Puddling Puddle Welds

2020 ◽  
Author(s):  
Dane Burden ◽  
Nic Roniger ◽  
Matt Romney
Keyword(s):  
Confusion Matrix ◽  
False Negative ◽  
Primary Objective ◽  
Metal Loss ◽  
Final Report ◽  
True Negative ◽  
Multiple Dataset ◽  
Unique Nature ◽  
Advanced Analysis ◽  
Products Pipeline

Abstract Unique characteristics of individual pipelines come from over a century of evolving design, construction, maintenance, regulation and operation. These characteristics are especially true for legacy, pre-regulated pipelines. Due to the unique nature of the threats present on these assets, there is a need for unique inspection technologies and techniques that can increase pipeline integrity. Reconditioned and repaired pipe utilizing puddle weld repairs is one such threat. An advanced analysis was completed on a 10-inch, 68-mile light products pipeline. The pipeline was constructed with reconditioned pipe that was estimated to contain tens of thousands of puddle welds. Historical in-line inspection (ILI) data generally underperformed in classifying and discriminating puddle welds versus metal loss features. The primary objective of this project was to assess the probability of identification (POI) of a multiple dataset ILI tool utilizing multiple magnetic flux leakage (MFL) magnetization directions and residual (RES) magnetization measurements. A secondary objective was to scrutinize data for signs of coincident features. Hydrostatic testing failures showed that puddle welds with porosity and cracking were susceptible to failure and that the identification of these features would be beneficial. Analysis of historical puddle weld investigations and newly completed multiple dataset ILI data revealed strong identification capabilities in the RES dataset. The high-field magnetizations offered secondary confirmation but often saturated out thermal effects or material differences. The final report included over 40,000 identified puddle welds and five classifications for further investigation. Field investigations for 212 features were completed and the results compared to the ILI data to assess performance. A confusion matrix was created for true positive (TP), true negative (TN), false positive (FP) and false negative (FN) conditions. The smallest TP puddle weld dimension was 0.7″ × 0.7″, and the population had a statistical sensitivity value of 98% (132 TP and 3 FP). Three additional anomalies denoted as atypical were also investigated. The ILI signatures at these locations were consistent with previous repairs in which puddle welds with cracking were found and repaired. Two of the three features investigated were found to have cracking. Crack propagation was found to be both axial and non-axial in orientation. The results show that puddle welds can be detected and identified with extremely high accuracy. In addition, the preliminary classification results for atypical puddle welds show a high potential for identifying secondary coincident features. This paper details the stages, deliverables and results from an ILI advanced analysis focused on puddle welds.

scholarly journals Detection of Fracture Bones in X-ray Images Categorization

2020 ◽  
pp. 1-11
Author(s):  
Adigun Oyeranmi ◽  
Babatunde Ronke ◽  
Rufai Mohammed ◽  
Aigbokhan Edwin
Keyword(s):  
Feature Extraction ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
False Negative ◽  
Support Vector ◽  
Kappa Statistics ◽  
Fracture Detection ◽  
True Negative ◽  
Research Attention ◽  
X Ray

Fractured bone detection and categorization is currently receiving research attention in computer aided diagnosis system because of the ease it has brought to doctors in classification and interpretation of X-ray images.  The choice of an efficient algorithm or combination of algorithms is paramount to accurately detect and categorize fractures in X-ray images, which is the first stage of diagnosis in treatment and correction of damaged bones for patients. This is what this research seeks to address. The research design involves data collection, preprocessing, segmentation, feature extraction, classification and evaluation of the proposed method. The sample dataset were x-ray images collected from the Department of Radiology, National Orthopedic Hospital, Igbobi-Lagos, Nigeria as well as Open Access Medical Image Repositories. The image preprocessing involves the conversion of images in RGB format to grayscale, sharpening and smoothing using Unsharp Masking Tool.  The segmentation of the preprocessed image was carried out by adopting the Entropy method in the first stage and Canny edge method in the second stage while feature extraction was performed using Hough Transformation. Detection and classification of fracture image employed a combination of two algorithms;  K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) for detecting fracture locations based on four classification types: (normal, comminute, oblique and transverse).Two performance assessment methods were employed to evaluate the developed system. The first evaluation was based on confusion matrix which evaluates fracture and non-fracture on the basis of TP (True Positive), TN (True negative), FP (False Positive) and FN (False Negative). The second appraisal was based on Kappa Statistics which evaluates the type of fracture by determining the accuracy of the categorized fracture bone type. The result of first assessment for fracture detection shows that 26 out of 40 preprocessed images were fractured, resulting to the following three values of performance metrics: accuracy value of 90%, sensitivity of 87% and specificity of 100%. The Kappa coefficient error assessment produced accuracy of 83% during classification. The proposed method can find suitable use in categorization of fracture types on different bone images based on the results obtained from the experiment.

