Autonomic System Research Articles

Leveraging Emerging Technologies for Enhanced Airport Ground Movement Management Efficiency and Reliability

The Executive summary serves to give the highlights of the research paper and an overview of the objectives, methods used, key findings and implications. It is a brief rundown of the whole research document and provides time-pressed readers with some insights onto the study without having to go into deeper details. Introduction: This research paper examines how blockchain, edge computing and autonomous systems can be integrated inside airports in order to enhance performance and reliability of airport ground movement management. There is need for innovative approaches towards optimizing ground movement in highly complex airports to ensure that they operate seamlessly in a growing air traffic scenario. Objectives: The main aim of this study is to explore how blockchain, edge computing and autonomous systems could change airport ground movement management entirely. These technologies will be exploited for improving data sharing, enhancing real-time processing capabilities as well as enabling automatic decision support services on the ground operations. Methodology: In this case a mixed method approach has been used which involves qualitative and quantitative analysis. Qualitative methods like surveys and questionnaires are done so as researchers get insights from different stakeholders, Integrating various stakeholders, including airlines, airports, and regulators, regarding their perspectives on integrating new technologies into ground movement management!!! Quantitative methods involve data analysis to assess the effectiveness and efficiency of proposed technological solutions. Additionally, simulations and case studies are conducted to evaluate the impact of these technologies on ground movement optimization. Key Findings: The research yields promising results, indicating significant improvements in routing efficiency, reduced taxiing times, and increased operational reliability. The integration of blockchain facilitates secure data sharing, while edge computing enables real-time processing, leading to faster decision-making in ground movement management! Autonomous systems contribute to optimizing resource routing and allocation decisions, further enhancing operational efficiency. Implications and Recommendations: The findings of the research carry both theoretical and practical implications. Theoretical implications include advancing knowledge in the field of airport ground movement management by exploring the potential of emerging technologies. On a practical level, the research provides valuable insights for airport stakeholders seeking to modernize their ground movement control systems?? Recommendations include addressing the complexity of integrating multiple emerging technologies and overcoming challenges related to data security and system interoperability. Conclusion: In conclusion, the research underscores the significance of integrating blockchain, edge computing, and autonomous systems into airport traffic management to enhance efficiency and reliability!!! By addressing key challenges in ground movement management, the study paves the way for more streamlined and resilient airport operations, ultimately benefiting the aviation industry as a whole.

Multilayer Neural Network Hardware Designs for Faster Convolutional Processing

The demand for efficient hardware implementations of convolutional neural networks (CNNs) has surged with the proliferation of deep learning applications in various domains such as computer vision, natural language processing, and autonomous systems. Convolutional layers, being the fundamental building blocks of CNNs, are computationally intensive, requiring optimized hardware architectures for real-time inference tasks. This paper presents novel hardware designs targeting faster convolutional processing within multilayer neural networks. We propose a multi-faceted approach that leverages parallelism, pipelining, and hardware acceleration techniques to enhance the efficiency of convolution operations. Our design optimally exploits the inherent data-level and model-level parallelism present in CNNs to achieve high throughput while minimizing latency.

Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

The global iron ore price is influenced by numerous factors, thus showcasing a complex interplay among them. The collective expectations of market participants over time shape the variations and trends within the iron ore price time series. Consequently, devising a robust forecasting model for the volatility of iron ore prices, as well as for other assets connected to this commodity, is critical for guiding future investments and decision-making processes in mining companies. Within this framework, the integration of artificial intelligence techniques, encompassing both technical and fundamental analyses, is aimed at developing a comprehensive, autonomous hybrid system for decision support, which is specialized in iron ore asset management. This approach not only enhances the accuracy of predictions but also supports strategic planning in the mining sector.

