Dull Bit Grading Using Video Intelligence

2021 ◽  
Author(s):  
Merit P. Ekeregbe ◽  
Mina S. Khalaf ◽  
Robello Samuel
Keyword(s):  
Contextual Information ◽  
Subjective Evaluation ◽  
Petroleum Industry ◽  
Medical Image Segmentation ◽  
Supervised Machine Learning ◽  
Processing Unit ◽  
Video Analytics ◽  
Research Areas ◽  
Drill Bits ◽  
Video Model

Abstract Although visual data analytics using image processing is one of the most growing research areas today and is largely applied in many fields, it is not fully utilized in the petroleum industry. This study is inspired by medical image segmentation in detecting tumor cells. This paper uses a supervised Machine Learning technique through video analytics to identify bit dullness that can be used in the drilling industry in place of the subjective screening approach. The evaluation of bit performance can be affected by subjective evaluation of the degree of dullness. The present approach of using video analytics is able to grade bit dullness to avoid user subjectivity. The approach involves the use of datasets in good quantity and quality by separating them into training datasets, testing datasets, and validation datasets. Due to the large datasets, Google Collaboratory was used as it provides access to its Graphic Processing Unit (GPU) online for the processing of the bit datasets. The processing time and resource consumption are minimized using Google GPU. Using the Google GPU resources, the procedure is automated without any installation. After the bit is pulled out and cleaned, a video is taken around and up and down in 360°. Further, it is compared against the green bit. By this approach, multiple video datasets are not required. The algorithm was validated with new sets of bit videos and the results were satisfactory. The identification of the dullness or otherwise of each screened bit is done with the aid of a bounding box with a stamp of a level of confidence (range 0.5–1) and the algorithm assigns for its decision on the identified or screened object. This method is also able to screen multiple bits stored in a single place. In an event where several drill bits are to be screened, manual grading will be a huge task and will require a lot of resources. This model and algorithm will take a few minutes to screen and provide grading for several bits while videos are passed through the algorithm. It has also been found that the grading with the video was much better than the single image as the contextual information extracted are much higher at the level of the entire video, per segment, per shot, and per frame. Also, methodology is made robust so that the video model test starts successfully without error. The time penalty for the processing is fast and it took less time for a single video screening. The work developed here is probably the first to handle the dull bit grading using video analytics. With more of these datasets available, the future automation of the IADC bit characterization will soon evolve into an automated process.

scholarly journals Cybersickness in current-generation virtual reality head-mounted displays: systematic review and outlook

2021 ◽  
Author(s):  
Polona Caserman ◽  
Augusto Garcia-Agundez ◽  
Alvar Gámez Zerban ◽  
Stefan Göbel
Keyword(s):  
Systematic Review ◽  
Virtual Reality ◽  
Meta Analysis ◽  
Subjective Evaluation ◽  
Future Research ◽  
Current Generation ◽  
Research Areas ◽  
Head Mounted Displays ◽  
Skin Response ◽  
Golden Standard

AbstractCybersickness (CS) is a term used to refer to symptoms, such as nausea, headache, and dizziness that users experience during or after virtual reality immersion. Initially discovered in flight simulators, commercial virtual reality (VR) head-mounted displays (HMD) of the current generation also seem to cause CS, albeit in a different manner and severity. The goal of this work is to summarize recent literature on CS with modern HMDs, to determine the specificities and profile of immersive VR-caused CS, and to provide an outlook for future research areas. A systematic review was performed on the databases IEEE Xplore, PubMed, ACM, and Scopus from 2013 to 2019 and 49 publications were selected. A summarized text states how different VR HMDs impact CS, how the nature of movement in VR HMDs contributes to CS, and how we can use biosensors to detect CS. The results of the meta-analysis show that although current-generation VR HMDs cause significantly less CS ($$p<0.001$$ p < 0.001 ), some symptoms remain as intense. Further results show that the nature of movement and, in particular, sensory mismatch as well as perceived motion have been the leading cause of CS. We suggest an outlook on future research, including the use of galvanic skin response to evaluate CS in combination with the golden standard (Simulator Sickness Questionnaire, SSQ) as well as an update on the subjective evaluation scores of the SSQ.

scholarly journals ASCU-Net: Attention Gate, Spatial and Channel Attention U-Net for Skin Lesion Segmentation

