≤0.7 was defined as the low-enhancement group,and TRER>0.7 was defined as the high-enhancement group.According to the TRER grouping,the Kaplan-Meier survival curve analysis results showed that the median survival(10.0 mo)with TRER≤0.7 was significantly shorter than that(22.0 mo)with TRER>0.7(P<0.05).In the univariate and multivariate analyses,the prognosis of patients with TRER≤0.7 was significantly worse than that of patients with TRER>0.7(P<0.05).Our results demonstrated that patients in the low TRER group were more likely to have higher American Joint Committee on Cancer stage,tumour stage and lymph node stage(all P<0.05),and TRER was significantly negatively correlated with tumour size(P<0.05).CONCLUSION TRER≤0.7 in patients with PDAC may represent a tumour with higher clinical stage and result in a shorter overall survival.展开更多
The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its...The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .展开更多
Subcutaneous vein network plays important roles to maintain microcirculation that is related to some diagnostic aspects.Despite developments of optical imaging technologies,still the difficulties about deep skin vascu...Subcutaneous vein network plays important roles to maintain microcirculation that is related to some diagnostic aspects.Despite developments of optical imaging technologies,still the difficulties about deep skin vascular imaging have been continued.On the other hand,since hemoglobin con-centration of human blood has key role in the veins imaging by optical manner,the used wavelength in vascular imaging,must be chosen considering absorption of hemoglobin.In this research,we constructed a near infrared(NIR)light source because of lower absorption of hemoglobin in this optical region.To obtain vascular image,reflectance geometry was used.Next,from recorded images,vascular network analysis,such as calculation of width of vascular of interest and complexity of selected region were implemented.By comparing with other modalities,we observed that proposed imaging system has great advantages including nonionized radiation,moderate penetration depth of 0.5-3 mm and diameter of 1 mm,cost-effective and algorit hmic simplicity for analysis.展开更多
An air classifier is used in the recycling process of covered electric wire in the recycling factories, in which the covered electric wires are crushed, sieved, and classified by the air classifier, which generates wa...An air classifier is used in the recycling process of covered electric wire in the recycling factories, in which the covered electric wires are crushed, sieved, and classified by the air classifier, which generates wastes. In these factories, operators manually adjust the air flow rate while checking the wastes discharged from the separator outlet. However, the adjustments are basically done by trial and error, and it is difficult to do them appropriately. In this study, we tried to develop the image processing system that calculates the ratio of copper (Cu) product and polyvinyl chloride (PVC) in the wastes as a substitute for the operator’s eyes. Six colors of PVC (white, gray, green, blue, black, and red) were used in the present work. An image consists of foreground and background. An image’s regions of interest are objects (Cu particles) in its foreground. However, the particles having a color similar to the background color are buried in the background. Using the difference of two color backgrounds, we separated particles and background without dependent of background. The Otsu’ thresholding was employed to choose the threshold to maximize the degree of separation of the particles and background. The ratio of Cu to PVC pixels from mixed image was calculated by linear discriminant analysis. The error of PVC pixels resulted in zero, whereas the error of Cu pixels arose to 4.19%. Comparing the numbers of Cu and PVC pixels within the contour, the minority of the object were corrected to the majority of the object. The error of Cu pixels discriminated as PVC incorrectly became zero percent through this correction.展开更多
The velocity profile determined by the gas pressure in the gas gap during molten metal filling in Lost Foam Process was numerically simulated. The results show that the molten metal flows forward in a circular-arc sha...The velocity profile determined by the gas pressure in the gas gap during molten metal filling in Lost Foam Process was numerically simulated. The results show that the molten metal flows forward in a circular-arc shape from the ingate, which is different from that in traditional green sand casting.展开更多
