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Mihailescu, Radu-Casian
Publikasjoner (10 av 28) Visa alla publikasjoner
Gabelaia, D., Kuznetsov, E., Mihailescu, R.-C., Razmadze, K. & Uridia, L. (2024). Temporal logic of surjective bounded morphisms between finite linear processes. Journal of Applied Non-Classical Logics, 34(1), 1-30
Åpne denne publikasjonen i ny fane eller vindu >>Temporal logic of surjective bounded morphisms between finite linear processes
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2024 (engelsk)Inngår i: Journal of Applied Non-Classical Logics, ISSN 1166-3081, E-ISSN 1958-5780, Vol. 34, nr 1, s. 1-30Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In this paper, we study temporal logic for finite linear structures and surjective bounded morphisms between them. We give a characterisation of such structures by modal formulas and show that every pair of linear structures with a bounded morphism between them can be uniquely characterised by a temporal formula up to an isomorphism. As the main result, we prove Kripke completeness of the logic with respect to the class of finite linear structures with bounded morphisms between them. 

sted, utgiver, år, opplag, sider
Taylor & Francis, 2024
Emneord
Temporal logic, modal definability, Kripke completeness
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64269 (URN)10.1080/11663081.2023.2269432 (DOI)2-s2.0-85174929514 (Scopus ID)
Tilgjengelig fra: 2023-12-12 Laget: 2023-12-12 Sist oppdatert: 2024-03-28bibliografisk kontrollert
Abid, M. A., Amouzad Mahdiraji, S., Lorig, F., Holmgren, J., Mihailescu, R.-C. & Petersson, J. (2023). A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment. Paper presented at 27th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES 2023), Athens, Greece, 6-8 September 2023. Procedia Computer Science, 225, 3536-3545
Åpne denne publikasjonen i ny fane eller vindu >>A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment
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2023 (engelsk)Inngår i: Procedia Computer Science, ISSN 1877-0509, Vol. 225, s. 3536-3545Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.

sted, utgiver, år, opplag, sider
Elsevier, 2023
Emneord
genetic algorithm, mobile stroke unit (MSU), optimization, healthcare, time to treatment
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64632 (URN)10.1016/j.procs.2023.10.349 (DOI)
Konferanse
27th International Conference on Knowledge Based and Intelligent Information and Engineering Systems (KES 2023), Athens, Greece, 6-8 September 2023
Forskningsfinansiär
The Kamprad Family Foundation
Tilgjengelig fra: 2023-12-20 Laget: 2023-12-20 Sist oppdatert: 2023-12-20bibliografisk kontrollert
Amouzad Mahdiraji, S., Abid, M. A., Holmgren, J., Mihailescu, R.-C., Lorig, F. & Petersson, J. (2023). An Optimization Model for the Placement of Mobile Stroke Units. In: Teresa Guarda; Filipe Portela; Jose Maria Diaz-Nafria (Ed.), Advanced Research in Technologies, Information, Innovation and Sustainability: Third International Conference, ARTIIS 2023, Madrid, Spain, October 18–20, 2023, Proceedings, Part I. Paper presented at Advanced Research in Technologies, Information, Innovation and Sustainability, Third International Conference, ARTIIS 2023, Madrid, Spain, October 18–20, 2023 (pp. 297-310). Springer
Åpne denne publikasjonen i ny fane eller vindu >>An Optimization Model for the Placement of Mobile Stroke Units
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2023 (engelsk)Inngår i: Advanced Research in Technologies, Information, Innovation and Sustainability: Third International Conference, ARTIIS 2023, Madrid, Spain, October 18–20, 2023, Proceedings, Part I / [ed] Teresa Guarda; Filipe Portela; Jose Maria Diaz-Nafria, Springer, 2023, s. 297-310Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Mobile Stroke Units (MSUs) are specialized ambulances that can diagnose and treat stroke patients; hence, reducing the time to treatment for stroke patients. Optimal placement of MSUs in a geographic region enables to maximize access to treatment for stroke patients. We contribute a mathematical model to optimally place MSUs in a geographic region. The objective function of the model takes the tradeoff perspective, balancing between the efficiency and equity perspectives for the MSU placement. Solving the optimization problem enables to optimize the placement of MSUs for the chosen tradeoff between the efficiency and equity perspectives. We applied the model to the Blekinge and Kronoberg counties of Sweden to illustrate the applicability of our model. The experimental findings show both the correctness of the suggested model and the benefits of placing MSUs in the considered regions.

