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Relevant bibliographies by topics / Automated Bug Detection

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Academic literature on the topic 'Automated Bug Detection'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Automated Bug Detection"

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Emeto, I. C., U. W. Anthony, A. A. Galadima, et al. "A WEB-BASED AUTOMATED BUG DETECTION AND FIX SUGGESTION SYSTEM." International Journal of Computer Science and Mobile Computing 14, no. 3 (2025): 28–38. https://doi.org/10.47760/ijcsmc.2025.v14i03.004.

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Software bugs are a persistent and costly challenge in software development, with organizations spending billions annually on debugging efforts. Traditional bug detection systems are often manual, resulting in inefficiencies and delayed resolutions. This research introduces a web-based automated bug detection and fix suggestion system that addresses these limitations. The system utilizes a dynamic database, Python-based automation, and a recommender engine to detect bugs in real time and propose context-aware fixes. Key innovations include a user-friendly reporting interface, integration of hi

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K S, Ms Surabhi. "Real Time Bug Detection Using Machine Learning Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42112.

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Real-time bug detection in software systems is a critical aspect of software quality assurance. Traditional debugging techniques often fail to scale with the increasing complexity of software. Machine learning (ML) offers a promising solution for automated and real-time bug detection. This paper explores various machine learning algorithms used for bug detection, their implementation in real-time systems using Python, and the challenges faced. We also discuss performance evaluation metrics and future directions in the field.

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ZOU, Jie, Ling XU, Mengning YANG, Xiaohong ZHANG, Jun ZENG, and Sachio HIROKAWA. "Automated Duplicate Bug Report Detection Using Multi-Factor Analysis." IEICE Transactions on Information and Systems E99.D, no. 7 (2016): 1762–75. http://dx.doi.org/10.1587/transinf.2016edp7052.

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Chen, Mengzhuo, Zhe Liu, Chunyang Chen, et al. "Standing on the Shoulders of Giants: Bug-Aware Automated GUI Testing via Retrieval Augmentation." Proceedings of the ACM on Software Engineering 2, FSE (2025): 825–46. https://doi.org/10.1145/3715755.

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In software development, similar apps often encounter similar bugs due to shared functionalities and implementation methods. However, current automated GUI testing methods mainly focus on generating test scripts to cover more pages by analyzing the internal structure of the app, without targeted exploration of paths that may trigger bugs, resulting in low efficiency in bug discovery. Considering that a large number of bug reports on open source platforms can provide external knowledge for testing, this paper proposes BugHunter, a novel bug-aware automated GUI testing approach that generates ex

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Nam, Seong-Guk, and Yeong-Seok Seo. "GUI Component Detection-Based Automated Software Crash Diagnosis." Electronics 12, no. 11 (2023): 2382. http://dx.doi.org/10.3390/electronics12112382.

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This study presents an automated software crash-diagnosis technique using a state transition graph (STG) based on GUI-component detection. An STG is a graph representation of the state changes in an application that are caused by actions that are executed in the GUI, which avoids redundant test cases and generates bug-reproduction scenarios. The proposed technique configures the software application STG using computer vision and artificial intelligence technologies and performs automated GUI testing without human intervention. Four experiments were conducted to evaluate the performance of the

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Ali, Waqas, Saima siraj Soomro, Shamshad Lakho, Nadeem Naeem Bhatti, and Imran Ali Memon. "Adaptive Bug Localization Framework for Precision-Driven Bug Localization in Software Engineering." VFAST Transactions on Software Engineering 12, no. 3 (2024): 230–42. http://dx.doi.org/10.21015/vtse.v12i3.1832.

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Software development always looks for automated methods to improve productivity and accuracy in issue detection. The paper conducts a comparative examination of several machine-learning techniques to tackle the bug localization difficulty. Our study compared the performance of Logistic Regression (LR), Random Forest Classifier (RFC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), and Adaptive Bug Localization System (ABLS) on five dataset versions. The results demonstrate the superior performance of ensemble learning methods. The ABLS model regularly beats other models regar

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Fezzardi, Pietro, Fabrizio Ferrandi, and Christian Pilato. "Enabling Automated Bug Detection for IP-Based Designs Using High-Level Synthesis." IEEE Design & Test 35, no. 5 (2018): 54–62. http://dx.doi.org/10.1109/mdat.2018.2824121.

