Operational Domain Research Articles

Consciência situacional como ferramenta estratégica da defesa cibernética

The technological growth of the last decades has changed the way many sectors of society and the state carry out their respective activities. The defense sector is part of this framework due to its essentially technological nature. With these changes, discussions began about phenomena known as ‘cyberspace’, ‘cyber power’, and ‘cyber domain’. Because cyberspace permeates and can even influence the other operational domains (sea, land, air, and space), having the right knowledge of cyberspace – both one’s own and that of one’s adversaries – has become a competitive advantage in interstate conflicts. This knowledge is called situational awareness, a concept conceived in the 1980's by Mica Endsley, then Chief Scientist of the United States Air Force, and which can be defined as the ability to perceive and understand activities in a given time and space and to predict the future evolution of the situation. This concept can be unfolded in the cyber domain, giving rise to the socalled cyber situational awareness, which generally means the full knowledge of the actions occurring in the cyber space of interest. Thus, this article aims to elucidate the strategic role of cyber situational awareness, identifying the theories and models of its emerging concept and, subsequently, its developments in military strategy.

Autonomous operation domain generation and reduction method supporting the seamless power supply of important loads

Autonomous operation domain generation and reduction method supporting the seamless power supply of important loads

Effect of Deep Breathing Relaxation and Progressive Muscle Relaxation on Blood Pressure

Hypertension, characterized by diastolic pressure exceeding 90 mmHg and systolic pressure surpassing 140 mmHg, poses a significant health challenge often addressed through pharmacological antihypertensive therapy. This research endeavors to evaluate the impact of complementary interventions, specifically deep breathing relaxation and progressive muscle relaxation, on reducing blood pressure among hypertensive patients within the operational domain of the Kertasemaya Community Health Center in the Indramayu Regency in 2023. Adopting a Quasi-Experimental Design with a Pretest-Posttest and Control Group paradigm, the study engaged a cohort of 70 respondents stratified into intervention and control groups. The intervention group underwent a combined therapy of deep breathing relaxation and progressive muscle relaxation, followed by a meticulous post-test for final observations. Meanwhile, the control group underwent an initial blood pressure measurement (pre-test), a relaxation period of 15-30 minutes, and concluded with a post-test for final measurements. The study revealed a statistically significant influence (p=0.013) on average blood pressure within the intervention group compared to the control group. This disparity was observed both before and after the implementation of deep breathing relaxation and progressive muscle relaxation techniques among hypertensive respondents. These findings underscore the potential efficacy of non-pharmacological interventions in mitigating hypertension and enhancing patient well-being. The combination of deep breathing relaxation and progressive muscle relaxation emerges as a highly effective strategy in reducing blood pressure among hypertensive individuals. This integrative therapeutic approach showcases promising potential for promoting cardiovascular well-being, offering a valuable non-pharmacological avenue for hypertension management

HUMAN RESOURCE MANAGEMENT OPERATIONS IN RELATION TO EMPOWERMENT OF PUBLIC SECONDARY TEACHERS IN PANABO CITY DIVISION

The study explored the relationship between human resource management operations and empowerment of teachers in public secondary schools of Panabo City Division. Also, it investigated the association of the involved variables and the domains of human resource management operations that significantly influenced empowerment of teachers. With the use of probability sampling, 150 secondary teachers in the public schools were selected as the respondents. Utilizing the descriptive-correlational survey method, the data collated were analyzed through the use of Mean, Product-Moment correlation and Regression Analysis. Results revealed that there was extensive human resource management operations and an extensive empowerment of teachers. Furthermore, there was a significant relationship between the two variables. Moreover, all domains of human resource management operations were found to have significantly influenced empowerment of teachers. Based on the findings, it was further suggested that higher officials in the Department of Education may identify means on how to help the school heads in strengthening the human resource management operations to ensure empowerment among teachers. KEYWORDS: Human resource management operations, empowerment of teachers, descriptive correlation, Panabo City Division, Philippines

Operational Environments at the Extreme Tactical Edge

You can’t get more “on the tactical edge” than in space. No other operational domain suffers from the combinations of distance from the operator, harsh environments, unreachable assets with aging hardware, and increadably long communications as space systems. The complexity of developing and deploying AI solutions in satellites and probes is far more difficult than deploying similar AI on Earth. This talk explores some of the considerations involved in deploying AI and machine learning (ML) in the space domain.