sted, utgiver, år, opplag, sider
Springer, 2023
Serie
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1935
Emneord
Optimization, MILP, Time to Treatment, Mobile Stroke Unit (MSU), MSU Placement
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-64865 (URN)10.1007/978-3-031-48858-0_24 (DOI)2-s2.0-85180781530 (Scopus ID)978-3-031-48857-3 (ISBN)978-3-031-48858-0 (ISBN)
Konferanse
Advanced Research in Technologies, Information, Innovation and Sustainability, Third International Conference, ARTIIS 2023, Madrid, Spain, October 18–20, 2023
Tilgjengelig fra: 2024-01-08 Laget: 2024-01-08 Sist oppdatert: 2024-01-08bibliografisk kontrollert
Jamali, M., Davidsson, P., Khoshkangini, R., Ljungqvist, M. G. & Mihailescu, R.-C. (2023). Specialized Indoor and Outdoor Scene-specific Object Detection Models. In: SPIE Digital Library: . Paper presented at International Conference on Machine Vision (ICMV 2023), Nov. 15-18, 2023, Yerevan, Armenia.
Åpne denne publikasjonen i ny fane eller vindu >>Specialized Indoor and Outdoor Scene-specific Object Detection Models
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2023 (engelsk)Inngår i: SPIE Digital Library, 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Serie
Studies in Computer Science
Emneord
object detection, YOLOv5, indoor object detection, outdoor object detection, scene classification
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-66441 (URN)
Konferanse
International Conference on Machine Vision (ICMV 2023), Nov. 15-18, 2023, Yerevan, Armenia
Merknad

The paper has not been published yet

Tilgjengelig fra: 2024-03-22 Laget: 2024-03-22 Sist oppdatert: 2024-03-27bibliografisk kontrollert
Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
Åpne denne publikasjonen i ny fane eller vindu >>The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning
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2023 (engelsk)Inngår i: Applied Sciences, E-ISSN 2076-3417, Vol. 13, nr 11, artikkel-id 6516Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed.

sted, utgiver, år, opplag, sider
MDPI, 2023
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-60144 (URN)10.3390/app13116516 (DOI)001004726600001 ()2-s2.0-85163091186 (Scopus ID)
Tilgjengelig fra: 2023-06-07 Laget: 2023-06-07 Sist oppdatert: 2023-09-05bibliografisk kontrollert
Amouzad Mahdiraji, S., Holmgren, J., Alshaban, A., Mihailescu, R.-C., Petersson, J. & Al Fatah, J. (2022). A Framework for Constructing Discrete Event Simulation Models for Emergency Medical Service Policy Analysis. Paper presented at 12th International Conference on Current and Future Trends of Information and Communication Technologies in Health care (ICTH 2022) October 26-28, 2022, Leuven, Belgium. Procedia Computer Science, 210, 133-140
Åpne denne publikasjonen i ny fane eller vindu >>A Framework for Constructing Discrete Event Simulation Models for Emergency Medical Service Policy Analysis
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2022 (engelsk)Inngår i: Procedia Computer Science, E-ISSN 1877-0509, Vol. 210, s. 133-140Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Constructing simulation models can be a complex and time-consuming task, in particular if the models are constructed from scratch or if a general-purpose simulation modeling tool is used. In this paper, we propose a model construction framework, which aims to simplify the process of constructing discrete event simulation models for emergency medical service (EMS) policy analysis. The main building blocks used in the framework are a set of general activities that can be used to represent different EMS care chains modeled as flowcharts. The framework allows to build models only by specifying input data, including demographic and statistical data, and providing a care chain of activities and decisions. In a case study, we evaluated the framework by using it to construct a model for the simulation of the EMS activities related to acute stroke. Our evaluation shows that the predefined activities included in the framework are sufficient to build a simulation model for the rather complex case of acute stroke.