scholarly journals SPK Penentuan Lokasi ATM Menggunakan Metode AHP dan SAW

2019 ◽  
Vol 5 (1) ◽  
pp. 49-56
Author(s):  
Gede Surya Mahendra ◽  
Kadek Yota Ernanda Aryanto
Keyword(s):  
False Positive ◽  
Confusion Matrix ◽  
False Negative ◽  
True Positive ◽  
True Negative

Persaingan industri perbankan saat ini semakin meningkat, baik dalam hal penyediaan inovasi produk serta peningkatan kualitas transaksi dan pelayanan. Untuk mengatasi masalah tersebut diciptakan sebuah terminal yang dikenal dengan ATM. Namun fungsionalitas dan efektifitas ATM tersebut belum memenuhi kebutuhan nasabah dikarenakan pengambilan keputusan penentuan lokasi ATM belum menggunakan SPK sehingga banyak kriteria yang terlupakan dalam penentuan lokasi ATM terbaik. Metode AHP yang merupakan sebuah hierarki fungsional dengan input utamanya adalah persepsi manusia sedangkan metode SAW dengan konsep dasar mencari penjumlahan terbobot dari rating kinerja pada setiap alternatif pada semua atribut. AHP digunakan untuk memberikan pembobotan pada masing-masing kriteria dan SAW untuk melakukan perangkingan dari masing-masing alternatif. Terdapat 7 kriteria dengan 11 sub kriteria pada pembobotan dan 76 data alternatif. Pengujian dilakukan dengan membandingkan hasil delpoyment ATM dengan hasil perhitungan sistem. Dari 76 data alternatif yang diujikan, terdapat 38 lokasi deployment ATM. Dari hasil pengujian yang ditampilkan dalam confusion matrix, pada kriteria yang tidak teruji signifikansi didapatkan 33 data True Positive, 38 True Negative, 5 False Negative dan 5 False Positive dengan akurasi sebesar 86,84%, dan pada kriteria yang teruji signifikansi didapatkan 35 data True Positive, 35 True Negative, 3 False Negative dan 3 False Positive memiliki akurasi 92,11%.

scholarly journals Classification of Melanoma and Nevus for Diagnosis of Skin Cancer by Using Optimized Convolution Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1984-1989
Author(s):  
Neha Maheshwari
Keyword(s):  
Neural Network ◽  
Median Filter ◽  
Confusion Matrix ◽  
False Negative ◽  
Convolution Neural Network ◽  
True Positive ◽  
True Negative ◽  
Index Terms ◽  
Risk Of Cancer

Abstract: Melanoma is taken into account a fatal sort of carcinoma .Differentiating melanoma from nevus is difficult task. Nevus is a common pigmented skin lesion, usually developing during adulthood, which is harmless. Since they look similar it has to be identified and reduce the risk of cancer. The death rate thanks to this disease is in particular other skin-related consolidated malignancies. In this work, we have used convolution neural networks to classify the image into melanoma and nevus. The images are pre-processed using median filter, top-bottom hat filter and are passed through layers of CNN. We have achieved an accuracy of 97.56%, sensitivity of 95.23%.The F1_socre is 97.56. Index terms: Melanoma, Nevus, True Positive, True Negative, False Negative, False Positive, Confusion Matrix, Epoch, Convolution Neural Network.