On the Feedback Synthesis for an Autonomous Linear System with Perturbations

On the Feedback Synthesis for an Autonomous Linear System with Perturbations

Bloom dynamics under the effects of periodic driving forces

Phytoplankton bloom received considerable attention for many decades. Different approaches have been used to explain the bloom phenomena. In this paper, we study a Nutrient–Phytoplankton–Zooplankton (NPZ) model consisting of a periodic driving force in the growth rate of phytoplankton due to solar radiation and analyse the dynamics of the corresponding autonomous and non-autonomous systems in different parametric regions. Then we introduce a novel aspect to extend the model by incorporating another periodic driving force into the growth term of the phytoplankton due to sea surface temperature (SST), a key point of innovation. Temperature dependency of the maximum growth rate (μmax) of the phytoplankton is modelled by the well-known Q10 formulation: μmax=μ0∗(Q10)T/10, where μ0 is maximum growth at 0oC. Stability conditions for all three equilibrium points are expressed in terms of the new parameter ρ2, which appears due to the incorporation of periodic driving forces. System dynamics is explored through a detailed bifurcation analysis, both mathematically and numerically, with respect to the light and temperature dependent phytoplankton growth response. Bloom phenomenon is explained by the saddle point bloom mechanism even when the co-existing equilibrium point does not exist for some values of ρ2. Solar radiation and SST are modelled using sinusoidal functions constructed from satellite data. Our results of the proposed model describe the initiation of the phytoplankton bloom better than an existing model for the region 25–35° W, 40–45° N of the North Atlantic Ocean. An improvement of 14 days (approximately) is observed in the bloom initiation time. The rate of change method (ROC) is applied to predict the bloom initiation.

Design and Experiment of an Autonomous Navigation System for a Cattle Barn Feed-Pushing Robot Based on UWB Positioning

The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments.

Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by In Situ Spectroscopy and Automation.

Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe2 films. By combing the automation of PLD synthesis and in situ diagnostic feedback with a high-throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe2 films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD.

Autonomous inland navigation: a literature review and extracontractual liability issues

Pilot tests for (semi-)autonomous transport via inland waterways are already taking place in Belgium and other European countries. However, the full commercial implementation of autonomous inland shipping might be hampered by liability issues. The allocation of liability, especially extracontractual liability, is an important concern for shipowners who want to invest in autonomous barges, and for other ecosystem actors. For this reason, a balanced risk distribution framework could boost the investment decision. A legal analysis of the current laws and regulations is necessary to evaluate whether they can be applied to new autonomous systems. The research approach consists of two steps. First, an in-depth literature review is carried out to determine whether extracontractual liability research has already been conducted and to highlight the gaps in autonomous inland waterways transport studies. Once it is proved that the vast majority of the research focuses on technology, it can be affirmed that there is a need to inquire about extracontractual liability. In the second part, thus, the two international Conventions on liability limitation and collision in inland navigation are examined.

Autonomous Ships: A Thematic Review

Ships connect the global economy through maritime transport. However, their susceptibility to increasing geopolitical conflicts has heightened concerns about the risks to crew safety and navigation security. This systematic literature review (SLR), utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, rigorously examines the safety and security of autonomous ships in maritime transport. The methodology employs a comprehensive search across major databases including Scopus, Google Scholar, and Science Direct, based on explicit inclusion criteria focusing on recent advancements from 2014 to 2023. By methodically analyzing 58 relevant publications screened from an initial pool of 1407, this paper highlights critical trends and gaps in the application of advanced sensor technologies, cybersecurity measures, and autonomous navigation systems. The findings provide insights into the operational challenges and technological developments shaping the future of maritime safety and security, offering valuable guidance for policymakers and industry stakeholders. This research contributes to scholarly discourse in this industry by mapping the trajectory of technological integration and its implications for maritime operations in a global context.

Neural Networks in Legal Theory

Abstract This article explores the domain of legal analysis and its methodologies, emphasising the significance of generalisation in legal systems. It discusses the process of generalisation in relation to legal concepts and the development of ideal concepts that form the foundation of law. The article examines the role of logical induction and its similarities with semantic generalisation, highlighting their importance in legal decision-making. It also critiques the formal-deductive approach in legal practice and advocates for more adaptable models, incorporating fuzzy logic, non-monotonic defeasible reasoning, and artificial intelligence. The potential application of neural networks, specifically deep learning algorithms, in legal theory is also discussed. The article discusses how neural networks encode legal knowledge in their synaptic connections, while the syllogistic model condenses legal information into axioms. The article also highlights how neural networks assimilate novel experiences and exhibit evolutionary progression, unlike the deductive model of law. Additionally, the article examines the historical and theoretical foundations of jurisprudence that align with the basic principles of neural networks. It delves into the statistical analysis of legal phenomena and theories that view legal development as an evolutionary process. The article then explores Friedrich Hayek’s theory of law as an autonomous self-organising system and its compatibility with neural network models. It concludes by discussing the implications of Hayek’s theory on the role of a lawyer and the precision of neural networks.