Pancreatic ductal adenocarcinoma(PDAC)is one of the most lethal malignancies because of its high invasiveness and metastatic potential.Computed tomography(CT)is often used as a preliminary diagnostic tool for pancreat...Pancreatic ductal adenocarcinoma(PDAC)is one of the most lethal malignancies because of its high invasiveness and metastatic potential.Computed tomography(CT)is often used as a preliminary diagnostic tool for pancreatic cancer,and it is increasingly used to predict treatment response and disease stage.Recently,a study published in World Journal of Gastroenterology reported that quantitative analysis of preoperative enhanced CT data can be used to predict postoperative overall survival in patients with PDAC.A tumor relative enhancement ratio of≤0.7 indicates a higher tumor stage and poor prognosis.展开更多
The present work focuses on the development of a novel computer-based approach for tear ferning(TF)featuring.The original TF images of the recently developedfive-point grading scale have been used to assign a grade fo...The present work focuses on the development of a novel computer-based approach for tear ferning(TF)featuring.The original TF images of the recently developedfive-point grading scale have been used to assign a grade for any TF image automatically.A vector characteristic(VC)representing each grade was built using the reference images.A weighted combination between features selected from textures analysis using gray level co-occurrence matrix(GLCM),power spectrum(PS)analysis and linear specificity of the image were used to build the VC of each grade.A total of 14 features from texture analysis were used.PS at di®erent frequency points and number of line segments in each image were also used.Five features from GLCM have shown significant di®erences between the recently developed grading scale images which are:angular second moment at 0and 45,contrast,and correlation at 0and 45;thesefive features were all included in the characteristic vector.Three specific power frequencies were used in the VC because of the discrimination power.Number of line segments was also chosen because of dissimilarities between images.A VC for each grade of TF reference images was constructed and was found to be significantly different from each other's.This is a basic and fundamental step toward an automatic grading for computer-based diagnosis for dry eye.展开更多
Objective:To investigate the collagen distribution pattern in the normal supraspinatus tendon with use of com- puter imaging analysis system, and through this way, to probe into the underlying relationship between the...Objective:To investigate the collagen distribution pattern in the normal supraspinatus tendon with use of com- puter imaging analysis system, and through this way, to probe into the underlying relationship between the collagen distribu- tion pattern and supraspinatus tendon tears. Methods: The slice specimens of normal supraspinatus tendon, with histological and immunohistochemical staining, were divided into 2 groups according to their respective distance of selected cross-sec- tions from the insertion of supraspinatus tendon, namely, one was at a distance of 1 cm near the insertion of supraspinatus tendon (Group A); the other was 2 cm close to the insertion (Group B). Computer imaging analysis system was employed for detecting the collagen area percentage on the cross-section of tendon. The Obtained data were processed by Spss8 .0. Results: ①The collagen cross-section area percentage in Group A was smaller than that in Group B. ②Type Ⅰ and Ⅲ colla- gen area percentage in Group A were smaller than those in Group B respectively. ③ In the same group of A or B, type Ⅰ collagen area percentage was conspicuously larger than that type Ⅲ held. Conclusion: There exists a significant difference of coffagen distribution near the insertion of normal supraspinatus tendon; the disparity pattern of collagen distribution is directly pertinent to the pre-existing hypovascularity zone in this region, which could play a certain part in the pathogenesis of supraspinatus tendon tears, and could be an intrinsic factor contributing to the etiology of supraspinatus tendon tears.展开更多
To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation f...To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.展开更多
<strong>Background:</strong> Worldwide, prostatic adenocarcinoma is the most common tumour type among men. <strong>Aim:</strong> The aim of the present investigation was to develop a computer p...