| 1. |
- Olsson, Ella, et al.
(författare)
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Graph-Based Knowledge Representation and Algorithms for Air and Maintenance Operations
- 2022
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Ingår i: ICAS Proceedings 33rd Congress of the International Council of the Aeronautical Sciences, Stockholm, Sweden. - : International Council of the Aeronautical Sciences. - 9781713871163
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Konferensbidrag (refereegranskat)abstract
- This work presents an approach for information exchange between adjacent air operations domains by means of graph technologies. The approach has the ability to leverage interoperability and collaboration between air- and ground-based systems and stakeholders in respective domains. In its foundation, it provides a means for relevant actors to access and assess relevant data, information and knowledge, and thus provide input in terms of viable action alternatives in a complex and dynamic operational context. As a proof-of-concept, we have utilizeda full-stack application framework to implement a decision support demonstrator for operational aircraft maintenance. Our solution facilitates a lightweight and dynamic representation of relevant domain knowledge,readily available for exploitation by graph algorithms, adapted to our domain. We have based our implementation on the full-stack application framework Grand-Stack, which is an architecture designed to exploit the power of graphs throughout its stack.
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| 2. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Analysis of Breakdown Reports Using Natural Language Processing and Machine Learning
- 2022
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Ingår i: Lecture Notes in Mechanical Engineering. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030936389 ; , s. 40-52
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Konferensbidrag (refereegranskat)abstract
- Proactive maintenance management of world-class standard is close to impossible without the support of a computerized management system. In order to reduce failures, and failure recurrence, the key information to log are failure causes. However, Computerized Maintenance Management System (CMMS) seems to be scarcely used for analysis for improvement initiatives. One part of this is due to the fact that many CMMS utilizes free-text fields which may be difficult to analyze statistically. The aim of this study is to apply Natural Language Processing (NPL), Ontology and Machine Learning (ML) as a means to analyze free-textual information from a CMMS. Through the initial steps of the study, it was concluded though that none of these methods were able to find any suitable hidden patterns with high-performance accuracy that could be related to recurring failures and their root causes. The main reason behind that was that the free-textual information was too unstructured, in terms of for instance: spelling- and grammar mistakes and use of slang. That is the quality of the data are not suitable for the analysis. However, several improvement potentials in reporting and to develop the CMMS further could be provided to the company so that they in the future more easily will be able to analyze its maintenance data.
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| 3. |
- Barua, Shaibal, et al.
(författare)
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Automated EEG Artifact Handling with Application in Driver Monitoring
- 2017
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers Inc.. - 2168-2194 .- 2168-2208.
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Tidskriftsartikel (refereegranskat)abstract
- Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a pre-processing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.
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| 4. |
- Barua, Shaibal, 1982-
(författare)
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Multivariate Data Analytics to Identify Driver’s Sleepiness, Cognitive load, and Stress
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Driving a vehicle in a dynamic traffic environment requires continuous adaptation of a complex manifold of physiological and cognitive activities. Impaired driving due to, for example, sleepiness, inattention, cognitive load or stress, affects one’s ability to adapt, predict and react to upcoming traffic events. In fact, human error has been found to be a contributing factor in more than 90% of traffic crashes. Unfortunately, there is no robust, objective ground truth for determining a driver’s state, and researchers often revert to using subjective self-rating scales when assessing level of sleepiness, cognitive load or stress. Thus, the development of better tools to understand, measure and monitor human behaviour across diverse scenarios and states is crucial. The main objective of this thesis is to develop objective measures of sleepiness, cognitive load and stress, which can later be used as research tools, either to benchmark unobtrusive sensor solutions or when investigating the influence of other factors on sleepiness, cognitive load, and stress.This thesis employs multivariate data analysis using machine learning to detect and classify different driver states based on physiological data. The reason for using rather intrusive sensor data, such as electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), skin conductance, finger temperature, and respiration is that these methods can be used to analyse how the brain and body respond to internal and external changes, including those that do not generate overt behaviour. Moreover, the use of physiological data is expected to grow in importance when investigating human behaviour in partially automated vehicles, where active driving is replaced by passive supervision.Physiological data, especially the EEG is sensitive to motion artifacts and noise, and when recorded in naturalistic environments such as driving, artifacts are unavoidable. An automatic EEG artifact handling method ARTE (Automated aRTifacts handling in EEG) was therefore developed. When used as a pre-processing step in the classification of driver sleepiness, ARTE increased classification performance by 5%. ARTE is data-driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered. In addition, several machine-learning algorithms have been developed for sleepiness, cognitive load, and stress classification. Regarding sleepiness classification, the best achieved accuracy was achieved using a Support Vector Machine (SVM) classifier. For multiclass, the obtained accuracy was 79% and for binary class it was 93%. A subject-dependent classification exhibited a 10% improvement in performance compared to the subject-independent classification, suggesting that much can be gained by using personalized classifiers. Moreover, by embedding contextual information, classification performance improves by approximately 5%. In regard to cognitive load classification, a 72% accuracy rate was achieved using a random forest classifier. Combining features from several data sources may improve performance, and indeed, we observed classification performance improvement by 10%-20% compared to using features from a single data source. To classify drivers’ stress, using the Case-based reasoning (CBR) and data fusion approach, the system achieved an 83.33% classification accuracy rate.This thesis work encourages the use of multivariate data for detecting and classifying driver states, including sleepiness, cognitive load, and stress. A univariate data source often presents challenges, since features from a single source or one just aspect of the feature are not entirely reliable; Therefore, multivariate information requires accurate driver state detection. Often, driver states are a subjective experience, in which other contextual data plays a vital role. Thus, the implication of incorporating contextual information in the classification scheme is presented in this thesis work. Although there are several commonalities, physiological signals are modulated differently in different driver states; Hence, multivariate data could help detect multiple driver states simultaneously – for example, cognitive load detection when a person is under the influence of different levels of stress.
