Relevant bibliographies by topics / Alarm classification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Alarm classification'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Alarm classification"

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Zhao, Boxu, and Guiming Luo. "Classification and Visualization of Alarm Data Based on Heterogeneous Distance." International Journal of Data Warehousing and Mining 14, no. 2 (2018): 60–80. http://dx.doi.org/10.4018/ijdwm.2018040104.

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Alarm classification and visualization of historical data is significant and sophisticated in the area of smart management in telecom network due to alarm flood and propagation. In this article, we propose a heterogeneous distance to compute the similarity distance matrix of alarms, which is applied to alarm classification. By using Multidimensional Scaling, alarm data in high dimension is translated into a 2-dimensional graph in alarm windows. Then alarm attention and relation are clearly shown by comparing current and past alarms. Experiments show MDS based on the heterogeneous distance has
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Lucke, Matthieu, Moncef Chioua, Chriss Grimholt, Martin Hollender, and Nina F. Thornhill. "Online Alarm Flood Classification Using Alarm Coactivations." IFAC-PapersOnLine 51, no. 18 (2018): 345–50. http://dx.doi.org/10.1016/j.ifacol.2018.09.324.

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Tejaswi, Ms G., Rodda Bhavani, Sarvepalli Srihitha, Shaik Arshiha, and Raja Venkata Sathya Sarayu. "PREDICTING FIRE ALARMS USING MULTI SENSOR DATA: A BINARY CLASSIFICATION APPROACH." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 15, no. 1 (2024): 242–55. http://dx.doi.org/10.61841/turcomat.v15i1.14617.

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Fires pose significant threats to human life, property, and the environment. Early detection of fire incidents is crucial to prevent extensive damage and to ensure the safety of occupants. Traditional fire alarm systems typically rely on a single type of sensor, such as smoke detectors or heat sensors, to detect specific fire indicators. These systems operate based on predefined thresholds and triggers. However, they can be prone to false alarms triggered by non-fire-related events (e.g., cooking fumes or dust) and may not provide early warning signs in certain scenarios. To address these limi
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ASALOMIA, LAURENŢIU-BOGDAN, GHEORGHE SAMOILESCU, and MARIUS-IULIAN MIHĂILESCU. "AN ADVANCED AI-BASED SYSTEM FOR INTELLIGENT BRIDGE ALARM MONITORING ON MARITIME VESSELS." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 70, no. 2 (2024): 223–28. https://doi.org/10.59277/rrst-ee.2025.2.12.

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Effective alarm monitoring systems are crucial within the maritime sector to ensure safety and operational continuity. Artificial intelligence is one of the most popular fields nowadays and is often used for machine learning applications. Utilizing AI-based anomaly detection and predictive analytics to analyze real-time alarm data from the ship’s sensor level, the proposed system enhances situational awareness and reduces false alarms. Therefore, machine learning models are trained on hundreds of thousands of historical alarm patterns to detect potential faults and improve response times. The
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Ling, Chen, Wang Xiaoming, Xue Ping, et al. "Based on Zabbix-Prometheus Group Classification Alarm System." Journal of Physics: Conference Series 2665, no. 1 (2023): 012010. http://dx.doi.org/10.1088/1742-6596/2665/1/012010.

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Abstract The efficient operation of the system is the foundation for improving business level in the rapid development of information technology. Based on the data integration of the Zabbix-Prometheus system, it achieves information data storage, data collection, grouping and classification linkage alarm functions. The Zabbix-Prometheus grouping and classification alarm integration system can effectively solve the problems of a large amount of redundant alarm information, no priority of alarm information, and inability to silence. It has established a multi-platform integrated alarm service sy
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Lucke, Matthieu, Moncef Chioua, Chriss Grimholt, Martin Hollender, and Nina F. Thornhill. "Advances in alarm data analysis with a practical application to online alarm flood classification." Journal of Process Control 79 (July 1, 2019): 56–71. https://doi.org/10.1016/j.jprocont.2019.04.010.

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During an alarm flood, the alarm rate is greater than the operator can effectively manage. Many alarm data analysis methods have been proposed in the literature to mitigate the impact of alarm floods. This paper gives a review of the state of the art in alarm data analysis and aims at structuring the field. A distinction between sequence mining methods that apply to alarm sequences and time series analysis methods that apply to alarm series is suggested. The review highlights that online applications to help the operators during alarm flood episodes have been only treated as sequence mining pr
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Hwang, Euy-Hong, Han-Bit Choi, Su-Bin Jung, Sung-Eun Lee, and Don-Mook Choi. "Development of a Framework for the Efficient Management of Unwanted Fire Alarms in Fire-fighting Facilities." Fire Science and Engineering 36, no. 4 (2022): 29–44. http://dx.doi.org/10.7731/kifse.48650041.