Searching for a Cheap Robust Steering Controller

The study of lateral steering control for Automated Driving Systems identifies new control solutions more often than new control problems. This is likely due to the maturity of the field. To prevent repeating efforts toward solving already-solved problems, what is needed is a cohesive way of evaluating all developed controllers under a wide variety of environmental conditions. This work serves as a step in this direction. Four controllers are tested on five maneuvers representing highways and collision avoidance trajectories. Each controller and maneuver combination is repeated on five sets of environmental conditions or Operational Design Domains (ODDs). The design of these ODDs ensures the translation of these experimental results to real-world applications. The commercial software, CarSim 2020, is extended with Simulink models of the environment, sensor dynamics, and state estimation performances to perform highly repeatable and realistic evaluations of each controller. The results of this work demonstrate that most of the combinations of maneuvers and ODDs have existing cheap controllers that achieve satisfactorily safe performance. Therefore, this field’s research efforts should be directed toward finding new control problems in lateral path tracking rather than proposing new controllers for ODDs that are already solved.

Driving Domain Classification Based on Kernel Density Estimation of Urban Land Use and Road Network Scaling Models

Current research on automated driving systems focuses on Level 4 automated driving (AD) in specific operational design Domains (ODD). Measurement data from customer fleet operation are commonly used to extract scenarios and ODD features (road infrastructure, etc.) for the testing of AD functions. To ensure data relevance for the vehicle use case, driving domain classification of the data is required. Generally, classification into urban, extra-urban and highway domains provides data with similar ODD features. Highway classification can be implemented using global navigation satellite system coordinates of the driving route, map-matching algorithms, and road classes stored in digital maps. However, the distinction between urban and extra-urban driving domains is more complex, as settlement taxonomies and administrative-level hierarchies are not globally consistent. Therefore, this paper presents a map-based method for driving domain classification. First, potential urban areas (PUA) are identified based on urban land-use density, which is determined based on land-use categories from OpenStreetMap (OSM) and then spatially smoothed by kernel density estimation. Subsequently, two road network scaling models are used to distinguish between urban and extra-urban domains for the PUA. Finally, statistics of ODD feature distribution are analysed for the classified urban and extra-urban areas.

BOTSH: Towards aligning semantic web of things with building ontologies

The W3C linked building data group is working on modeling the information for integrating building information with building life cycle data using Semantic Web technologies. The community has proposed a set of semantic models such as ifcOWL and Building Topology Ontology (BOT), to model various applications across Architecture, Engineering, Construction, and Operation (AECO) domain. On the other hand, the Semantic Web of Things (SWoT) group proposed standard semantic models such as M3-lite and BOSH ontologies for describing the sensor networks, observations, and sensor measurements. Both the aforementioned domains have their own siloed applications and with the evolution of the smart home domain, there is a need to combine the knowledge of building information with the sensor knowledge to develop cross-domain applications. However, in order to develop such downstream applications leveraging advantages from both domains requires interoperable knowledge. This paper proposes an interoperable ontology, Building Topology Ontology for Smart Homes (BOTSH), with the aim of aligning the building domain with sensors domain semantic models. The BOTSH ontology facilitates capturing knowledge from both domains and helps in developing cross-domain applications. The potential of the proposed model was demonstrated using a real-life building model based on the competency questions framed by the domain experts.