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 501
Author(s):  
Xiaozhong Tong ◽  
Junyu Wei ◽  
Bei Sun ◽  
Shaojing Su ◽  
Zhen Zuo ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Contextual Information ◽  
Skin Lesions ◽  
Medical Image Segmentation ◽  
Learning Networks ◽  
Wide Range ◽  
Accurate Performance ◽  
Relevant Field ◽  
Skin Smooth ◽  
Smooth Transitions

Segmentation of skin lesions is a challenging task because of the wide range of skin lesion shapes, sizes, colors, and texture types. In the past few years, deep learning networks such as U-Net have been successfully applied to medical image segmentation and exhibited faster and more accurate performance. In this paper, we propose an extended version of U-Net for the segmentation of skin lesions using the concept of the triple attention mechanism. We first selected regions using attention coefficients computed by the attention gate and contextual information. Second, a dual attention decoding module consisting of spatial attention and channel attention was used to capture the spatial correlation between features and improve segmentation performance. The combination of the three attentional mechanisms helped the network to focus on a more relevant field of view of the target. The proposed model was evaluated using three datasets, ISIC-2016, ISIC-2017, and PH2. The experimental results demonstrated the effectiveness of our method with strong robustness to the presence of irregular borders, lesion and skin smooth transitions, noise, and artifacts.

scholarly journals Lost in Space: Geolocation in Event Data

2018 ◽  
Vol 7 (04) ◽  
pp. 871-888 ◽  
Author(s):  
Sophie J. Lee ◽  
Howard Liu ◽  
Michael D. Ward
Keyword(s):  
Learning Algorithm ◽  
Text Processing ◽  
Contextual Information ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Model Parameters ◽  
Event Data ◽  
Data Set ◽  
N Gram ◽  
Automated Text Processing

Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine-learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training data set and use this model to predict whether a location word in the test data set accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.

scholarly journals MF-Net: Multi-Scale Information Fusion Network for CNV Segmentation in Retinal OCT Images

2021 ◽  
Vol 15 ◽  
Author(s):  
Qingquan Meng ◽  
Lianyu Wang ◽  
Tingting Wang ◽  
Meng Wang ◽  
Weifang Zhu ◽  
...  
Keyword(s):  
Information Fusion ◽  
Data Augmentation ◽  
Contextual Information ◽  
Treatment Plan ◽  
Speckle Noise ◽  
Medical Image Segmentation ◽  
Unlabeled Data ◽  
Feature Maps ◽  
Multi Scale ◽  
Segmentation Accuracy

Choroid neovascularization (CNV) is one of the blinding ophthalmologic diseases. It is mainly caused by new blood vessels growing in choroid and penetrating Bruch's membrane. Accurate segmentation of CNV is essential for ophthalmologists to analyze the condition of the patient and specify treatment plan. Although many deep learning-based methods have achieved promising results in many medical image segmentation tasks, CNV segmentation in retinal optical coherence tomography (OCT) images is still very challenging as the blur boundary of CNV, large morphological differences, speckle noise, and other similar diseases interference. In addition, the lack of pixel-level annotation data is also one of the factors that affect the further improvement of CNV segmentation accuracy. To improve the accuracy of CNV segmentation, a novel multi-scale information fusion network (MF-Net) based on U-Shape architecture is proposed for CNV segmentation in retinal OCT images. A novel multi-scale adaptive-aware deformation module (MAD) is designed and inserted into the top of the encoder path, aiming at guiding the model to focus on multi-scale deformation of the targets, and aggregates the contextual information. Meanwhile, to improve the ability of the network to learn to supplement low-level local high-resolution semantic information to high-level feature maps, a novel semantics-details aggregation module (SDA) between encoder and decoder is proposed. In addition, to leverage unlabeled data to further improve the CNV segmentation, a semi-supervised version of MF-Net is designed based on pseudo-label data augmentation strategy, which can leverage unlabeled data to further improve CNV segmentation accuracy. Finally, comprehensive experiments are conducted to validate the performance of the proposed MF-Net and SemiMF-Net. The experiment results show that both proposed MF-Net and SemiMF-Net outperforms other state-of-the-art algorithms.

scholarly journals Enhancing U-Net with Spatial-Channel Attention Gate for Abnormal Tissue Segmentation in Medical Imaging