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Fezzardi, Pietro, and Fabrizio Ferrandi. "Automated Bug Detection for High-level Synthesis of Multi-threaded Irregular Applications." ACM Transactions on Parallel Computing 7, no. 4 (2020): 1–26. http://dx.doi.org/10.1145/3418086.

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Rathnasuriya, Ravishka, Nidhi Majoju, Zihe Song, and Wei Yang. "An Investigation on Numerical Bugs in GPU Programs Towards Automated Bug Detection." Proceedings of the ACM on Software Engineering 2, ISSTA (2025): 1654–77. https://doi.org/10.1145/3728950.

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General-purpose graphics processing unit (GPU) computing has emerged as a leading parallel computing paradigm, offering significant performance gains in various domains such as scientific computing and deep learning. However, GPU programs are susceptible to numerical bugs, which can lead to incorrect results or crashes. These bugs are difficult to detect, debug, and fix due to their dependence on specific input values or types and the absence of reliable error-checking mechanisms and oracles. Additionally, the unique programming conventions of GPUs complicate identifying the root causes of bug

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Arivoli, Anbarasu. "AI FOR AUTOMATED BUG DETECTION AND DEBUGGING: A COMPARATIVE STUDY OF CURRENT APPROACHES." International Journal of Business Quantitative Economics and Applied Management Research 7, no. 12, 2024 (2024): 57–66. https://doi.org/10.5281/zenodo.15236719.

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This paper explores the use of artificial intelligence, particularly machine learning and deep learning, in enhancing software debugging. It compares traditional methods with AI-driven approaches like reinforcement learning, highlighting their adaptability, efficiency, and scalability in large-scale systems. The study also discusses challenges such as computational costs and model interpretability while proposing optimizations for real-time debugging. It envisions AI as a powerful support tool that improves defect detection and resolution, ultimately enabling faster, more secure, and reliable

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Dissertations / Theses on the topic "Automated Bug Detection"

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Azzam, Yves Said. "PathCase SB: Automating Performance Monitoring And Bugs Detection." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1334589632.

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Ibrar, Fahad. "Strip-Miner: Automatic Bug Detection in Large Software Code with Low False Positive Rate." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/97934.

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There are a number of techniques for automatic bug detection, most of them have a high false positive rate when used in practice. This work proposes an approach, named Strip-Miner, that combines simple dependency analysis of code with a data mining technique "frequent itemset mining" to reduce the false positive rate. We adopt a two phase approach 1) finding the potential bugs and 2) filtering the false positive ones. In the first phase we extract code elements and dependencies among them using static analysis and frequent itemset mining to find programming patterns where a deviation from thes

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Süßkraut, Martin. "Automatic Hardening against Dependability and Security Software Bugs." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-38342.

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It is a fact that software has bugs. These bugs can lead to failures. Especially dependability and security failures are a great threat to software users. This thesis introduces four novel approaches that can be used to automatically harden software at the user's site. Automatic hardening removes bugs from already deployed software. All four approaches are automated, i.e., they require little support from the end-user. However, some support from the software developer is needed for two of these approaches. The presented approaches can be grouped into error toleration and bug removal. The two e

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Bjurenfalk, Jonatan, and August Johnson. "Automated error matching system using machine learning and data clustering : Evaluating unsupervised learning methods for categorizing error types, capturing bugs, and detecting outliers." Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177280.

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For large and complex software systems, it is a time-consuming process to manually inspect error logs produced from the test suites of such systems. Whether it is for identifyingabnormal faults, or finding bugs; it is a process that limits development progress, and requires experience. An automated solution for such processes could potentially lead to efficient fault identification and bug reporting, while also enabling developers to spend more time on improving system functionality. Three unsupervised clustering algorithms are evaluated for the task, HDBSCAN, DBSCAN, and X-Means. In addition,

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MALAVISI, MARZIA. "Structural Health Monitoring Framework for Automatic Damage Detection based on IoT and Big Data Analytics: Application to a network of structures." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2840369.

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Ivarsson, Anton, and Jacob Stachowicz. "Evaluating machine learning methods for detecting sleep arousal." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259996.