Upaya Meningkatkan Motivasi Belajar Matematika Siswa SD Melalui Optimasi Game “Moving Twins”

Elementary school (SD) students must possess fundamental mathematical abilities. One of them is the capacity to execute numerical calculation procedures. Evidence from the field indicates that the academic performance of primary school kids in mathematics remains at a poor level. This occurs due to the constraints imposed by teachers on pupils' activities at school, leading to a rapid onset of boredom during the learning process. Hence, it is imperative to establish a novel educational environment by leveraging media tools to enhance the enjoyment and dynamism of the learning process. The learning method "MOVING TWINS" will be utilized. The objective of this study is to enhance the learning motivation of primary school children in the subject of arithmetic operations by utilizing the Moving Twins game. The participants of this study consisted of elementary school kids, selected using the purposive sampling technique. This study employs a pre-experimental design known as a one group pretest-posttest design. The research findings indicate that employing the Moving Twins Game can enhance the learning motivation of primary school pupils in the domain of number counting operations. Keywords: Learning Motivation, Arithmetic Operations, Moving Twins Game.

Distributed Localization for UAV–UGV Cooperative Systems Using Information Consensus Filter

In the evolving landscape of autonomous systems, the integration of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has emerged as a promising solution for improving the localization accuracy and operational efficiency for diverse applications. This study introduces an Information Consensus Filter (ICF)-based decentralized control system for UAVs, incorporating the Control Barrier Function–Control Lyapunov Function (CBF–CLF) strategy aimed at enhancing operational safety and efficiency. At the core of our approach lies an ICF-based decentralized control algorithm that allows UAVs to autonomously adjust their flight controls in real time based on inter-UAV communication. This facilitates cohesive movement operation, significantly improving the system resilience and adaptability. Meanwhile, the UAV is equipped with a visual recognition system designed for tracking and locating the UGV. According to the experiments proposed in the paper, the precision of this visual recognition system correlates significantly with the operational distance. The proposed CBF–CLF strategy dynamically adjusts the control inputs to maintain safe distances between the UAV and UGV, thereby enhancing the accuracy of the visual system. The effectiveness and robustness of the proposed system are demonstrated through extensive simulations and experiments, highlighting its potential for widespread application in UAV operational domains.

A Sustainable Multi-Objective Model for Capacitated-Electric-Vehicle-Routing-Problem Considering Hard and Soft Time Windows as Well as Partial Recharging.

Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied in recent years. This paper deals with an extended version of EVRP, in which electric vehicles (EVs) deliver goods to customers. The limited battery capacity of EVs causes their operational domains to be less than those of gasoline vehicles. For this purpose, several charging stations are considered in this study for EVs. In addition, depending on the operational domain, a full charge may not be needed, which reduces the operation time. Therefore, partial recharging is also taken into account in the present research. This problem is formulated as a multi-objective integer linear programming model, whose objective functions include economic, environmental, and social aspects. Then, the preemptive fuzzy goal programming method (PFGP) is exploited as an exact method to solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired by nature, including MOSA, MOGWO, MOPSO, and NSGAII_TLBO, are utilized to solve large-sized problems. The results obtained from solving the numerous test problems demonstrate that the hybrid meta-heuristic algorithm can provide efficient solutions in terms of quality and non-dominated solutions in all test problems. In addition, the performance of the algorithms was compared in terms of four indexes: time, MID, MOCV, and HV. Moreover, statistical analysis is performed to investigate whether there is a significant difference between the performance of the algorithms. The results indicate that the MOSA algorithm performs better in terms of the time index. On the other hand, the NSGA-II-TLBO algorithm outperforms in terms of the MID, MOCV, and HV indexes.