<strong>Background:</strong> Worldwide, prostatic adenocarcinoma is the most common tumour type among men. <strong>Aim:</strong> The aim of the present investigation was to develop a computer program to identify normal prostate biopsies and distinguish them from biopsies showing premalignant alterations (LGPIN, HGPIN) and adenocarcinoma. <strong>Method:</strong> Prostate biopsies (n = 2094) taken from 191 consecutive men during 2016 were stained with triple immunehistochemisty (antibodies to AMACRA, p63 and CK 5). Digital images of the biopsies were obtained with a scanning microscope and used to develop an automatic computer program (CelldaTM), intended to identify the morphological alterations. Visual microscopic finding was used as a reference. <strong>Result:</strong> Of the 191 men, 121 (63.4%) were diagnosed as having prostate adenocarcinoma and 70 (36.6%) as having no malignancy on the basis of the visual microscopy. In comparison, computer analysis identified 134 (70.2%) men with malignant disease and 57 (29.8%) with non-malignant disease after exclusion of artifacts, which constituted 10.4% of areas (indicated as malignant disease). Discrepant results were recorded in 15 (7.9%) men, and in 14 of these cases, HGPIN and areas suggestive of early invasion were common. Thus, it was uncertain whether these cases should be regarded as malignant or not. The agreement between the visual examination and the computer analysis was 92.1% (kappa value 0.823, sensitivity 99.2 and specificity was 0.80). <strong>Conclusion:</strong> It seems that computer analysis could serve as an adjunct to simplify and shorten the diagnostic procedure, first of all by ensuring that normal prostate biopsies are sorted out from those sent for visual microscopic evaluation.展开更多
Application of the computer image analysis for improving microbial viability assessment by plate count and fluorescence microscopy was investigated. Yeast cells were used as a model microorganism. The application of t...Application of the computer image analysis for improving microbial viability assessment by plate count and fluorescence microscopy was investigated. Yeast cells were used as a model microorganism. The application of the improved methods for the viability assessment of yeast cells after preservation by freezing and freeze-drying was demonstrated.展开更多
The flexibility of traditional image processing system is limited because those system are designed for specific applications. In this paper, a new TMS320C64x-based multi-DSP parallel computing architecture is present...The flexibility of traditional image processing system is limited because those system are designed for specific applications. In this paper, a new TMS320C64x-based multi-DSP parallel computing architecture is presented. It has many promising characteristics such as powerful computing capability, broad I/O bandwidth, topology flexibility, and expansibility. The parallel system performance is evaluated by practical experiment.展开更多
The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can b...The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.展开更多
基金Projects(50934002,51074013,51304076,51104100)supported by the National Natural Science Foundation of ChinaProject(IRT0950)supported by the Program for Changjiang Scholars Innovative Research Team in Universities,ChinaProject(2012M510007)supported by China Postdoctoral Science Foundation
文摘Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.
基金financially supported by the National Natural Science Foundation of China(No.51304076)the Natural Science Foundation of Hunan Province,China(No.14JJ4064)
文摘Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance.To quantify the mineral dissemination and pore space distribution of an ore particle,a cylindrical copper oxide ore sample(I center dot 4.6 mm x 5.6 mm)was scanned using high-resolution X-ray computed tomography(HRXCT),a nondestructive imaging technology,at a spatial resolution of 4.85 mu m.Combined with three-dimensional(3D)image analysis techniques,the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated.In addition,the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques.Furthermore,the pore phase features,including the pore size distribution,pore surface area,pore fractal dimension,pore centerline,and the pore connectivity,were investigated quantitatively.The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated,with a large surface area and low connectivity.This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.
基金supported in part by the National Science Fund for Distinguished Young Scholars of China(62225303)the National Natural Science Fundation of China(62303039,62433004)+2 种基金the China Postdoctoral Science Foundation(BX20230034,2023M730190)the Fundamental Research Funds for the Central Universities(buctrc202201,QNTD2023-01)the High Performance Computing Platform,College of Information Science and Technology,Beijing University of Chemical Technology
文摘Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.
基金supported by the National Natural Science Foundation of China(Grant Nos.62192774,62105243,61925504,6201101335,62020106009,62192770,62192772,62105244,62305250,and 62322217)the Science and Technology Commission of Shanghai Municipality(Grant Nos.17JC1400800,20JC1414600,and 21JC1406100)+1 种基金the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100)the Fundamental Research Funds for the Central Universities.