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| 5. |
- Bengtsson, Marcus, 1977-, et al.
(författare)
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Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields
- 2024
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Ingår i: Advances in Transdisciplinary Engineering. - : IOS Press BV. - 9781643685106 - 9781643685113 ; , s. 27-38
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Konferensbidrag (refereegranskat)abstract
- Companies must enhance total maintenance effectiveness to stay competitive, focusing on both digitalization and basic maintenance procedures. Digitalization offers technologies for data-driven decision-making, but many maintenance decisions still lack a factual basis. Prioritizing efficiency and effectiveness require analyzing equipment history, facilitated by using Computerized Maintenance Management Systems (CMMS). However, CMMS data often contains unstructured free-text, leading to manual analysis, which is resource-intensive and reactive, focusing on short time periods and specific equipment. Two approaches are available to solve the issue: minimizing free-text entries or using advanced methods for processing them. Free-text allows detailed descriptions but may lack completeness, while structured reporting aids automated analysis but may limit fault description richness. As knowledge and experience are vital assets for companies this research uses a hybrid approach by combining Natural Language Processing with domain specific ontology and Large Language Models to extract information from free-text entries, enabling the possibility of real-time analysis e.g., identifying recurring failure and knowledge sharing across global sites.
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| 6. |
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| 7. |
- D'Cruze, Ricky Stanley, et al.
(författare)
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A Case Study on Ontology Development for AI Based Decision Systems in Industry
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 693-706
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Konferensbidrag (refereegranskat)abstract
- Ontology development plays a vital role as it provides a structured way to represent and organize knowledge. It has the potential to connect and integrate data from different sources, enabling a new class of AI-based services and systems such as decision support systems and recommender systems. However, in large manufacturing industries, the development of such ontology can be challenging. This paper presents a use case of an application ontology development based on machine breakdown work orders coming from a Computerized Maintenance Management System (CMMS). Here, the ontology is developed using a Knowledge Meta Process: Methodology for Ontology-based Knowledge Management. This ontology development methodology involves steps such as feasibility study, requirement specification, identifying relevant concepts and relationships, selecting appropriate ontology languages and tools, and evaluating the resulting ontology. Additionally, this ontology is developed using an iterative process and in close collaboration with domain experts, which can help to ensure that the resulting ontology is accurate, complete, and useful for the intended application. The developed ontology can be shared and reused across different AI systems within the organization, facilitating interoperability and collaboration between them. Overall, having a well-defined ontology is critical for enabling AI systems to effectively process and understand information.
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| 8. |
- de Jesus, L. D., et al.
(författare)
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Use of multiple low cost autonomous drones for high voltage line inspection
- 2022
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Ingår i: 33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022. - : International Council of the Aeronautical Sciences. - 9781713871163 ; , s. 5591-5596
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Konferensbidrag (refereegranskat)abstract
- Many high-voltage line monitoring companies have been using drones to carry out preventive inspection. The inspection procedure usually makes use of pilots that control drones which films the transmission lines. Afterwards, the images need to be analyzed in order to detect problems. This work proposes the usage of drones that make autonomous flights to perform the inspection. The proposed approach exploits three drones to inspect each important part of a transmission line, with the first inspecting the wires, the second inspecting the towers and the third checking the safety area under the line. All three drones are controlled by an embedded system with artificial intelligence which is able to identify a potential problem and issue an alert to locations that might present issues. © 2022 ICAS. All Rights Reserved.
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| 9. |
- Funk, Peter, 1957-
(författare)
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Intelligent Human Computer Collaboration (HCC)
- 2002
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Ingår i: Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI). - : Gesellschaft fur Informatik (GI). - 3885793407 ; , s. 35-36
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Konferensbidrag (refereegranskat)abstract
- Claims have been made that more than 50% of companies corporate knowledge is inside peoples heads. Sharing experience and knowledge in companies is largely unsupported. Networks of people have proven to be efficient both within organisations and within groups of people sharing the same profession etc. We propose an approach where experience and knowledge is shared in a dynamic collaboration between humans and computers where the issue is to efficiently capture/share/reuse experience and knowledge. For this we propose the use of a number of different methods and techniques, in particular EM and AI methods and techniques, CBR, collaborative filtering, user modelling...
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| 10. |
- Giacomossi, L., et al.
(författare)
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Cooperative Search and Rescue with Drone Swarm
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 381-393
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Konferensbidrag (refereegranskat)abstract
- Unmanned Aerial Vehicle (UAV) swarms, also known as drone swarms, have been a subject of extensive research due to their potential to enhance monitoring, surveillance, and search missions. Coordinating several drones flying simultaneously presents a challenge in increasing their level of automation and intelligence to improve strategic organization. To address this challenge, we propose a solution that uses hill climbing, potential fields, and search strategies in conjunction with a probability map to coordinate a UAV swarm. The UAVs are autonomous and equipped with distributed intelligence to facilitate a cooperative search application. Our results show the effectiveness of the swarm, indicating that this approach is a promising approach to addressing this problem.
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