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This study aimed to develop a framework for efficiently managing unwanted fire alarms in fire-fighting facilities. To this end, as a method of analyzing and considering the actual situation, the unwanted fire alarm management framework for firefighting facilities was developed by checking domestic and foreign factor classification and investigating related standards, the domestic unwanted fire alarms-related statistical status, surveys and fields. The main contents of the unwanted fire alarm management framework are composed of basic information and response, space, device, cause and outcome i
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Kasiran, Zolidah, Norkhushaini Awang, and Fatin Nurhanani Rusli. "Mobile Malware Classification." International Journal of Engineering & Technology 7, no. 4.31 (2018): 209–2013. http://dx.doi.org/10.14419/ijet.v7i4.31.23369.

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Android malware is growing in such an exponential pace which lead to the need of an efficient malware intrusion detection technique. The single approach of clustering or classification technique in malware intrusion detection yield to high negative positive alarm rate.. This project had proposed clustering in intrusion detection method using hybrid learning approaches combining K-Means clustering and Naïve Bayes classification had been proposed. The result had shown the improved false rate alarm in malware detection. Â
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Dorgo, Gyula, Ahmet Palazoglu, and Janos Abonyi. "Decision trees for informative process alarm definition and alarm-based fault classification." Process Safety and Environmental Protection 149 (May 2021): 312–24. http://dx.doi.org/10.1016/j.psep.2020.10.024.

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Zafar, Raheel, Shah Zaib, and Muhammad Asif. "False Fire Alarm Detection Using Data Mining Techniques." International Journal of Decision Support System Technology 12, no. 4 (2020): 21–35. http://dx.doi.org/10.4018/ijdsst.2020100102.

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In the era of smart home technology, early warning systems and emergency services are inevitable. To make smart homes safer, early fire alarm systems can play a significant role. Smart homes usually utilize communication, sensors, actuators, and other technologies to provide a safe and smart environment. This research work introduced a model for the fire alarm system and designed a fire alarm detection (FAD) simulator to produce a synthetic dataset. The designed simulator utilizes a variety of sensors (temperature, gas, and humidity) to simulate fire alarm scenarios based on real-world data. T
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Dissertations / Theses on the topic "Alarm classification"

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Wirén, Jens, and Farhad Kimanos. "A Survey & Implementation of Financial Alarm Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142661.

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The goal of this thesis is to find, implement and evaluate a suitable machine learning algorithm to classify and predict true and false alerts using labelled data. Alerts are triggered in the Scila Surveillance software when certain parameters are exceeded in a trade, such as a to big volume over a to small time-span. Financial market operators are nowadays required by law to perform market surveillance and due to the huge amounts of data accumulated, machine learning techniques in general and supervised learning in particular comes as a natural choice. This thesis starts with a survey of exis
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Nechay, Daniel. "Controlling false alarm/discovery rates in online Internet traffic classification." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86579.

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Classifying Internet traffic flows online into applications or broader classes without inspecting the packet payloads or without relying on port numbers has become a necessity for network operators. The operators can use this information to monitor their networks and provide per-class quality of service. There has been a great deal of research done on Internet traffic classification recently and numerous techniques have been proposed. While the current techniques can obtain a high accuracy classifying Internet traffic, providing performance guarantees for particular classes of interest has nev
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Hjelmervik, Karl Thomas. "Sonar false alarm rate suppression using classification methods based on acoustic modelling." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14912.

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The use of high–resolution, active sonar systems in littoral environments often results in high false alarm rates. False alarm rate inflation (FARI) and non–Rayleigh reverberation (NRR) are two well–documented causes. FARI may occur when the reverberation in the normaliser window is non–stationary, while NRR may occur when the sonar footprint is too small for the central limit theorem to apply for the scatterer statistics. The main originator for false alarms in littoral environments are either the sea floor itself or objects located on the sea floor. Automatic classification methods may be us
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Chebaro, Omar. "Classification de menaces d’erreurs par analyse statique, simplification syntaxique et test structurel de programmes." Thesis, Besançon, 2011. http://www.theses.fr/2011BESA2021/document.