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Sleep arousal is a phenomenon that affects the sleep of a large amount of people. The process of predicting and classifying arousal events is done manually with the aid of certified technologists, although some research has been done on automation using Artificial Neural Networks (ANN). This study explored how a Support Vector Machine performed(SVM) compared to an ANN on this task. Polysomnography (PSG) is a sort of sleep study which produces the data that is used in classifying sleep disorders. The PSG-data used in this thesis consists of 13 wave forms sampled at or resampled at 200Hz. There

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Skogsmo, Markus. "A Scalable Approach for Detecting Dumpsites using Automatic Target Recognition with Feature Selection and SVM through Satellite Imagery." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-418792.

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Throughout the world, there is a great demand to map out the increasing environmental changes and life habitats on Earth. The vast majority of Earth Observations today, are collected using satellites. The Global Watch Center (GWC) initiative was started with the purpose of producing a global situational awareness of the premises for all life on Earth. By collecting, studying and analyzing vast amounts of data in an automatic, scalable and transparent way, the GWC aims are to work towards reaching the United Nations (UN) Sustainable Development Goals (SDG). The GWC vision is to make use of qual

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Kang, Li. "Automated Duplicate Bug Reports Detection - An Experiment at Axis Communication AB." Thesis, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15399.

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Context. Bug tracking systems play an important role in software maintenance. They allow users to submit bug reports. However, it has been observed that often a bug report submitted is a duplicate (when several users submit bug reports for the same problem, these reports are called duplicated issue reports) which results in considerable duplicate bug reports in a bug tracking system. Solutions for automating the process of duplicate bug reports detection can increase the productivity of software maintenance activities, as new incoming bug reports are directly compared with the existing bug rep

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Zhang, Lei. "DASE: Document-Assisted Symbolic Execution for Improving Automated Test Generation." Thesis, 2014. http://hdl.handle.net/10012/8532.

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Software testing is crucial for uncovering software defects and ensuring software reliability. Symbolic execution has been utilized for automatic test generation to improve testing effectiveness. However, existing test generation techniques based on symbolic execution fail to take full advantage of programs’ rich amount of documentation specifying their input constraints, which can further enhance the effectiveness of test generation. In this paper we present a general approach, Document-Assisted Symbolic Execution (DASE), to improve automated test generation and bug detection. DASE leverag

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Ade-Ibijola, Abejide Olu. "Automatic novice program comprehension for semantic bug detection." Thesis, 2016. http://hdl.handle.net/10539/21019.

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A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the Degree of Doctor of Philosophy in Computer Science April 2016<br>Automatically comprehending novice programs with the aim of giving useful feedback has been an Artificial Intelligence problem for over four decades. Solving this problem basically entails manipulating the underlying program plans; i.e. extracting and comparing the novice's plan to the expert's plan and inferring where the novice's bug is from. The bugs of interest in this domain are often s

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Books on the topic "Automated Bug Detection"

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Hagan, John, Bill McCarthy, and Daniel Herda. Chicago's Reckoning. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780197627860.001.0001.

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Chicago is confronting a racial reckoning that we explain with an exclusion-containment theory of legal cynicism. Mayors RJ and RM Daley used public and private funds to exclude and contain South and West Side predominantly Black neighborhoods where Police Detective Jon Burge supervised torture of over 100 Black men. A 1982 case involved Andrew Wilson’s tortured confession to two police killings. This case coincided with RM Daley’s pursuit of White votes in an early and unsuccessful primary campaign for mayor. Suspicions about Daley’s connection to Wilson’s confession lasted throughout his car

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Hilgurt, S. Ya, and O. A. Chemerys. Reconfigurable signature-based information security tools of computer systems. PH “Akademperiodyka”, 2022. http://dx.doi.org/10.15407/akademperiodyka.458.297.

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The book is devoted to the research and development of methods for combining computational structures for reconfigurable signature-based information protection tools for computer systems and networks in order to increase their efficiency. Network security tools based, among others, on such AI-based approaches as deep neural networking, despite the great progress shown in recent years, still suffer from nonzero recognition error probability. Even a low probability of such an error in a critical infrastructure can be disastrous. Therefore, signature-based recognition methods with their theoretic

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Book chapters on the topic "Automated Bug Detection"

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Manthey, Norbert, and Marius Lindauer. "SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers." In Theory and Applications of Satisfiability Testing – SAT 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40970-2_36.