Autonomous Vehicles Perception, Acceptance, and Future Prospects in the GCC: An Analysis Using the UTAUT-Based Model

The emergence of Autonomous Vehicles (AVs) marks a significant advancement in the automotive industry, transitioning from driver-assistance technologies to fully autonomous systems. This change is particularly impactful in the Gulf Cooperation Council (GCC) region, which is a significant automotive market and technological hub. However, the adoption of AVs in the GCC faces unique challenges due to the influence of cultural norms and geographical characteristics. Our research utilizes a customized framework of the Unified Theory of Acceptance and Use of Technology (UTAUT), which is adapted to include cultural and geographical factors. This approach fills a gap in the existing literature by identifying and analyzing the key factors affecting the adoption of AVs in the GCC. Our findings indicate a difference in the receptiveness towards AVs among different demographics. Younger participants displayed a more favorable attitude towards AVs compared to older individuals. Additionally, gender and educational attainment play significant roles in the acceptance of AVs. Specifically, our results suggest that there are variations in acceptance rates among genders and individuals with varying levels of education. The United Arab Emirates (UAE) has a relatively high acceptance rate of AVs due to its advanced infrastructure and openness to technological innovations. Our study identifies facilitating conditions and performance expectancy as crucial determinants of intention to use AVs in the GCC. It emphasizes the importance of infrastructure readiness and the perceived advantages of AVs in promoting their adoption.

A Ship Trajectory Prediction Method Based on an Optuna–BILSTM Model

In the field of maritime traffic management, overcoming the challenges of low prediction accuracy and computational inefficiency in ship trajectory prediction is crucial for collision avoidance. This paper presents an advanced solution using a deep bidirectional long- and short-term memory network (BILSTM) and the Optuna hyperparameter automatic optimized framework. Utilizing automatic identification system (AIS) data to analyze ship navigation patterns, the study applies Optuna to fine-tune the hyperparameters of the BILSTM network to improve prediction accuracy and efficiency. The developed Optuna–BILSTM model shows a remarkable 7% increase in prediction accuracy over traditional back propagation (BP) neural networks and standard BILSTM models. These results not only improve ship navigation and safety but also have significant implications for the development of autonomous ship collision avoidance systems, marking a significant step toward safer and more efficient maritime traffic management.

VizNav: A Modular Off-Policy Deep Reinforcement Learning Framework for Vision-Based Autonomous UAV Navigation in 3D Dynamic Environments

Unmanned aerial vehicles (UAVs) provide benefits through eco-friendliness, cost-effectiveness, and reduction of human risk. Deep reinforcement learning (DRL) is widely used for autonomous UAV navigation; however, current techniques often oversimplify the environment or impose movement restrictions. Additionally, most vision-based systems lack precise depth perception, while range finders provide a limited environmental overview, and LiDAR is energy-intensive. To address these challenges, this paper proposes VizNav, a modular DRL-based framework for autonomous UAV navigation in dynamic 3D environments without imposing conventional mobility constraints. VizNav incorporates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with Prioritized Experience Replay and Importance Sampling (PER) to improve performance in continuous action spaces and mitigate overestimations. Additionally, VizNav employs depth map images (DMIs) to enhance visual navigation by accurately estimating objects’ depth information, thereby improving obstacle avoidance. Empirical results show that VizNav, by leveraging TD3, improves navigation, and the inclusion of PER and DMI further boosts performance. Furthermore, the deployment of VizNav across various experimental settings confirms its flexibility and adaptability. The framework’s architecture separates the agent’s learning from the training process, facilitating integration with various DRL algorithms, simulation environments, and reward functions. This modularity creates a potential to influence RL simulation in various autonomous navigation systems, including robotics control and autonomous vehicles.

Multi-Agent target allocation and safe trajectory planning for artificial pollination tasks

This paper presents an autonomous multi-agent drone system utilized for an artificial pollination task to address the declining bee population and its impact on natural pollination. The main goal is to visit all the targets (flowers) by drones. The system receives the position of the targets located on a vertical fruiting wall from a single sensor mounted on an unmanned ground vehicle (UGV). Then, it determines the sequence of target allocation to minimize the task time and to minimize the accumulated distance each drone travels. The limited payload capacity of each drone and the need to refill stock carried by the UGV are also considered. After allocating the targets, a safe trajectory is generated for each drone to avoid collision with other drones, considering uncertainty in the drones’ positions. When a drone reaches a target, the system allocates a new target, and the cycle repeats until the task is completed. The system is reactive to the evolution of the task and adjusts accordingly. An additional tested factor is the impact of the positioning error on the task time. The system was evaluated through dynamic simulations using real datasets of peach and pear orchards and finally in a laboratory proof-of-concept experiment using a micro-drone system.