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La validation des logiciels est une partie cruciale dans le cycle de leur développement. Deux techniques de vérification et de validation se sont démarquées au cours de ces dernières années : l’analyse statique et l’analyse dynamique. Les points forts et faibles des deux techniques sont complémentaires. Nous présentons dans cette thèse une combinaison originale de ces deux techniques. Dans cette combinaison, l’analyse statique signale les instructions risquant de provoquer des erreurs à l’exécution, par des alarmes dont certaines peuvent être de fausses alarmes, puis l’analyse dynamique (génér
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Harbs, Justin, and Jack Svensson. "Automated alarm and root-cause analysis based on real time high-dimensional process data : Part of a joint research project between UmU, Volvo AB & Volvo Cars." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-148511.

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Today, a large amount of raw data are available within manufacturing industries. Unfortunately, most of it is not further analyzed in search of valuable information regarding the optimization of processes. In the painting process at the Volvo plant in Umeå, adjusted settings on the process equipments (e.g. robots, machines etc.) are mostly based on the experience of the personnel rather than actual facts (i.e. analyzed data). Consequently, time- and cost waste caused by defects is obtained when painting the commercial heavy-duty truck bodies (cabs). Hence, the aim of this masters thesis is to
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Widiputri, Diah Indriani. "Incorporating human factors into process plant lifecycle." Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2011. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-74469.

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Major accidents in the process industries occurred mostly as an outcome of multiple failures in different safety barriers and their interrelation with unsafe acts by frontline operators. This has become the reason why safety analyses in terms of plant technical aspects cannot be performed independently from analysing human response to the changing technology. Unsafe acts and errors by operators must be seen as a symptom of system insufficiencies and underlying problems, rather than as the cause of an accident. With this paradigm, the need to optimally configure the system and the whole working
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Chebaro, Omar. "Classification de menaces d'erreurs par analyse statique, simplification syntaxique et test structurel de programmes." Phd thesis, Université de Franche-Comté, 2011. http://tel.archives-ouvertes.fr/tel-00839151.

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La validation des logiciels est une partie cruciale dans le cycle de leur développement. Deux techniques de vérification et de validation se sont démarquées au cours de ces dernières années : l'analyse statique et l'analyse dynamique. Les points forts et faibles des deux techniques sont complémentaires. Nous présentons dans cette thèse une combinaison originale de ces deux techniques. Dans cette combinaison, l'analyse statique signale les instructions risquant de provoquer des erreurs à l'exécution, par des alarmes dont certaines peuvent être de fausses alarmes, puis l'analyse dynamique (génér
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Zarjam, Pega. "EEG Data acquisition and automatic seizure detection using wavelet transforms in the newborn EEG." Queensland University of Technology, 2003. http://eprints.qut.edu.au/15795/.

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This thesis deals with the problem of newborn seizre detection from the Electroencephalogram (EEG) signals. The ultimate goal is to design an automated seizure detection system to assist the medical personnel in timely seizure detection. Seizure detection is vital as neurological diseases or dysfunctions in newborn infants are often first manifested by seizure and prolonged seizures can result in impaired neuro-development or even fatality. The EEG has proved superior to clinical examination of newborns in early detection and prognostication of brain dysfunctions. However, long-term newborn
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Zarjam, Peggy. "EEG Data acquisition and automatic seizure detection using wavelet transforms in the newborn EEG." Thesis, Queensland University of Technology, 2003. https://eprints.qut.edu.au/15795/1/Pega_Zarjam_Thesis.pdf.

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This thesis deals with the problem of newborn seizre detection from the Electroencephalogram (EEG) signals. The ultimate goal is to design an automated seizure detection system to assist the medical personnel in timely seizure detection. Seizure detection is vital as neurological diseases or dysfunctions in newborn infants are often first manifested by seizure and prolonged seizures can result in impaired neuro-development or even fatality. The EEG has proved superior to clinical examination of newborns in early detection and prognostication of brain dysfunctions. However, long-term new
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Christe, Rafael de Oliveira. "Caracterização de populações de mosquitos Aedes fluvitalis (Diptera: Culicidae) em parques municipais da cidade de São Paulo utilizando morfometria geométrica alar." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/99/99131/tde-22032017-082449/.

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Introdução: O mosquito Aedes fluviatilis é um culicídeo encontrado abundantemente pela Cidade de São Paulo, como mostram recentes levantamentos faunísticos em parques municipais. Estas ilhas verdes localizadas na malha urbana da metrópole possuem a capacidade de manter ciclos biológicos de muitos organismos, alguns deles, causadores de doenças. Embora o Aedes fluviatilis não esteja relacionado até o presente momento a doenças que atingem o homem, possui biologia, ecologia, capacidade vetora e padrões populacionais ainda pouco conhecidos. Com a intenção de agregar conhecimento sobre o perfil po
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Books on the topic "Alarm classification"

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Installation and Classification of Residential Burglar Alarm Systems. 4th ed. Underwriters Laboratories, 1999.