scholarly journals A New Image Processing Technique for Medical Image Analysis

2021 ◽  
Vol 9 (8) ◽  
pp. 1806-1813
Author(s):  
Shreyasi Ghosh
Keyword(s):  
Image Processing ◽  
Confusion Matrix ◽  
Medical Image Analysis ◽  
False Negative ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Misclassification Rate ◽  
True Negative ◽  
Sobel Edge Detection ◽  
Edge Detection Method

Abstract: In this communication, we report identification of lung cancer with the help of a new image processing technique. To start with, we compare the results obtained from our proposed method with those obtained from the original Sobel edge detection method. The comparison is done by creating a confusion matrix and considering various parameters such as True Positive, True Negative, False Positive, False Negative, Accuracy, Misclassification Rate, Precision, Recall, Specificity, F Score, Matthews Correlation Coefficient (MCC), F Measurement (FM), Z score, T Score, and P Score. Our proposed method gives better total accuracy than the normal Sobel edge detector. Keywords: Neural network, statistical learning, edge filters, deep learning, image processing

scholarly journals Significance of Pelvic Fluid Observed during Ovarian Cancer Screening with Transvaginal Sonogram

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 144
Author(s):  
Justin W. Gorski ◽  
Charles S. Dietrich ◽  
Caeli Davis ◽  
Lindsay Erol ◽  
Hayley Dietrich ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
False Negative ◽  
Primary Objective ◽  
Control Group ◽  
Free Fluid ◽  
Ovarian Malignancy ◽  
Ovarian Cancer Screening ◽  
True Negative ◽  
Benign Ovarian Tumors

The primary objective was to examine the role of pelvic fluid observed during transvaginal ultrasonography (TVS) in identifying ovarian malignancy. A single-institution, observational study was conducted within the University of Kentucky Ovarian Cancer Screening trial from January 1987 to September 2019. We analyzed true-positive (TP), false-positive (FP), true-negative (TN), and false-negative (FN) groups for the presence of pelvic fluid during screening encounters. Measured outcomes were the presence and duration of fluid over successive screening encounters. Of the 48,925 women surveyed, 2001 (4.1%) had pelvic fluid present during a TVS exam. The odds ratio (OR) of detecting fluid in the comparison group (TN screen; OR = 1) significantly differed from that of the FP cases (benign pathology; OR: 13.4; 95% confidence interval (CI): 9.1–19.8), the TP cases with a low malignant potential (LMP; OR: 28; 95% CI: 26.5–29.5), TP ovarian cancer cases (OR: 50.4; 95% CI: 27.2–93.2), and FN ovarian cancer cases (OR: 59.3; 95% CI: 19.7–178.1). The mean duration that pelvic fluid was present for women with TN screens was 2.2 ± 0.05 encounters, lasting 38.7 ± 1.3 months. In an asymptomatic screening population, free fluid identified in TVS exams was more associated with ovarian malignancy than in the control group or benign ovarian tumors. While pelvic free fluid may not solely discriminate malignancy from non-malignancy, it appears to be clinically relevant and warrants thoughtful consideration.

The role of serum periostin in the diagnosis of asthma: A meta-analysis

2020 ◽  
Vol 41 (4) ◽  
pp. 240-247
Author(s):  
Lei Yang ◽  
Qingtao Zhao ◽  
Shuyu Wang
Keyword(s):  
Diagnostic Accuracy ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
False Negative ◽  
Summary Receiver Operating Characteristic ◽  
True Negative ◽  
Negative Results ◽  
Noninvasive Biomarker ◽  
Sensitivity Specificity ◽  
Serum Periostin

