This article proposes a novel techniques for unsupervised learning in image recognition using automatic fuzzy clustering algorithm (AFCA) for discrete data. There are two main stages in order to recognize images in th...
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Laser micro-drilling is a significant manufacturing method used to drill precise microscopic holes into metals. Quality inspection of micro-holes is costly and redrilling defective holes can lead to imperfection owing...
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Laser micro-drilling is a significant manufacturing method used to drill precise microscopic holes into metals. Quality inspection of micro-holes is costly and redrilling defective holes can lead to imperfection owing to the misalignment in re-aligning the removed specimens. Thus this paper proposes an in-situ, automatic inspection method using photodiode data and machine learning models to detect defects in real-time during the fabrication of SK5 steel plates with 1064 nm Nd:YAG Laser machines to reduce the workload and increase the quality of products. Further, it explores the possibility of generalizing the models to 51 different scenarios of fabrication by classifying unseen data into 51 classes. A dataset of around 1,500,000 time series data points was generated using an optical probe while drilling over 56,000 holes into test specimens. 15 different combinations of thickness and diameter were drilled using suggested parameters. An additional 12 potential defect-prone conditions were designed to obtain data during conditional drilling. Hole quality was measured for each hole using OGP 3D profile microscope measuring machine. Results showed high accuracy in specialized defect detection within each scenario and showed a possibility of classifying photodiode data patterns, offering opportunities to improve the practicality of the proposed solution.
Cluster analysis is a crucial issue in multivariate statistics and data science due to its application in various fields. This study proposes a cutting-edge clustering algorithm for image data with important improveme...
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Cluster analysis is a crucial issue in multivariate statistics and data science due to its application in various fields. This study proposes a cutting-edge clustering algorithm for image data with important improvements. First, we extract the texture features from each image and represent them as two-dimensional intervals, which serve as effective input data for recognizing similarities between images. Subsequently, we introduce a measure called overlap distance for evaluating the similarity between intervals in multi-dimensional cases. Furthermore, the study develops an automatic fuzzy clustering algorithm specifically designed for images. This algorithm addresses multiple challenges simultaneously, including determining the appropriate number of clusters, identifying specific elements within clusters, and estimating the probability of each element belonging to clusters. In addition, the study implements a Matlab program to test the effectiveness and practical applications of the proposed algorithm. The results obtained from the proposed algorithm outperform those of current methods when applied to datasets with variations in the number of elements, fields, and characteristics of images.
The purpose of the study was to evaluate the accuracy of a commercial automatic digital templating algorithm compared to manual digital templating in total knee arthroplasty (TKA). The study also evaluated if race and...
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The purpose of the study was to evaluate the accuracy of a commercial automatic digital templating algorithm compared to manual digital templating in total knee arthroplasty (TKA). The study also evaluated if race and the presence of a standardized calibration marker on preoperative radiographs effect the accuracy of digital templating. One hundred twenty-five consecutive patients undergoing primary TKA were included in the study. Patient demographics, etiology of arthritis, and the presence of a standardized calibration marker on preoperative anteroposterior (AP) and lateral radiographs was recorded. Manual digital templating and the use of the "auto-knee" templating algorithm with "Traumacad" software was performed and recorded. Intraoperative sizes of the actual implants used were recorded. Pearson chi 2 test was used to evaluate the accuracy of auto versus manual templating. Manual templating was within 1 size of the implant used intraoperatively for femoral and tibial implants 97.6% and 94.2% of the time, respectively. The "auto-knee" algorithm was within one size of the implant used for femoral and tibial implants 51.2% and 71.2% of the time, respectively. The presence of a standardized calibration marker on the AP view did not change accuracy of templating for both components. There was no difference in accuracy of templating between races. We caution surgeons from exclusively using an automatic algorithm as it is less accurate than manual templating for TKA.
Background Accurate identification of vascular lumen region founded the base of bubble detection and bubble grading, which played a significant role in the detection of vascular gas emboli for the diagnosis of decompr...
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Background Accurate identification of vascular lumen region founded the base of bubble detection and bubble grading, which played a significant role in the detection of vascular gas emboli for the diagnosis of decompression *** To assist in the detection of vascular bubbles, it is crucial to develop an automatic algorithm that could identify vascular lumen areas in ultrasound videos with the interference of bubble *** This article proposed an automated vascular lumen region recognition (VLRR) algorithm that could sketch the accurate boundary between vessel lumen and tissues from dynamic 2D ultrasound videos. It adopts 2D ultrasound videos of the lumen area as input and outputs the frames with circled vascular lumen boundary of the videos. Normalized cross-correlation method, distance transform technique, and region growing technique were adopted in this *** A double-blind test was carried out to test the recognition accuracy of the algorithm on 180 samples in the images of 6 different grades of bubble videos, during which, intersection over union and pixel accuracy were adopted as evaluation metrics. The average IOU on the images of different bubble grades reached 0.76. The mean PA on 6 of the images of bubble grades reached *** It is concluded that the proposed method could identify the vascular lumen with high accuracy, potentially applicable to assist clinicians in the measurement of the severity of vascular gas emboli in clinics.
The 3D laser scanning has increasingly been applied in the field of monitoring roadway deformation. Different from a terrestrial laser scanner (TLS), a mobile laser scanner (MLS) is more flexible in on-site operations...