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Parízek, Pavel. "BUBEN: Automated Library Abstractions Enabling Scalable Bug Detection for Large Programs with I/O and Complex Environment." In Automated Technology for Verification and Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31784-3_13.

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Hoyos, Isaias, Bruno Esposito, and Miguel Nunez-del-Prado. "DETECTOR: Automatic Detection System for Terrorist Attack Trajectories." In Information Management and Big Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11680-4_17.

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Liblit, Ben. "Automated Detection and Repair of Concurrency Bugs." In Hardware and Software: Verification and Testing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34188-5_3.

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Koprivec, Filip, Gregor Kržmanc, Maja Škrjanc, Klemen Kenda, and Erik Novak. "Screening Tool for Anti-money Laundering Supervision." In Big Data and Artificial Intelligence in Digital Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-94590-9_13.

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AbstractEfficient screening of transactions provides an empowering tool for anti-money laundering procedures and actions. Automatic classification and detection of anomalous behaviours and transaction structures enable faster and more effective action on the side of the supervisory authority. This chapter introduces research achievements and tools developed to streamline transaction monitoring and ease domain experts with automatic and semi-automatic filtering of risky transaction typologies. Presented tools are integrated as part of PAMLS (Platform for Anti-Money Laundering Supervision) to st

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Liu, Lianye, Jinping Liu, Juanjuan Wu, Jiaming Zhou, and Meiling Cai. "Novelty Detection-Based Automated Anomaly Identification via Optimized Deep Generative Model." In Big Data. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9709-8_9.

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van Ditmarsch, Hans, Krisztina Fruzsa, Roman Kuznets, and Ulrich Schmid. "A Logic for Repair and State Recovery in Byzantine Fault-Tolerant Multi-agent Systems." In Automated Reasoning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63501-4_7.

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AbstractWe provide novel epistemic logical language and semantics for modeling and analysis of byzantine fault-tolerant multi-agent systems, with the intent of not only facilitating reasoning about the agents’ fault status but also supporting model updates for repair and state recovery. Besides the standard knowledge modalities, our logic provides additional agent-specific hope modalities capable of expressing that an agent is not faulty, and also dynamic modalities enabling change to the agents’ correctness status. These dynamic modalities are interpreted as model updates that come in three f

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Ferrara, Matteo, and Annalisa Franco. "Morph Creation and Vulnerability of Face Recognition Systems to Morphing." In Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_6.

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AbstractFace recognition in controlled environments is nowadays considered rather reliable, and very good accuracy levels can be achieved by state-of-the-art systems in controlled scenarios. However, even under these desirable conditions, digital image alterations can severely affect the recognition performance. In particular, several studies show that automatic face recognition systems are very sensitive to the so-called face morphing attack, where face images of two individuals are mixed to produce a new face image containing facial features of both subjects. Face morphing represents nowaday

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Hasan, Mohammad Asif, Hasan Sarker, and Md Omaer Faruq Goni. "Explainable Automated Brain Tumor Detection Using CNN." In Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8937-9_33.

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Catucci, Antonella, Alessia Tricomi, Laura De Vendictis, Savvas Rogotis, and Nikolaos Marianos. "Farm Weather Insurance Assessment." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_19.

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AbstractThe pilot aimed to develop services supporting both the risk and the damage assessment in the agro-insurance domain. It is based on the use of remotely sensed data, integrated with meteorological data, and adopts machine learning and artificial intelligence tools. Netherlands and Greece have been selected as pilot areas . In the Netherlands, the pilot was focused on potato crops for the identification of areas with higher risk, based on the historical analysis of heavy rains. In addition, it covered automated detection of potato parcels with anomalous behaviours (damage assessment) fro

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Conference papers on the topic "Automated Bug Detection"

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Sheng Xuan, Phang Justin, and Yunli Lee. "Automated Code Review and Bug Detection." In 2024 19th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). IEEE, 2024. https://doi.org/10.1109/isai-nlp64410.2024.10799479.

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Zhang, Lei, and Andriy Miranskyy. "Automated Flakiness Detection in Quantum Software Bug Reports." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10274.