Background: Serum periostin has been proposed as a noninvasive biomarker for asthma diagnosis and management. However, its accuracy for the diagnosis of asthma in different populations is not completely clear. Methods: This meta-analysis aimed to evaluate the diagnostic accuracy of periostin level in the clinical determination of asthma. Several medical literature data bases were searched for relevant studies through December 1, 2019. The numbers of patients with true-positive, false-positive, false-negative, and true-negative results for the periostin level were extracted from each individual study. We assessed the risk of bias by using Quality Assessment of Diagnostic Accuracy Studies 2. We used the meta-analysis to produce summary estimates of accuracy. Results: In total, nine studies with 1757 subjects met the inclusion criteria. The pooled estimates of sensitivity, specificity, and diagnostic odds ratios for the detection of asthma were 0.58 (95% confidence interval [CI], 0.38‐0.76), 0.86 (95% CI, 0.74‐0.93), and 8.28 (95% CI, 3.67‐18.68), respectively. The area under the summary receiver operating characteristic curve was 0.82 (95% CI, 0.79‐0.85). And significant publication bias was found in this meta‐analysis (p = 0.39). Conclusion: Serum periostin may be used for the diagnosis of asthma, with moderate diagnostic accuracy.

scholarly journals A multicenter, hospital-based and non-inferiority study for diagnostic efficacy of automated whole breast ultrasound for breast cancer in China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yujing Xin ◽  
Xinyuan Zhang ◽  
Yi Yang ◽  
Yi Chen ◽  
Yanan Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
False Negative ◽  
Final Analysis ◽  
Age Group ◽  
Surgical Biopsy ◽  
True Negative ◽  
Diagnostic Efficacy ◽  
Negative Results ◽  
Informed Consent Form ◽  
Magnetic Resonance Imaging Mri

AbstractThis study is the first multi-center non-inferiority study that aims to critically evaluate the effectiveness of HHUS/ABUS in China breast cancer detection. This was a multicenter hospital-based study. Five hospitals participated in this study. Women (30–69 years old) with defined criteria were invited for breast examination by HHUS, ABUS or/and mammography. For BI-RADS category 3, an additional magnetic resonance imaging (MRI) test was provided to distinguish the true negative results from false negative results. For women classified as BI-RADS category 4 or 5, either core aspiration biopsy or surgical biopsy was done to confirm the diagnosis. Between February 2016 and March 2017, 2844 women signed the informed consent form, and 1947 of them involved in final analysis (680 were 30 to 39 years old, 1267 were 40 to 69 years old).For all participants, ABUS sensitivity (91.81%) compared with HHUS sensitivity (94.70%) with non-inferior Z tests, P = 0.015. In the 40–69 age group, non-inferior Z tests showed that ABUS sensitivity (93.01%) was non-inferior to MG sensitivity (86.02%) with P < 0.001 and HHUS sensitivity (95.44%) was non-inferior to MG sensitivity (86.02%) with P < 0.001. Sensitivity of ABUS and HHUS are all superior to that of MG with P < 0.001 by superior test.For all participants, ABUS specificity (92.89%) was non-inferior to HHUS specificity (89.36%) with P < 0.001. Superiority test show that specificity of ABUS was superior to that of HHUS with P < 0.001. In the 40–69 age group, ABUS specificity (92.86%) was non-inferior to MG specificity (91.68%) with P < 0.001 and HHUS specificity (89.55%) was non-inferior to MG specificity (91.68%) with P < 0.001. ABUS is not superior to MG with P = 0.114 by superior test. The sensitivity of ABUS/HHUS is superior to that of MG. The specificity of ABUS/HHUS is non-inferior to that of MG. In China, for an experienced US radiologist, both HHUS and ABUS have better diagnostic efficacy than MG in symptomatic individuals.

scholarly journals 18F-Fluciclovine Positron Emission Tomography in Prostate Cancer: A Systematic Review and Diagnostic Meta-Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 304
Author(s):  
Giuseppina Biscontini ◽  
Cinzia Romagnolo ◽  
Chiara Cottignoli ◽  
Andrea Palucci ◽  
Fabio Massimo Fringuelli ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computed Tomography ◽  
Positron Emission Tomography ◽  
Meta Analysis ◽  
False Negative ◽  
Final Analysis ◽  
Emission Tomography ◽  
True Negative ◽  
Positron Emission ◽  
Pet Ct