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The 3D laser scanning has increasingly been applied in the field of monitoring roadway deformation. Different from a terrestrial laser scanner (TLS), a mobile laser scanner (MLS) is more flexible in on-site operations, but its scanning range accuracy is only precise to the centimeter level. still remains in centimeters. In addition, extracting deformation information from point cloud data of roadway is a complex process. In order to improve the accuracy of deformation monitoring and the efficiency of point cloud processing, with the aid of the identification plate for measurement (IPM) and the statistical analysis methods, an MLS-based high-accuracy measurement and automatic analysis method of roadway deformation is proposed. In this approach, the center point of the identification plate for measurement (IPM-CP) is taken as the control point for deformation monitoring (CPDM). The IPMs in the point cloud are positioned based on the locations of the seed points. The K-radius search algorithm and the density-based spatial clustering of applications with noise (DBSCAN) algorithm are introduced to extract and segment the point cloud of the target area (PCTA). The calculation methods for the key parameters of the algorithms are developed. The judgement index for identifying the IPM point cloud and the methods for target point extraction are presented. The convergence features of the surrounding rocks is analyzed by comparing the lengths of measuring lines of monitoring sections at different stages. The method was applied and validated in the Sanshandao Gold Mine for roadway deformation monitoring. The results show that this method is able to automatically obtain a highly precise data on roadway deformation.
The development of high-fidelity in silico simulations of the endovascular thrombectomy (EVT) procedure, the treatment for acute ischemic stroke, is gaining importance for the possibility of investigating the causes o...
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The development of high-fidelity in silico simulations of the endovascular thrombectomy (EVT) procedure, the treatment for acute ischemic stroke, is gaining importance for the possibility of investigating the causes of failure of the clinical procedure and optimizing the treatment. This work proposed a novel methodology for a realistic modeling of the thrombus aspiration in a combined EVT procedure, with stent-retriever and proximal aspiration catheter. In the combined EVT procedure, the thrombus is entrapped in the stent struts and is retrieved towards the aspiration catheter. During the retrieval, the thrombus may rotate, and therefore different portions of its surface are subjected to aspiration forces. An automatic algorithm is implemented allowing to redefine the portion of the thrombus surface subjected to the aspiration pressure in different time points of a high-fidelity finite-element EVT simulation. The algorithm updates at each iteration the loaded portion of thrombus surface, by selecting elements aligned with the vascular centerline. The algorithm is applied in a high-fidelity EVT simulation in a patient-like vascular model, demonstrating the ability of obtaining a realistic simulation of thrombus aspiration.
Understanding long-term underwater sandbars dynamics is a key element for optimal coastal zone management, but this endeavour is often held back by data availability. In order to increase the spatial and temporal reso...
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Understanding long-term underwater sandbars dynamics is a key element for optimal coastal zone management, but this endeavour is often held back by data availability. In order to increase the spatial and temporal resolution, availability and quality of sandbars observations, this study proposes and evaluates a new approach for automatic extraction of sandbars positions from freely available satellite imagery, using a case study of complex waters located on the Danube Delta coast, Black Sea. Validation of the satellite extracted sandbars locations against 4 in-situ measurements between 2016 and 2018 shows a very good correlation between the two datasets, demonstrating the high accuracy of satellite-derived data in revealing the underwater morphology of the upper shoreface along large areas and with high temporal resolution. The obtained sandbars positions were then integrated into an automatic workflow able to accurately represent the seasonal and multi-annual sandbars dynamics and also to give an insight about the sandbars alongshore behavioural changes. The results are similar to the ones previously suggested by other studies, which empowered different methods. These prove the high potential of the newly proposed algorithm to be used for the automatic extraction of the positions of underwater features in the surf zone on other sites worldwide, after proper tuning of parameters in accordance with local conditions. The main constraints of the proposed approach are represented by the omission of sandbars position that imprint a low signal on the satellite image due to increased water depth (especially for the outer bar), high turbidity (mostly for the inner bar) or any other noise generation situation (sun glint, small wind waves). The main advantages of the described algorithm are related to the simplicity of implementation and adaptability to other areas of interest and other types of satellite data.
Landslide profile has been widely used in the dynamics analysis, characteristics illustration and parameter measurement along the longitudinal direction of landslide. However, landslide profile and landslide character...
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Landslide profile has been widely used in the dynamics analysis, characteristics illustration and parameter measurement along the longitudinal direction of landslide. However, landslide profile and landslide characteristic parameters that can be derived from landslide profile have not been formally included in landslide inventories. This paper firstly presented a newly developed algorithm for automatic generation of landslide profile, and then suggested 14 parameters that can be directly derived from landslide profile to characterize landslides. The proposed approach has been coded in a freely available R script called ALPA. A case study showed that the profiles of landslides with various scales and morphologies can be successfully generated by ALPA. A correlation analysis suggested that the 14 parameters can provide independent and informative complements to landslide characterization, which are different from the landslide area and volume. The results also implied that landslide area, which quantifies the total area affected by a landslide, is highly correlated with the product of volume and maximum drop height and the product of volume and maximum residual height, which are related to destructive power. Although limited by some complications, ALPA provides a practical tool for producing landslide profiles and landslide characteristic parameters, which are valuable complements to landslide inventory.
The right panel drawing direction is an important prerequisite for generating qualified parts, an important step before the panel forming simulation is to determine the reasonable direction of the drawing. Manually ad...
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ISBN:
(纸本)9783037856949
The right panel drawing direction is an important prerequisite for generating qualified parts, an important step before the panel forming simulation is to determine the reasonable direction of the drawing. Manually adjust parts in order to overcome rely on experience, the drawbacks to the drawing direction, the direction of the drawing punch and forming the contact area of the sheet as the goal of automatic determination algorithm. Objective function of the direction of the drawing for the variable contact area in the drawing direction of the feasible region, the use of heritage algorithms to optimize the objective function of the contact area and, ultimately feasible within the contact area corresponding to the drawing direction, that is the best drawing direction. The measured results show that the direction of the drawing based on genetic algorithm, the automatic algorithm can fast and accurate to obtain the optimal direction of drawing.
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