sted, utgiver, år, opplag, sider
Elsevier, 2022
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-56003 (URN)10.1016/j.procs.2022.10.129 (DOI)2-s2.0-85144819456 (Scopus ID)
Konferanse
12th International Conference on Current and Future Trends of Information and Communication Technologies in Health care (ICTH 2022) October 26-28, 2022, Leuven, Belgium
Tilgjengelig fra: 2022-11-14 Laget: 2022-11-14 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Amouzad Mahdiraji, S., Holmgren, J., Mihailescu, R.-C. & Petersson, J. (2022). A Micro-Level Simulation Model for Analyzing the Use of MSUs in Southern Sweden. Paper presented at 11th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH 2021) November 1-4, 2021, Leuven, Belgium. Procedia Computer Science, 198, 132-139
Åpne denne publikasjonen i ny fane eller vindu >>A Micro-Level Simulation Model for Analyzing the Use of MSUs in Southern Sweden
2022 (engelsk)Inngår i: Procedia Computer Science, E-ISSN 1877-0509, Vol. 198, s. 132-139Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A mobile stroke unit (MSU) is a special type of ambulance, where stroke patients can be diagnosed and provided intravenous treatment, hence allowing to cut down the time to treatment for stroke patients. We present a discrete event simulation (DES) model to study the potential benefits of using MSUs in the southern health care region of Sweden (SHR). We included the activities and actions used in the SHR for stroke patient transportation as events in the DES model, and we generated a synthetic set of stroke patients as input for the simulation model. In a scenario study, we compared two scenarios, including three MSUs each, with the current situation, having only regular ambulances. We also performed a sensitivity analysis to further evaluate the presented DES model. For both MSU scenarios, our simulation results indicate that the average time to treatment is expected to decrease for the whole region and for each municipality of SHR. For example, the average time to treatment in the SHR is reduced from 1.31h in the baseline scenario to 1.20h and 1.23h for the two MSU scenarios. In addition, the share of stroke patients who are expected to receive treatment within one hour is increased by a factor of about 3 for both MSU scenarios.

sted, utgiver, år, opplag, sider
Elsevier, 2022
Emneord
Ischemic stroke; stroke transport; MSU; DES; time to treatment; stroke logistics
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-54479 (URN)10.1016/j.procs.2021.12.220 (DOI)2-s2.0-85124617439 (Scopus ID)
Konferanse
11th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH 2021) November 1-4, 2021, Leuven, Belgium
Tilgjengelig fra: 2022-08-22 Laget: 2022-08-22 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Skiöld, D., Arora, S., Mihailescu, R.-C. & Balaghi, R. (2022). Forecasting key performance indicators for smart connected vehicles. In: A C B Garcia, M Ferro, J C R Ribon (Ed.), Advances in artificial intelligence: IBERAMIA 2022. Paper presented at 17th Ibero-American Conference on Artificial Intelligence (IBERAMIA), NOV 23-25 2022, Cartagena de Indias, COLOMBIA (pp. 414-415). Springer, 13788
Åpne denne publikasjonen i ny fane eller vindu >>Forecasting key performance indicators for smart connected vehicles
2022 (engelsk)Inngår i: Advances in artificial intelligence: IBERAMIA 2022 / [ed] A C B Garcia, M Ferro, J C R Ribon, Springer, 2022, Vol. 13788, s. 414-415Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

As connectivity has been introduced to the car industry, automotive companies have in-use cars which are connected to the internet. A key concern in this context represents the difficulty of knowing how the connection quality changes over time and if there are associated issues. In this work we describe the use of CDR data from connected cars supplied by Volvo to build and study forecasting models that predict how relevant KPIs change over time. Our experiments show promising results for this predictive task, which can lead to improving user experience of connectivity in smart vehicles.