2020 ◽  
Vol 10 (17) ◽  
pp. 5729
Author(s):  
Trinh Le Ba Khanh ◽  
Duy-Phuong Dao ◽  
Ngoc-Huynh Ho ◽  
Hyung-Jeong Yang ◽  
Eu-Tteum Baek ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Multiple Scales ◽  
Contextual Information ◽  
Poor Performance ◽  
Medical Image Segmentation ◽  
Tissue Segmentation ◽  
Low Level ◽  
Computational Overhead ◽  
Segmentation Task

In recent years, deep learning has dominated medical image segmentation. Encoder-decoder architectures, such as U-Net, can be used in state-of-the-art models with powerful designs that are achieved by implementing skip connections that propagate local information from an encoder path to a decoder path to retrieve detailed spatial information lost by pooling operations. Despite their effectiveness for segmentation, these naïve skip connections still have some disadvantages. First, multi-scale skip connections tend to use unnecessary information and computational sources, where likable low-level encoder features are repeatedly used at multiple scales. Second, the contextual information of the low-level encoder feature is insufficient, leading to poor performance for pixel-wise recognition when concatenating with the corresponding high-level decoder feature. In this study, we propose a novel spatial-channel attention gate that addresses the limitations of plain skip connections. This can be easily integrated into an encoder-decoder network to effectively improve the performance of the image segmentation task. Comprehensive results reveal that our spatial-channel attention gate remarkably enhances the segmentation capability of the U-Net architecture with a minimal computational overhead added. The experimental results show that our proposed method outperforms the conventional deep networks in term of Dice score, which achieves 71.72%.

scholarly journals Accelerating 3D Medical Image Segmentation by Adaptive Small-Scale Target Localization

2021 ◽  
Vol 7 (2) ◽  
pp. 35
Author(s):  
Boris Shirokikh ◽  
Alexey Shevtsov ◽  
Alexandra Dalechina ◽  
Egor Krivov ◽  
Valery Kostjuchenko ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Graphics Processing Units ◽  
Medical Image ◽  
State Of The Art ◽  
Three Dimensional ◽  
Medical Image Segmentation ◽  
Small Scale ◽  
Processing Unit ◽  
Central Processing ◽  
Lung Cancer Diagnosis

The prevailing approach for three-dimensional (3D) medical image segmentation is to use convolutional networks. Recently, deep learning methods have achieved human-level performance in several important applied problems, such as volumetry for lung-cancer diagnosis or delineation for radiation therapy planning. However, state-of-the-art architectures, such as U-Net and DeepMedic, are computationally heavy and require workstations accelerated with graphics processing units for fast inference. However, scarce research has been conducted concerning enabling fast central processing unit computations for such networks. Our paper fills this gap. We propose a new segmentation method with a human-like technique to segment a 3D study. First, we analyze the image at a small scale to identify areas of interest and then process only relevant feature-map patches. Our method not only reduces the inference time from 10 min to 15 s but also preserves state-of-the-art segmentation quality, as we illustrate in the set of experiments with two large datasets.

scholarly journals Image-Label Recovery on Fashion Data Using Image Similarity from Triple Siamese Network

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Debapriya Banerjee ◽  
Maria Kyrarini ◽  
Won Hwa Kim
Keyword(s):  
State Of The Art ◽  
Metric Learning ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Research Areas ◽  
Complete Dataset ◽  
Data Points ◽  
Graph Neural Networks ◽  
Few Data ◽  
Weakly Labeled Data

Weakly labeled data are inevitable in various research areas in artificial intelligence (AI) where one has a modicum of knowledge about the complete dataset. One of the reasons for weakly labeled data in AI is insufficient accurately labeled data. Strict privacy control or accidental loss may also cause missing-data problems. However, supervised machine learning (ML) requires accurately labeled data in order to successfully solve a problem. Data labeling is difficult and time-consuming as it requires manual work, perfect results, and sometimes human experts to be involved (e.g., medical labeled data). In contrast, unlabeled data are inexpensive and easily available. Due to there not being enough labeled training data, researchers sometimes only obtain one or few data points per category or label. Training a supervised ML model from the small set of labeled data is a challenging task. The objective of this research is to recover missing labels from the dataset using state-of-the-art ML techniques using a semisupervised ML approach. In this work, a novel convolutional neural network-based framework is trained with a few instances of a class to perform metric learning. The dataset is then converted into a graph signal, which is recovered using a recover algorithm (RA) in graph Fourier transform. The proposed approach was evaluated on a Fashion dataset for accuracy and precision and performed significantly better than graph neural networks and other state-of-the-art methods.