Background: to explore the diagnostic accuracy of 18F-Fluciclovine positron-emission tomography (PET) in prostate cancer (PCa), considering both primary staging prior to radical therapy, biochemical recurrence, and advanced setting. Methods: A systematic web search through Embase and Medline was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Studies performed from 2011 to 2020 were evaluated. The terms used were “PET” or “positron emission tomography” or “positron emission tomography/computed tomography” or “PET/CT” or “positron emission tomography-computed tomography” or “PET-CT” and “Fluciclovine” or “FACBC” and “prostatic neoplasms” or “prostate cancer” or “prostate carcinoma”. Only studies reporting about true positive (TP), true negative (TN), false positive (FP) and false negative (FN) findings of 18F-fluciclovine PET were considered eligible. Results: Fifteen out of 283 studies, and 697 patients, were included in the final analysis. The pooled sensitivity for 18F-Fluciclovine PET/CT for diagnosis of primary PCa was 0.83 (95% CI: 0.80–0.86), the specificity of 0.77 (95% CI: 0.74–0.80). The pooled sensitivity for preoperative LN staging was 0.57 (95% CI: 0.39–0.73) and specificity of 0.99 (95% CI: 0.94–1.00). The pooled sensitivity for the overall detection of recurrence in relapsed patients was 0.68 (95% CI: 0.63–0.73), and specificity of 0.68 (95% CI: 0.60–0.75). Conclusion: This meta-analysis showed promising results in term of sensitivity and specificity for 18F-Fluciclovine PET/CT to stage the primary lesion and in the assessment of nodal metastases, and for the detection of PCa locations in the recurrent setting. However, the limited number of studies and the broad heterogeneity in the selected cohorts and in different investigation protocols are limitation affecting the strength of these results.

scholarly journals Can artificial intelligence reduce the interval cancer rate in mammography screening?

2021 ◽  
Author(s):  
Kristina Lång ◽  
Solveig Hofvind ◽  
Alejandro Rodríguez-Ruiz ◽  
Ingvar Andersson
Keyword(s):  
Artificial Intelligence ◽  
Risk Score ◽  
Mammography Screening ◽  
False Negative ◽  
Interval Cancer ◽  
Stage Iv ◽  
True Negative ◽  
Stage Iv Disease ◽  
Interval Cancer Rate ◽  
Screening Mammograms

Abstract Objectives To investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening. Materials and methods Preceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learning–based AI system. The system assigns a risk score from 1 to 10. Two experienced breast radiologists reviewed and classified the cases in consensus as true negative, minimal signs or false negative and assessed whether the AI system correctly localised the cancer. The potential reduction of interval cancer was calculated at different risk score thresholds corresponding to approximately 10%, 4% and 1% recall rates. Results A statistically significant correlation between interval cancer classification groups and AI risk score was observed (p < .0001). AI scored one in three (143/429) interval cancer with risk score 10, of which 67% (96/143) were either classified as minimal signs or false negative. Of these, 58% (83/143) were correctly located by AI, and could therefore potentially be detected at screening with the aid of AI, resulting in a 19.3% (95% CI 15.9–23.4) reduction of interval cancer. At 4% and 1% recall thresholds, the reduction of interval cancer was 11.2% (95% CI 8.5–14.5) and 4.7% (95% CI 3.0–7.1). The corresponding reduction of interval cancer with grave outcome (women who died or with stage IV disease) at risk score 10 was 23% (8/35; 95% CI 12–39). Conclusion The use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities. Key Points • Retrospective study showed that AI detected 19% of interval cancer at the preceding screening exam that in addition showed at least minimal signs of malignancy. Importantly, these were correctly localised by AI, thus obviating supplementary screening modalities. • AI could potentially reduce a proportion of particularly aggressive interval cancers. • There was a correlation between AI risk score and interval cancer classified as true negative, minimal signs or false negative.

scholarly journals Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3801 ◽  
Author(s):  
Ahmed Raza ◽  
Vladimir Ulansky
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Preventive Maintenance ◽  
False Negative ◽  
Maintenance Cost ◽  
Arbitrary Distribution ◽  
Average Lifetime ◽  
True Negative ◽  
Degradation Failure ◽  
Lifetime Maintenance

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.

Sign in / Sign up

Export Citation Format

Share Document