Designing Heuristic-Based Tuners for Fractional-Order PID Controllers in Automatic Voltage Regulator Systems Using a Hyper-Heuristic Approach

This work introduces an alternative approach for developing a customized Metaheuristic (MH) tailored for tuning a Fractional-Order Proportional-Integral-Derivative (FOPID) controller within an Automatic Voltage Regulator (AVR) system. Leveraging an Automated Algorithm Design (AAD) methodology, our strategy generates MHs by utilizing a population-based Search Operator (SO) domain, thus minimizing human-induced bias. This approach eliminates the need for manual coding or the daunting task of selecting an optimal algorithm from a vast collection of the current literature. The devised MH consists of two distinct SOs: a dynamic swarm perturbator succeeded by a Metropolis-type selector and a genetic crossover perturbator, followed by another Metropolis-type selector. This MH fine-tunes the FOPID controller’s parameters, aiming to enhance control performance by reducing overshoot, rise time, and settling time. Our research includes a comparative analysis with similar studies, revealing that our tailored MH significantly improves the FOPID controller’s speed by 1.69 times while virtually eliminating overshoot. Plus, we assess the tuned FOPID controller’s resilience against internal disturbances within AVR subsystems. The study also explores two facets of control performance: the impact of fractional orders on conventional PID controller efficiency and the delineating of a confidence region for stable and satisfactory AVR operation. This work’s main contributions are introducing an innovative method for deriving efficient MHs in electrical engineering and control systems and demonstrating the substantial benefits of precise controller tuning, as evidenced by the superior performance of our customized MH compared to existing solutions.

Innovating in New Operating Domains Begins Not in the Pragmatic and Known, but the Fantastic and Weird

Innovating in New Operating Domains Begins Not in the Pragmatic and Known, but the Fantastic and Weird

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.

OntoAgency: An agency-based ontology for tracing control, ownership and decision-making in smart buildings

This article proposes an agency-based design ontology (OntoAgency) to systematically trace the relationships between stakeholders who own, control, and decide upon the benefits and experiences provided by smart building services. The ontology associates ‘smartness’ to different building operation domains and assigns their functionalities to agents involved in the flow of data and decision making. It enables control paths to be traced back to specific people, machines, and companies behind smart operations – including those that appear to be ‘external’ to a smart building. Based on Actor-Network Theory (ANT), the ontology connects knowledge from the social sciences to technical knowledge from building design, construction, operation and controls. The need in developing this ontology originates from the limits of conventional design and engineering ontologies, particularly with respect to their unproblematic take on the interplay between different social and technical systems and their neglect to model the varied abilities of stakeholders to exert control over, and benefit from, the technical systems and operations. The proposed ontology addresses these gaps by offering a more realistic and versatile model of inter-agents relationships. It can also highlight points of consented and unconsented data leakage and associated security issues. This knowledge becomes critical for designing and managing increasingly more complex smart building systems.

Optimizing industrial transport with a connected automated vehicle demonstrator for assembly systems and end-of-line production

In recent years, the automotive industry has witnessed significant progress in the development of automated driving technologies. The integration of advanced sensors and systems in vehicles has led to the emergence of various functionalities, such as driving assistance and autonomous driving. Applying these technologies on the assembly line can enhance the efficiency, safety, and speed of transportation, especially at end-of-line production. This work presents a connected automated vehicle (CAV) demonstrator for generating autonomous driving systems and services for the automotive industry. Our prototype electric vehicle is equipped with state-of-the-art sensors and systems for perception, localization, navigation, and control. We tested various algorithms and tools for transforming the vehicle into a self-driving platform, and the prototype was simulated and tested in an industrial environment as proof of concept for integration into assembly systems and end-of-line transport. Our results show the successful integration of self-driving vehicle platforms in the automotive industry, particularly in factory halls. We demonstrate the localization, navigation, and communication capabilities of our prototype in a demo area. This work anticipates a significant increase in efficiency and operating cost reduction in vehicle manufacturing, despite challenges such as current low traveling speeds and high equipment costs. Ongoing research aims to enhance safety for higher vehicle speeds, making it a more viable business case for manufacturers, considering the increasing standardization of automated driving equipment in cars. The main contribution of this paper lies in introducing the general concept architecture of the integration of automated driving functionalities in end-of-line assembly and production systems. Showing a case study of the effective development and implementation of such functionalities with a CAV demonstrator in a more standardized industrial operational design domain.