scholarly journals Medical Image Segmentation Using a Combination of Lattice Boltzmann Method and Fuzzy Clustering Based on GPU CUDA Parallel Processing

2021 ◽  
Vol 17 (11) ◽  
pp. 76
Author(s):  
Ignasius Boli Suban ◽  
Suyoto Suyoto ◽  
Pranowo Pranowo
Keyword(s):  
Image Segmentation ◽  
Parallel Processing ◽  
Level Set ◽  
Fuzzy Clustering ◽  
Lattice Boltzmann ◽  
Rapid Development ◽  
Medical Image Segmentation ◽  
Processing Unit ◽  
Level Set Function ◽  
Set Function

The rapid development of computer technology has had a significant influence on advances in medical science. This development concerns segmenting medical images that can be used to help doctors diagnose patient diseases. The boundary between objects contained in an image is captured using the level set function. The equation of the level set function is solved numerically by combining the Lattice Boltzmann (LBM) method and fuzzy clustering. Parallel processing using a graphical processing unit (GPU) accelerates the execution of the segmentation process. The results showed that image segmentation with a relatively large size could be done quickly. The use of parallel programming with the GPU can accelerate up to 39.22 times compared to the speed of serial programming with the CPU. In addition, the comparisons with other research and benchmark data show consistent results.

Narrative Discourse in Young Children With Histories of Early Corrective Heart Surgery

2002 ◽  
Vol 45 (2) ◽  
pp. 318-331 ◽  
Author(s):  
Lowry Hemphill ◽  
Paola Uccelli ◽  
Kendra Winner ◽  
Chien-ju Chang ◽  
David Bellinger
Keyword(s):  
Personal Experience ◽  
Heart Surgery ◽  
Contextual Information ◽  
Subjective Evaluation ◽  
Narrative Discourse ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Information Reporting ◽  
Personal Experience Narratives ◽  
Narrative Performance

Narrative attainment was assessed in a group of 76 four-year-old children at risk for brain injury because of histories of early corrective heart surgery. Elicited personal experience narratives were coded for narrative components, evaluative devices, and information adequacy and were contrasted with narratives produced by a comparison group of typically developing 4-year-olds. The production of autonomous narrative discourse was identified as an area of special vulnerability for children with this medical history. Despite considerable heterogeneity in narrative performance, children with early corrective heart surgery produced fewer narrative components than typically developing children. Results suggest that the elaboration of events and contextual information, the expression of subjective evaluation and causality, and clarity and explicitness of information reporting may constitute special challenges for this population of children. Implications of these findings for clinical assessment and possible risks for socioemotional relationships and academic achievement are discussed.

scholarly journals Optimizing the ultimate recovery by infill drilling using streamline simulation

2019 ◽  
Vol 42 ◽  
pp. e45293
Author(s):  
Keyvan Dailami ◽  
Hamid Reza Nasriani ◽  
Seyed Adib Sajjadi ◽  
Mohammad Rafie Rafiee ◽  
Justin Whitty ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Petroleum Industry ◽  
Field Performance ◽  
Oil Field ◽  
Recovery Factor ◽  
Processing Unit ◽  
Streamline Simulation ◽  
Simulation And Optimization ◽  
Central Processing ◽  
The Impact

Even though numerical simulators that use the finite difference approach to model the oil and gas fields and to forecast the field performance are popular in the petroleum industry, they suffer from a very long central processing unit (CPU) time in the complex reservoirs with large number of grids. This issue could be resolved by streamline simulation and it could significantly decrease the runtime. This work explains the the streamline simulation concept and then a real oil field is studied using this technique, the streamline simulation is conducted by a commercial simulator, i.e., FrontSim streamline simulator and then the model was analyzed to find the optimum location of infill wells. In this work, 34 different cases were studied using Streamline simulation method and FrontSim software by considering different arrangement of infill wells. It was concluded that a significant enhancement in the ultimate recovery factor of the oil reservoir could be attained by considering different arrangement of the infill horizontal and vertical wells. It was highlighted that the ultimate recovery factor could be increased significantly, i.e., 13%. Additionally the water cut of the field could be reduced significantly. The novelty of this work is to capture the impact of both vertical and horizontal wells on the ultimate recovery enhancement simultaneously using the concept of streamline simulation and optimization of the field performance using streamline simulation concept.

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