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Standard for Safety: Installation and Classification of Residential Burglar Alarm Systems (Ul1641). 2nd ed. Underwriters Laboratories, 1999.

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Book chapters on the topic "Alarm classification"

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Gagnon, François, Frédéric Massicotte, and Babak Esfandiari. "Using Contextual Information for IDS Alarm Classification (Extended Abstract)." In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02918-9_9.

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Shin, Moon Sun, Eun Hee Kim, and Keun Ho Ryu. "False Alarm Classification Model for Network-Based Intrusion Detection System." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_38.

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Peco Chacon, Ana Maria, and Fausto Pedro Garcia Marquez. "Classification of SCADA Alarms and False Alarm Identification Using Support Vector Machine for Wind Turbine Management." In Computational Intelligence, Data Analytics and Applications. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27099-4_41.

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Atwah, Rasha, Rewaa Barakat, Taraf Alsubaie, and Dana Zuhairy. "Smart Classification Recycle Bin with a Reward Point System." In Proceedings in Technology Transfer. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8588-9_18.

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Abstract Improper waste disposal may release toxins, including methane gas, to the environment, potentially contributing to the greenhouse effect. In accordance with the 2030 vision of the Kingdom of Saudi Arabia to produce a sustainable, healthy environment, we designed and implemented a prototype smart recycling bin that effectively classifies and sorts recyclable materials deposited by consumers and provides real-time monitoring of the bin for adverse conditions. Additionally, a reward system is incorporated to incentivize consumers to engage in recycling programs. The Smart Classification
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Arora, Teena, Venki Balasubramanian, Andrew Stranieri, Mai Shenhan, Rajkumar Buyya, and Sardar M.N. Islam. "Classification of Methods to Reduce Clinical Alarm Signals for Remote Patient Monitoring: A Critical Review." In Cloud Computing in Medical Imaging. Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003145189-10.

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Zhao, Zhenmin, Jianghong Li, and Jiahang Cui. "Feature Extraction for Gas Turbine Rotor Fault Diagnosis in Complex Networks Based on Phase Space Method." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-7887-4_94.

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Abstract Aiming at the typical fault diagnosis of gas turbine rotor imbalance, contact-rubbing and misalignment, a feature extraction method for rotor fault diagnosis in complex networks based on phase space method is proposed. The delay parameters and embedding dimension parameters of the phase space method are determined by the autocorrelation function method based on the generalized embedding window length, and then the threshold parameters of the node connection edge are added on the basis of the phase space method to construct the rotor complex network, and the network statistical charact
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Ramirez, Isaac Segovia, and Fausto Pedro García Márquez. "Classification Learner Applied to False Alarms for Wind Turbine Maintenance Management." In International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92905-3_13.

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"Fire Alarm Systems and Automatic Detectors." In Hazardous Area Classification in Petroleum and Chemical Plants. CRC Press, 2013. http://dx.doi.org/10.1201/b16199-7.

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Duan, Xinjie, Adarsh Vasudevan, Ebru Turanoglu Bekar, Kanika Gandhi, and Anders Skoogh. "A Data Scientific Approach Towards Predictive Maintenance Application in Manufacturing Industry." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220148.

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Most industries have recently started to harness the power of data to assess their performance and improve their production systems for future competitiveness and sustainability. Therefore, utilization of data for obtaining insights through data-driven approaches is invading every domain of industrial applications. Predictive maintenance (PdM) is one of the highest impacted industrial use cases in data-driven applications due to its ability to predict machine failures by implementing machine learning algorithms. This study aims to propose a systematic data scientific approach to provide valuab
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Malik, Sapna, and Kiran Khatter. "Malicious Application Detection and Classification System for Android Mobiles." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch008.

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The Android Mobiles constitute a large portion of mobile market which also attracts the malware developer for malicious gains. Every year hundreds of malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, t
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Conference papers on the topic "Alarm classification"

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Xing, Cheng, Chunyu Zhang, Min Zhang, et al. "Alarm Priority Sorting with Multimodal Data Fusion in Optical Networks." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m3h.5.

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An alarm priority sorting scheme using multimodal data fusion is proposed, achieving 95.79% fault-related alarm classification accuracy, which has actual alarm priority ranking result and faster running time than traditional method.
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Shatewakasi, Yeersen, Birgit Vogel-Heuser, Jan Wilch, Victoria Hankemeier, and Josua Höfgen. "Towards NLP-driven Online-classification of Industrial Alarm Messages." In IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2024. https://doi.org/10.1109/iecon55916.2024.10905746.