文摘Manufacturing-robust imaging systems leveraging computational optics hold immense potential for easing manufacturing constraints and enabling the development of cost-effective,high-quality imaging solutions.However,conventional approaches,which typically rely on data-driven neural networks to correct optical aberrations caused by manufacturing errors,are constrained by the lack of effective tolerance analysis methods for quantitatively evaluating manufacturing error boundaries.This limitation is crucial for further relaxing manufacturing constraints and providing practical guidance for fabrication.We propose a physics-informed design paradigm for manufacturing-robust imaging systems with computational optics,integrating a physics-informed tolerance analysis methodology for evaluating manufacturing error boundaries and a physics-informed neural network for image reconstruction.With this approach,we achieve a manufacturing-robust imaging system based on an off-axis three-mirror freeform all-aluminum design,delivering a modulation transfer function exceeding 0.34 at the Nyquist frequency(72 lp/mm)in simulation.Notably,this system requires a manufacturing precision of only 0.5λin root mean square(RMS),representing a remarkable 25-fold relaxation compared with the conventional requirement of 0.02λin RMS.Experimental validation further confirmed that the manufacturing-robust imaging system maintains excellent performance in diverse indoor and outdoor environments.Our proposed method paves the way for achieving high-quality imaging without the necessity of high manufacturing precision,enabling practical solutions that are more cost-effective and time-efficient.
基金supported by the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)+3 种基金the National Science Foundation of China 12422303,12403024,12222301,12173007,and 12261141690the Postdoctoral Fellowship Program of CPSF under grant Number GZB20240731the Young Data Scientist Project of the National Astronomical Data Center,and the China Post-doctoral Science Foundation No.2023M743447support from the NSFC through grant No.12303039 and No.12261141690.
文摘As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the precise data processing pipeline designed for wide-field,CMOS-based devices,including the removal of instrumental effects,astrometry,photometry,and flux calibration.When applying this pipeline to approximately3000 observations taken in the Field 02(f02)region by MST,the results demonstrate a remarkable astrometric precision of approximately 70–80 mas(about 0.1 pixel),an impressive calibration accuracy of approximately1 mmag in the MST zero points,and a photometric accuracy of about 4 mmag for bright stars.Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs,highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys,like the SiTian project.
基金funded by Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/MRC/13/771-4.
文摘Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.
基金supported by the National Key R&D Program of China(2017YFF0205600)the International Research Cooperation Seed Fund of Beijing University of Technology(2018A08)+1 种基金Science and Technology Project of Beijing Municipal Commission of Transport(2018-kjc-01-213)the Construction of Service Capability of Scientific and Technological Innovation-Municipal Level of Fundamental Research Funds(Scientific Research Categories)of Beijing City(PXM2019_014204_500032).
文摘In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.
基金Supported by the Medical Centre of Minimally Invasive Technology of Fujian Province,No.2017[171],and No.2017[4]Joint Funds for the Innovation of Science and Technology,Fujian Province,No.2017Y9059the United Fujian Provincial Health and Education Project for Tackling the Key Research,No.2019-WJ-07.
文摘BACKGROUND Pancreatic ductal adenocarcinoma(PDAC)is one of the most lethal malignancies with high mortality and short survival time.Computed tomography(CT)plays an important role in the diagnosis,staging and treatment of pancreatic tumour.Pancreatic cancer generally shows a low enhancement pattern compared with normal pancreatic tissue.AIM To analyse whether preoperative enhanced CT could be used to predict postoperative overall survival in patients with PDAC.METHODS Sixty-seven patients with PDAC undergoing pancreatic resection were enrolled retrospectively.All patients underwent preoperative unenhanced and enhanced CT examination,the CT values of which were measured.The ratio of the preoperative CT value increase from the nonenhancement phase to the portal venous phase between pancreatic tumour and normal pancreatic tissue was calculated.The cut-off value of ratios was obtained by the receiver operating characteristic(ROC)curve of the tumour relative enhancement ratio(TRER),according to which patients were divided into low-and high-enhancement groups.Univariate and multivariate analyses were performed using Cox regression based on TRER grouping.Finally,the correlation between TRER and clinicopathological characteristics was analysed.RESULTS The area under the curve of the ROC curve was 0.768(P<0.05),and the cut-off value of the ROC curve was calculated as 0.7.TRER≤0.7 was defined as the low-enhancement group,and TRER>0.7 was defined as the high-enhancement group.According to the TRER grouping,the Kaplan-Meier survival curve analysis results showed that the median survival(10.0 mo)with TRER≤0.7 was significantly shorter than that(22.0 mo)with TRER>0.7(P<0.05).In the univariate and multivariate analyses,the prognosis of patients with TRER≤0.7 was significantly worse than that of patients with TRER>0.7(P<0.05).Our results demonstrated that patients in the low TRER group were more likely to have higher American Joint Committee on Cancer stage,tumour stage and lymph node stage(all P<0.05),and TRER was significantly negatively correlated with tumour size(P<0.05).CONCLUSION TRER≤0.7 in patients with PDAC may represent a tumour with higher clinical stage and result in a shorter overall survival.