Refined Short‐Term Forecasting Atmospheric Temperature Profiles in the Stratosphere Based on Operators Learning of Neural Networks

AbstractThe efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere.

Examining IT Service Management at Company XYZ through the Lens of ITIL V3 Service Operation Domain

Examining IT Service Management at Company XYZ through the Lens of ITIL V3 Service Operation Domain

Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience

The integration of Artificial Intelligence (AI) into supply chain management has emerged as a pivotal avenue for enhancing efficiency and resilience in contemporary business operations. This paper explores various theoretical approaches to AI within the context of supply chain optimization, delineating pathways to achieve heightened performance and adaptability. Commencing with a historical overview, the paper delves into the evolution of AI techniques in supply chain management, elucidating how these methodologies have transformed the landscape of logistics and operations. Fundamental to this exploration is the discussion on mathematical modeling and algorithmic frameworks that underpin supply chain optimization, providing the theoretical foundation for subsequent AI applications. A key focus of the paper lies in the application of machine learning techniques for demand forecasting and inventory management, which leverage data-driven insights to optimize resource allocation and mitigate risks associated with supply-demand fluctuations. Additionally, network theory and graph algorithms play a crucial role in optimizing the structure and dynamics of supply chain networks, enabling efficient transportation, distribution, and inventory routing. Strategic decision-making in supply chains is addressed through the lens of game theory, which offers theoretical frameworks to model interactions among multiple stakeholders and optimize outcomes in competitive environments. Moreover, swarm intelligence and multi-agent systems provide innovative solutions for coordination and collaboration within complex supply chain ecosystems. Evolutionary algorithms and artificial neural networks are discussed as powerful tools for supply chain design, predictive analytics, and risk management, offering capabilities for optimizing decision-making processes across various operational domains. Furthermore, reinforcement learning techniques empower dynamic decision-making in real-time operational settings, fostering adaptive and resilient supply chain management practices. By integrating multiple AI techniques, hybrid approaches offer synergistic solutions that capitalize on the strengths of diverse methodologies to address multifaceted challenges in supply chain optimization. Through a synthesis of theoretical insights and practical case studies, this paper provides valuable insights into the current state and future directions of AI-driven supply chain optimization.

A comprehensive approach to safety for highly automated off-road machinery under Regulation 2023/1230

As of January 2027, Machine Safety Regulation 2023/1230 replaces Directive 2006/42/EC, and a proposal for the regulation of Artificial Intelligence (AI) systems is soon to be ratified by the European Council. These legislative changes address hazards from increased automation and AI integration. Original Equipment Manufacturers (OEMs) face compliance challenges due to a lack of standardized guidelines, especially for off-road machinery with level 4 ‘high automation’ capabilities.At level 4 ‘high automation’, the machine operator assumes a supervisory role, intervening only in situations beyond the designed operational domain. Relying solely on supervisor intervention at this level of automation is unreasonable. Within this context, legislative safety requirements to maintain safety advocate for robust risk management systems, including both a human-in-the-loop safety option and adequate safety-related decision-making by the level 4 ‘high automation’ off-road machine, i.e., the safe reasoning software becomes paramount.In this research, conducted through a systematic mapping study, we discuss emerging safety trends and integration for the safe reasoning software a level 4 off-road machine requires. Our analyses shows the necessity of safety constraint-based design procedures. These procedures involve constraining the AI system by embedding fault management, reasoning checks, and diagnostic monitoring together with safety-related decision-making, thereby enhancing functional reliability and reducing risks. We consider these trends and patterns through the lens of changing machine safety legislative requirements, and the proposal of the regulation of AI products. Illustrating their implications alongside a case study of a level 4 tree harvesting machine, for a tangible and to industry transferable perspective on our results.