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Coutinho, Joana, Alexandre Lemos, Miguel Terra-Neves, et al. "BugOut: Automated Test Generation and Bug Detection for Low-Code." In 2024 IEEE Conference on Software Testing, Verification and Validation (ICST). IEEE, 2024. http://dx.doi.org/10.1109/icst60714.2024.00041.

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Bindu Nalluri, Venkata Madhu, Parvathraj K. M M, Y. Nagendar, Kalaivani E, Prabhu A E, and Athiraja Atheeswaran. "Enhancing Software Debugging Processes Through Machine Learning-Driven Automated Bug Detection." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026231.

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Gaddamwar, Kushal, Yatharth Srivastava, Jyoti Parashar, Ritwij Aryan Parmar, Aditya Kumar Pandey, and Virendra Singh Kushwah. "Deep Learning for Contextual Bug Detection and Automated Fixes in Software Systems." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912101.

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Nwasra, Nedal, Jamal Zaraqu, and Zahid Hussain Qaisar. "Improving Software Reliability Through Bug Detection and Automated Error Repair Using Transformer-Based Models." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013645.

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Eid, Maged, and Giancarlo Dalle Ave. "Automated Interpretation of Chemical Engineering Diagrams Using Computer Vision." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.129214.

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This paper presents state-of-the-art object detection and object identification algorithms for digitizing and interpreting chemical engineering diagrams, including, Block Flow Diagrams (BFDs), Process Flow Diagrams (PFDs), and Piping and Instrumentation Diagrams (P&IDs), using computer vision techniques. These diagrams are essential for visualizing plant processes and equipment but are often stored as image-based PDFs, making manual digitization/interpretation labor-intensive and error-prone. The proposed algorithm automates tasks such as detecting unit operations and identifying them usin

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Jin, Claire, Sudha Rao, Xiangyu Peng, et al. "Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.907.

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Khatri, Vikramajeet, Mehrnoosh Monshizadeh, Sina Hojjatinia, Siwar Kriaa, and Petri Mähönen. "Automated Data Correlation for IoT Anomaly Detection with B5G Networks." In 2024 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2024. http://dx.doi.org/10.23919/softcom62040.2024.10721776.

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Kale, Vivek, Hanru Yan, Shyamali Mukherjee, et al. "Toward Automated Detection of Portability Bugs in Kokkos Parallel Programs." In SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2024. https://doi.org/10.1109/scw63240.2024.00029.

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Reports on the topic "Automated Bug Detection"

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Price, Donald. SM-403-148100-R01 Mineral Wells 2012 RAM Gas and Oil Leak Detection Field Study Results. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010851.

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In June 2012, the RAM Program conducted a comprehensive field study to evaluate the performance of current off the shelf sensor technologies for detecting gas and oil leaks on pipelines. This study had three key objectives: 1. Evaluate the capabilities of current technologies that are offered commercially for methane leak detection monitoring using standard pipeline patrol aircraft 2. Provide a test location for development of emerging technologies that are not yet commercially available for pipeline leak detection 3. Assess the feasibility of using airborne sensors to detect staged liquid oil

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Bell, Jack, Rik Law, Howell Li, Ben Anderson, and Darcy M. Bullock. New Opportunities for Automated Pedestrian Performance Measures. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317351.

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Pedestrian safety is an important concern when evaluating intersections. Previous literature has shown that exclusive pedestrian phases improve safety, but at the expense of imposing greater pedestrian and motorist delay. However, outside of crash data, there are no easily implementable performance measures for pedestrians at traffic signals. This study proposes two performance metrics: (1) a time-to-jaywalk measure, and (2) the Conflict Occupancy Ratio (COR) for evaluating concurrent pedestrian signal phasing with turning vehicles. The COR quantifies conflicts between turning vehicles and ped

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Lohn, Andrew, Anna Knack, Ant Burke, and Krystal Jackson. Autonomous Cyber Defense. Center for Security and Emerging Technology, 2023. http://dx.doi.org/10.51593/2022ca007.