基金This work was supported by Science and Technology Project of State Grid Corporation“Research on Key Technologies of Power Artificial Intelligence Open Platform”(5700-202155260A-0-0-00).
文摘The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .
基金Scientic and Technological Research Council of Turkey(TUBITAK),under grand,No:113E771.
文摘Subcutaneous vein network plays important roles to maintain microcirculation that is related to some diagnostic aspects.Despite developments of optical imaging technologies,still the difficulties about deep skin vascular imaging have been continued.On the other hand,since hemoglobin con-centration of human blood has key role in the veins imaging by optical manner,the used wavelength in vascular imaging,must be chosen considering absorption of hemoglobin.In this research,we constructed a near infrared(NIR)light source because of lower absorption of hemoglobin in this optical region.To obtain vascular image,reflectance geometry was used.Next,from recorded images,vascular network analysis,such as calculation of width of vascular of interest and complexity of selected region were implemented.By comparing with other modalities,we observed that proposed imaging system has great advantages including nonionized radiation,moderate penetration depth of 0.5-3 mm and diameter of 1 mm,cost-effective and algorit hmic simplicity for analysis.
文摘An air classifier is used in the recycling process of covered electric wire in the recycling factories, in which the covered electric wires are crushed, sieved, and classified by the air classifier, which generates wastes. In these factories, operators manually adjust the air flow rate while checking the wastes discharged from the separator outlet. However, the adjustments are basically done by trial and error, and it is difficult to do them appropriately. In this study, we tried to develop the image processing system that calculates the ratio of copper (Cu) product and polyvinyl chloride (PVC) in the wastes as a substitute for the operator’s eyes. Six colors of PVC (white, gray, green, blue, black, and red) were used in the present work. An image consists of foreground and background. An image’s regions of interest are objects (Cu particles) in its foreground. However, the particles having a color similar to the background color are buried in the background. Using the difference of two color backgrounds, we separated particles and background without dependent of background. The Otsu’ thresholding was employed to choose the threshold to maximize the degree of separation of the particles and background. The ratio of Cu to PVC pixels from mixed image was calculated by linear discriminant analysis. The error of PVC pixels resulted in zero, whereas the error of Cu pixels arose to 4.19%. Comparing the numbers of Cu and PVC pixels within the contour, the minority of the object were corrected to the majority of the object. The error of Cu pixels discriminated as PVC incorrectly became zero percent through this correction.
文摘The velocity profile determined by the gas pressure in the gas gap during molten metal filling in Lost Foam Process was numerically simulated. The results show that the molten metal flows forward in a circular-arc shape from the ingate, which is different from that in traditional green sand casting.
文摘Pancreatic ductal adenocarcinoma(PDAC)is one of the most lethal malignancies because of its high invasiveness and metastatic potential.Computed tomography(CT)is often used as a preliminary diagnostic tool for pancreatic cancer,and it is increasingly used to predict treatment response and disease stage.Recently,a study published in World Journal of Gastroenterology reported that quantitative analysis of preoperative enhanced CT data can be used to predict postoperative overall survival in patients with PDAC.A tumor relative enhancement ratio of≤0.7 indicates a higher tumor stage and poor prognosis.
文摘The present work focuses on the development of a novel computer-based approach for tear ferning(TF)featuring.The original TF images of the recently developedfive-point grading scale have been used to assign a grade for any TF image automatically.A vector characteristic(VC)representing each grade was built using the reference images.A weighted combination between features selected from textures analysis using gray level co-occurrence matrix(GLCM),power spectrum(PS)analysis and linear specificity of the image were used to build the VC of each grade.A total of 14 features from texture analysis were used.PS at di®erent frequency points and number of line segments in each image were also used.Five features from GLCM have shown significant di®erences between the recently developed grading scale images which are:angular second moment at 0and 45,contrast,and correlation at 0and 45;thesefive features were all included in the characteristic vector.Three specific power frequencies were used in the VC because of the discrimination power.Number of line segments was also chosen because of dissimilarities between images.A VC for each grade of TF reference images was constructed and was found to be significantly different from each other's.This is a basic and fundamental step toward an automatic grading for computer-based diagnosis for dry eye.