sted, utgiver, år, opplag, sider
Springer, 2022
Serie
Lecture Notes in Artificial Intelligence, ISSN 0302-9743, E-ISSN 1611-3349 ; 13788
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-61083 (URN)000972628400037 ()978-3-031-22418-8 (ISBN)978-3-031-22419-5 (ISBN)
Konferanse
17th Ibero-American Conference on Artificial Intelligence (IBERAMIA), NOV 23-25 2022, Cartagena de Indias, COLOMBIA
Tilgjengelig fra: 2023-06-20 Laget: 2023-06-20 Sist oppdatert: 2023-12-13bibliografisk kontrollert
Tell, A., Hägred, C. & Mihailescu, R.-C. (2022). Perceptions of Time: Determine the Time of an Analogue Watch using Computer Vision. In: 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT): . Paper presented at 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), 18-21 December 2022, Alamein New City, Egypt. Institute of Electrical and Electronics Engineers (IEEE)
Åpne denne publikasjonen i ny fane eller vindu >>Perceptions of Time: Determine the Time of an Analogue Watch using Computer Vision
2022 (engelsk)Inngår i: 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), Institute of Electrical and Electronics Engineers (IEEE), 2022Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper explores the problem of determining the time of an analogue wristwatch by developing two systems and conducting a comparative study. The first system uses OpenCV to find the watch hands and applies geometrical techniques to calculate the time. The second system uses Machine Learning by building a neural network to classify images in Tensorflow using a multi-labelling approach. The results show that in a set environment the geometric-based approach performs better than the Machine Learning model. The geometric system predicted time correctly with an accuracy of 80% whereas the best Machine Learning model only achieves 74%. Experiments show that the accuracy of the neural network model did increase when using data augmentation, however there was no significant improvement when adding synthetic data to our training set.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2022
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-59126 (URN)10.1109/gcaiot57150.2022.10019054 (DOI)000972037000008 ()2-s2.0-85147650610 (Scopus ID)979-8-3503-0984-3 (ISBN)979-8-3503-0985-0 (ISBN)
Konferanse
2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), 18-21 December 2022, Alamein New City, Egypt
Tilgjengelig fra: 2023-04-05 Laget: 2023-04-05 Sist oppdatert: 2023-12-13bibliografisk kontrollert
Mihailescu, R.-C. (2021). A weakly-supervised deep domain adaptation method for multi-modal sensor data. In: 2021 IEEE GLOBAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS (GCAIOT): . Paper presented at IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), DEC 12-16, 2021, Dubai, U ARAB EMIRATES (pp. 45-50). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>A weakly-supervised deep domain adaptation method for multi-modal sensor data
2021 (engelsk)Inngår i: 2021 IEEE GLOBAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS (GCAIOT), IEEE , 2021, s. 45-50Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Nearly every real-world deployment of machine learning models suffers from some form of shift in data distributions in relation to the data encountered in production. This aspect is particularly pronounced when dealing with streaming data or in dynamic settings (e.g. changes in data sources, behaviour and the environment). As a result, the performance of the models degrades during deployment. In order to account for these contextual changes, domain adaptation techniques have been designed for scenarios where the aim is to learn a model from a source data distribution, which can perform well on a different, but related target data distribution. In this paper we introduce a variational autoencoder-based multi-modal approach for the task of domain adaptation, that can be trained on a large amount of labelled data from the source domain, coupled with a comparably small amount of labelled data from the target domain. We demonstrate our approach in the context of human activity recognition using various IoT sensing modalities and report superior results when benchmarking against the effective mSDA method for domain adaptation.

sted, utgiver, år, opplag, sider
IEEE, 2021
Emneord
Domain adaptation, Neural Networks, Internet of Things, Human Activity Recognition
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-51709 (URN)10.1109/GCAIoT53516.2021.9693050 (DOI)000790983800008 ()2-s2.0-85126734760 (Scopus ID)978-1-6654-3841-4 (ISBN)
Konferanse
IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), DEC 12-16, 2021, Dubai, U ARAB EMIRATES
Tilgjengelig fra: 2022-05-30 Laget: 2022-05-30 Sist oppdatert: 2024-02-05bibliografisk kontrollert
Prosjekter
Dynamic Intelligent Sensor Intensive Systems; Malmö universitet; Publikasjoner
Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
AVANS projekt: "Internet of Things Master's Program"; Malmö universitet
Organisasjoner