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Li, YuFang. "Using PCTRAN Nuclear Power Plant Accident Simulation Alarm System with Deep Learning for Alarm Flood Classification and Prognostics and Health Management." In 2025 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 2025. https://doi.org/10.1109/icphm65385.2025.11062031.

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Zou, Peiyuan, Wei Chen, Guohua Fang, Sai Wang, Hongning Yu, and Min Wan. "Alarm Event Classification Based on Edit Distance and Hierarchical Clustering in Electricity Substation." In 2024 4th International Conference on Intelligent Power and Systems (ICIPS). IEEE, 2024. https://doi.org/10.1109/icips64173.2024.10900241.

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Tucker, Brian, and Paula Dempsey. "Health Monitoring Survey of Bell 412EP Transmissions." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11468.

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Health and usage monitoring systems (HUMS) use vibration-based Condition Indicators (CI) to assess the health of helicopter powertrain components. A fault is detected when a CI exceeds its threshold value. The effectiveness of fault detection can be judged on the basis of assessing the condition of actual components from fleet aircraft. The Bell 412 HUMS-equipped helicopter is chosen for such an evaluation. A sample of 20 aircraft included 12 aircraft with confirmed transmission and gearbox faults (detected by CIs) and eight aircraft with no known faults. The associated CI data is classified i
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Miraliakbar, Alireza, and Zheyu Jiang. "Fast, Accurate, and Robust Fault Detection and Diagnosis of Industrial Processes." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.184473.

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Modern industrial processes are continuously monitored by a large number of sensors. Despite having access to large volumes of historical and online sensor data, industrial practitioners still face challenges in the era of Industry 4.0 in effectively utilizing them to perform online process monitoring and fast fault detection and diagnosis. To target these challenges, in this work, we present a novel framework named �FARM� for Fast, Accurate, and Robust online process Monitoring. FARM is a holistic monitoring framework that integrates (a) advanced multivariate statistical process control (SPC)
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Latha, B., S. Mridhula, Aishvarya R, and S. Shreeya Darshna. "Classification of Distress Alarms in Intensive Care Unit Using Neural Network." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866372.

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Tchamova, Albena, and Jean Dezert. "Intelligent alarm classification based on DSmT." In 2012 6th IEEE International Conference Intelligent Systems (IS). IEEE, 2012. http://dx.doi.org/10.1109/is.2012.6335124.

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Timms, C. R. "A methodology for alarm classification and prioritisation." In International Conference on People in Control (Human Interfaces in Control Rooms, Cockpits and Command Centres). IEE, 1999. http://dx.doi.org/10.1049/cp:19990191.

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Martukhovich, Ilya O., and Alexander A. Spector. "Classification of signals for seismic intrusion alarm system." In 2008 9th International Workshop and Tutorials on Electron Devices and Materials. IEEE, 2008. http://dx.doi.org/10.1109/sibedm.2008.4585889.

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Reports on the topic "Alarm classification"

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Rose and Zhao. L52070 Circumferential Guided Wave for Enhanced Defect Detection Classification and Sizing in Piping. Pipeline Research Council International, Inc. (PRCI), 2002. http://dx.doi.org/10.55274/r0011198.

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Because of interest in using EMAT sensors in the PIG for circumferential guided wave defect detection and quantification analysis, this work was undertaken to explore computational modeling aspects for improved sensor design, and data acquisition and analysis. Significant findings were obtained in that a monotonic increase in amplitude could occur with defect depths provided the proper frequency range and mode were utilized. Results show �Lamb type� waves are useful for frequencies under 200 KHz whereas shear longitudinal were potentially useful from 50 KHz to 1 MHz. Results are also reported
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Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45902.

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The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a da
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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavemen
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Cheng, Peng, James V. Krogmeier, Mark R. Bell, Joshua Li, and Guangwei Yang. Detection and Classification of Concrete Patches by Integrating GPR and Surface Imaging. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317320.

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Abstract:
This research considers the detection, location, and classification of patches in concrete and asphalt-on-concrete pavements using data taken from ground penetrating radar (GPR) and the WayLink 3D Imaging System. In particular, the project seeks to develop a patching table for “inverted-T” patches. A number of deep neural net methods were investigated for patch detection from 3D elevation and image observation, but the success was inconclusive, partly because of a dearth of training data. Later, a method based on thresholding IRI values computed on a 12-foot window was used to localize pavemen
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