文摘Objective:To investigate the collagen distribution pattern in the normal supraspinatus tendon with use of com- puter imaging analysis system, and through this way, to probe into the underlying relationship between the collagen distribu- tion pattern and supraspinatus tendon tears. Methods: The slice specimens of normal supraspinatus tendon, with histological and immunohistochemical staining, were divided into 2 groups according to their respective distance of selected cross-sec- tions from the insertion of supraspinatus tendon, namely, one was at a distance of 1 cm near the insertion of supraspinatus tendon (Group A); the other was 2 cm close to the insertion (Group B). Computer imaging analysis system was employed for detecting the collagen area percentage on the cross-section of tendon. The Obtained data were processed by Spss8 .0. Results: ①The collagen cross-section area percentage in Group A was smaller than that in Group B. ②Type Ⅰ and Ⅲ colla- gen area percentage in Group A were smaller than those in Group B respectively. ③ In the same group of A or B, type Ⅰ collagen area percentage was conspicuously larger than that type Ⅲ held. Conclusion: There exists a significant difference of coffagen distribution near the insertion of normal supraspinatus tendon; the disparity pattern of collagen distribution is directly pertinent to the pre-existing hypovascularity zone in this region, which could play a certain part in the pathogenesis of supraspinatus tendon tears, and could be an intrinsic factor contributing to the etiology of supraspinatus tendon tears.
文摘To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.
文摘<strong>Background:</strong> Worldwide, prostatic adenocarcinoma is the most common tumour type among men. <strong>Aim:</strong> The aim of the present investigation was to develop a computer program to identify normal prostate biopsies and distinguish them from biopsies showing premalignant alterations (LGPIN, HGPIN) and adenocarcinoma. <strong>Method:</strong> Prostate biopsies (n = 2094) taken from 191 consecutive men during 2016 were stained with triple immunehistochemisty (antibodies to AMACRA, p63 and CK 5). Digital images of the biopsies were obtained with a scanning microscope and used to develop an automatic computer program (CelldaTM), intended to identify the morphological alterations. Visual microscopic finding was used as a reference. <strong>Result:</strong> Of the 191 men, 121 (63.4%) were diagnosed as having prostate adenocarcinoma and 70 (36.6%) as having no malignancy on the basis of the visual microscopy. In comparison, computer analysis identified 134 (70.2%) men with malignant disease and 57 (29.8%) with non-malignant disease after exclusion of artifacts, which constituted 10.4% of areas (indicated as malignant disease). Discrepant results were recorded in 15 (7.9%) men, and in 14 of these cases, HGPIN and areas suggestive of early invasion were common. Thus, it was uncertain whether these cases should be regarded as malignant or not. The agreement between the visual examination and the computer analysis was 92.1% (kappa value 0.823, sensitivity 99.2 and specificity was 0.80). <strong>Conclusion:</strong> It seems that computer analysis could serve as an adjunct to simplify and shorten the diagnostic procedure, first of all by ensuring that normal prostate biopsies are sorted out from those sent for visual microscopic evaluation.
文摘Application of the computer image analysis for improving microbial viability assessment by plate count and fluorescence microscopy was investigated. Yeast cells were used as a model microorganism. The application of the improved methods for the viability assessment of yeast cells after preservation by freezing and freeze-drying was demonstrated.
基金This project was supported by the National Natural Science Foundation of China (60135020).
文摘The flexibility of traditional image processing system is limited because those system are designed for specific applications. In this paper, a new TMS320C64x-based multi-DSP parallel computing architecture is presented. It has many promising characteristics such as powerful computing capability, broad I/O bandwidth, topology flexibility, and expansibility. The parallel system performance is evaluated by practical experiment.
基金supported by the National 863 Foundation under grant 863-2.5.1.25.
文摘The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.
