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Unmanned Aerial Vehicles (UAVs) are increasingly crucial in Search and Rescue (SAR) operations due to their ability to enhance efficiency and reduce costs. Search and Rescue is a vital activity as it directly impacts the preservation of life and safety in critical situations, such as locating and rescuing individuals in perilous or remote environments. However, the effectiveness of these operations heavily depends on the quality of sensor data for accurate target detection. This study investigates the application of the Real Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN) algorithm to enhance the resolution and detail of infrared images captured by UAV sensors. By improving image quality through super-resolution, we then assess the performance of the YOLOv8 target detection algorithm on these enhanced images. Preliminary results indicate that Real-ESRGAN significantly improves the quality of low-resolution infrared data, even when using pre-trained models not specifically tailored to our dataset, this highlights a considerable potential of applying the algorithm in the preprocessing stages of images generated by UAVs for search and rescue operations.

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.

Research on unmanned autonomous vehicles (UAVs) for search and rescue (SAR) missions is widespread due to its cost-effectiveness and enhancement of security and flexibility in operations. However, a significant challenge arises from the quality of sensors, terrain variability, noise, and the sizes of targets in the images and videos taken by them. Generative adversarial networks (GANs), introduced by Ian Goodfellow, among their variations, can offer excellent solutions for improving the quality of sensors, regarding super-resolution, noise removal, and other image processing issues. To identify new insights and guidance on how to apply GANs to detect living beings in SAR operations, a PRISMA-oriented systematic literature review was conducted to analyze primary studies that explore the usage of GANs for edge or object detection in images captured by drones. The results demonstrate the utilization of GAN algorithms in the realm of image enhancement for object detection, along with the metrics employed for tool validation. These findings provide insights on how to apply or modify them to aid in target identification during search stages.

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.

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.

Search missions for objects are relevant in both industrial and civilian context, such as searching for a missing child in a forest or to locating equipment in a building or large factory. To send out a drone swarm to quickly locate a misplaced item in a factory, a missing machine on a building site or a missing child in a forest is very similar. Image-based Machine Learning algorithms are now so powerful that they can be trained to identify objects with high accuracy in real time. The next challenge is to perform the search as efficiently as possible, using as little time and energy as possible. If we have information about the area to search, we can use heuristic and probabilistic methods to perform an efficient search. In this paper, we present a case study where we developed a method and approach to evaluate different search algorithms enabling the selection of the most suitable, i.e., most efficient search algorithm for the task at hand. A couple of probabilistic and heuristic search methods were implemented for testing purposes, and they are the following: Bayesian Search together with a Hill Climbing search algorithm and Bayesian Search together with an A-star search algorithm. A swarm adapted lawn mower search strategy is also implemented. In our case study, we see that the performance of the search heavily depends on the area to search in and domain knowledge, e.g., knowledge about how a child is expected to move through a forest area when lost. In our tests, we see that there are significant gains to be made by selecting a search algorithm suitable for the search context at hand.

There is an increased need for protection against unauthorized entry of drones as there has been an increased number of reports of UAV’s entering restricted areas. In this paper we explore an approach of using a swarm/team of drones that are able to cooperate, to autonomously engage and disable one or more unauthorized drones entering a restricted area. In our approach, we have investigated technologies for distributed decision-making and task allocation in real-time, in a dynamic simulated environment and developed descriptive models for how such technologies may be exploited in a mission designed for a drone swarm. This includes the definition of discrete tasks, how they interact and how they are composed to form such a mission, as well as the realization and execution of these tasks using machine learning models combined with behaviour trees. To evaluate our approach, we use a simulated environment for mission execution where relevant KPI’s related to the design of the mission have been used to measure how efficient our approach is in deterring or incapacitating unauthorized drones. The evaluation has been performed using Monte-Carlo simulations on a batch of randomized scenarios and measures of effectiveness has been used to measure each scenario instance and later compiled into a final assessment for the main scenario as well as each ingoing task. The results show a mission success in 93% of the simulated scenarios. Of these 93%, 58% of the scenarios resulted in the threat being neutralized and in 35% of the scenarios the threat was driven away from the critical area. We believe that the application of such measurements aids to validate the applicability of this capability in a real-world scenario and in order to assert the relevance of these parameters, future validations in real-world operational scenarios are warranted.

For the last few years, cases of applying artificial intelligence (AI) to engineering activities towards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematically reach higher and productivity levels, owing to its holistic perspective and consideration of economic and environmental targets. To address the current gap to more systematic deployment of AI with LCE (AI-LCE) we have performed a systematic literature review emphasizing the three aspects:(1) the most prevalent AI techniques, (2) the current AI-improved LCE subfields and (3) the subfields with highly enhanced by AI. A specific set of inclusion and exclusion criteria were used to identify and select academic papers from several fields, i.e. production, logistics, marketing and supply chain and after the selection process described in the paper we ended up with 42 scientific papers. The study and analysis show that there are many AI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, and Infrastructure; Sustainable Cities and Communities; and Responsible Consumption and Production. Overall, the papers give a picture of diverse AI techniques used in LCE. Production design and Maintenance and Repair are the top explored LCE subfields whereas logistics and Procurement are the least explored subareas. Research in AI-LCE is concentrated in a few dominating countries and especially countries with a strong research funding and focus on Industry 4.0; Germany is standing out with numbers of publications. The in-depth analysis of selected and relevant scientific papers are helpful in getting a more correct picture of the area which enables a more systematic approach to AI-LCE in the future.

The increasingly complex context of dynamic, high-tempo military air operations raise new needs for aircraft maintenance and logistic support systems to more rapidly respond to changes in operational needs and available resources, with retained support resource efficiency. The future maintenance and support system are thus envisioned with improved net-centric capabilities to facilitate matching of tactical needs with aircraft maintenance capabilities. The study addresses this challenge by creating abstract representations and definitions of relevant tactical structures and maintenance structures, processes, and resources using enterprise modelling. By addressing this in a holistic perspective, a better understanding of the matching problem is achieved, enabling efficient matching of operational needs with available resources. Based on these findings, graph models are created from a domain-centric view of two adjacent domain contexts, which includes command and control and aircraft maintenance contexts. The result has the ability to leverage interoperability and collaboration between air- and ground-based systems by facilitating interactions between tactical needs and aircraft maintenance resources.

The increasingly complex context of dynamic, high-tempo military air operations raises new needs for airbase aircraft maintenance and logistic support systems to more rapidly respond to changes in operational needs, with retained support resource efficiency. The future maintenance and support system are thus envisioned with improved net-centric capabilities to facilitate matching of tactical needs with aircraft maintenance capabilities. Today military Command and Control (C2) of tactical needs against airbase aircraft maintenance capabilities contain many manual activities. This constrains speed of execution as well as drives manning requirements, and there is a need to further develop existing IS and IT support. Thus the studied matching capability address these limitations through an approach based on improved integration of the air vehicle on-board health management system with corresponding ground-based functions, and exploitation of technologies such as big-data analytics, diagnostic-prognostics, Artificial Intelligence (AI), machine learning and reasoning systems in a system-of-system-wide service architecture. However, understanding the concept of matching of tactical needs and aircraft maintenance capabilities requires insights of complex multi-domain C2 interactions and interrelations between the tactical domain and the aircraft maintenance domain. Whereas each domain is quite well understood, the more detailed interrelations between the domains is less studied. This paper present an approach to this problem by creating useful representations of the underpinning insights, by enterprise modeling of abstract representations and definitions of relevant tactical and maintenance structures, processes, and resources, in the airbase context, to better understand the matching problem, and to address this problem through a holistic perspective.