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The current AI-for-cybersecurity paradigm focuses on detection using automated tools, but it has largely neglected holistic autonomous cyber defense systems — ones that can act without human tasking. That is poised to change as tools are proliferating for training reinforcement learning-based AI agents to provide broader autonomous cybersecurity capabilities. The resulting agents are still rudimentary and publications are few, but the current barriers are surmountable and effective agents would be a substantial boon to society.

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Christensen, Lance. PR-459-133750-WEB Fast, Accurate, Automated System to Find and Quantify Natural Gas Leaks. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011608.

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Thursday, August 8, 2019 11:00 am ET PRESENTER: Lance Christensen, PhD, NASA Jet Propulsion Laboratory HOST: Francois Rongere, PG and E MODERATOR: Carrie Greaney, PRCI CLICK BUY/DOWNLOAD TO ACCESS WEBINAR REGISTRATION LINK Join the PRCI Surveillance, Operations and Maintenance Technical Committee as they present research, conducted by NASA Jet Propulsion Laboratory (JPL), related to the Open Path Laser Spectrometer (OPLS). New advances in sensor technology, with high sensitivity towards detecting methane and ethane, present the energy pipeline industry with cost effective ways to improve safet

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Slone, Scott Michael, Marissa Torres, Nathan Lamie, Samantha Cook, and Lee Perren. Automated change detection in ground-penetrating radar using machine learning in R. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49442.

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Ground-penetrating radar (GPR) is a useful technique for subsurface change detection but is limited by the need for a subject matter expert to process and interpret coincident profiles. Use of a machine learning model can automate this process to reduce the need for subject matter expert processing and interpretation. Several machine learning models were investigated for the purpose of comparing coincident GPR profiles. Based on our literature review, a Siamese Twin model using a twinned convolutional network was identified as the optimum choice. Two neural networks were tested for the interna

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Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, 2023. http://dx.doi.org/10.3289/sw_2_2023.

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The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in

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Evans, John T., Greg M. Shaver, Michael A. Mardikes, et al. Autonomous Mower Pilot Project. Purdue University, 2025. https://doi.org/10.5703/1288284317840.

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The Indiana Department of Transportation (INDOT) spends approximately $19 million annually on roadside mowing, which is a process that is time-consuming, costly, and poses safety risks. Automated mowing solutions have the potential to reduce costs and increase safety, but they will need to be extremely well tested before being deployed. In this work, data from current mowing practices was collected using machine mounted cameras, which was used to inform the creation of a digital twin environment that allows for rigorous initial testing of potential autonomous solution with no risk. The digital

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Unknown, Author. L51630 In-Line Detection and Sizing of Stress Corrosion Cracks Using EMAT Ultrasonics. Pipeline Research Council International, Inc. (PRCI), 1990. http://dx.doi.org/10.55274/r0010616.

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The development of stress corrosion cracks (SCC) in buried gas pipelineshas posed a serious threat to pipeline integrity for many years. It can be reliably detected by magnetic particle techniques in the field or by laboratory studies using low frequency eddy currents. It is also possible to find and measure the depths of the cracks from the ID by careful scanning with an ultra-sonic angle beam probe but the transducer must be manipulated by a skilled operator. All of these approaches are not very satisfactory for in-line inspections because they are not suitable for covering the total area of

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Tao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir, and Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7594391.bard.

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Extending the previous two years of research results (Mizarch, et al, 2012, Tao, 2011, 2012), the third year’s efforts in both Maryland and Israel were directed towards the engineering of the system. The activities included the robust chick handling and its conveyor system development, optical system improvement, online dynamic motion imaging of chicks, multi-image sequence optimal feather extraction and detection, and pattern recognition. Mechanical System Engineering The third model of the mechanical chick handling system with high-speed imaging system was built as shown in Fig. 1. This syst

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Naqvi, Qaim, Patrick Wolff, Brenda Molano-Flores, and Jinelle Sperry. Camera traps are an effective tool for monitoring insect–plant interactions. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48496.

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Insect and pollinator populations are vitally important to the health of ecosystems, food production, and economic stability, but are declining worldwide. New, cheap, and simple monitoring methods are necessary to inform management actions and should be available to researchers around the world. Here, we evaluate the efficacy of a commercially available, close-focus automated camera trap to monitor insect–plant interactions and insect behavior. We compared two video settings—scheduled and motion-activated—to a traditional human observation method. Our results show that camera traps with schedu

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