Autonomous Vehicle for Fire detecting and Extinguishing

— Our project presents an innovative solution for fire detection and extinguishing through an autonomous rover system. Leveraging fire sensors integrated with a node MCU, the system swiftly identifies fire outbreaks and transmits alerts to the rover via the ThingSpeak cloud platform. Upon receipt of the alert, the rover activates a buzzer for immediate local notification and dispatches alerts through a dedicated mobile application, ensuring rapid response from relevant personnel. Employing a line follower mechanism, the rover autonomously navigates towards the fire-affected area. Equipped with a water spraying system, the rover efficiently extinguishes flames, minimizing water wastage and enhancing firefighting effectiveness. This approach significantly reduces response time, enhancing safety across diverse environments. The system's ability to optimize resource utilization and automate firefighting procedures makes it adaptable to various settings, including industrial facilities, urban areas, and remote locations. Overall, our autonomous rover system represents a substantial advancement in firefighting technology, offering swift and efficient fire detection and extinguishing capabilities to mitigate fire hazards effectively. Keywords— NodeMCU, Rover, Flame Sensors, Servomotor, L293d motor drivers, Li-ion batteries

A Survey of MPSoC Management toward Self-Awareness

Managing Multi-Processor Systems-on-Chip (MPSoCs) is becoming increasingly complex as demands for advanced capabilities rise. This complexity is due to the involvement of more processing elements and resources, leading to a higher degree of heterogeneity throughout the system. Over time, management schemes have evolved from simple to autonomous systems with continuous control and monitoring of various parameters such as power distribution, thermal events, fault tolerance, and system security. Autonomous management integrates self-awareness into the system, making it aware of its environment, behavior, and objectives. Self-Aware Cyber-Physical Systems-on-Chip (SA-CPSoCs) have emerged as a concept to achieve highly autonomous management. Communication infrastructure is also vital to SoCs, and Software-Defined Networks-on-Chip (SDNoCs) can serve as a base structure for self-aware systems-on-chip. This paper presents a survey of the evolution of MPSoC management over the last two decades, categorizing research works according to their objectives and improvements. It also discusses the characteristics and properties of SA-CPSoCs and explains why SDNoCs are crucial for these systems.

Median Absolute Deviation for BGP Anomaly Detection

The stability and reliability of the global Internet infrastructure heavily rely on the Border Gateway Protocol (BGP), a crucial protocol that facilitates the exchange of routing information among various Autonomous Systems, ensuring seamless connectivity worldwide. However, BGP inherently possesses a susceptibility to abnormal routing behaviors, potentially leading to significant connectivity disruptions. Despite extensive efforts, accurately detecting and effectively mitigating such abnormalities persist as tough challenges. To tackle these, this article proposes a novel statistical approach employing the median absolute deviation under certain constraints to proactively detect anomalies in BGP. By applying advanced analysis techniques, this research offers a robust method for the early detection of anomalies, such as Internet worms, configuration errors, and link failures. This innovative approach has been empirically validated, achieving an accuracy rate of 90% and a precision of 95% in identifying these disruptions. This high level of precision and accuracy not only confirms the effectiveness of the statistical method employed but also marks a significant step forward for enhancing the stability and reliability of the global Internet infrastructure.

ASV station keeping under wind disturbances using neural network simulation error minimization model predictive control

AbstractStation keeping is an essential maneuver for autonomous surface vehicles (ASVs), mainly when used in confined spaces, to carry out surveys that require the ASV to keep its position or in collaboration with other vehicles where the relative position has an impact over the mission. However, this maneuver can become challenging for classic feedback controllers due to the need for an accurate model of the ASV dynamics and the environmental disturbances. This work proposes a model predictive controller using neural network simulation error minimization (NNSEM–MPC) to accurately predict the dynamics of the ASV under wind disturbances. The performance of the proposed scheme under wind disturbances is tested and compared against other controllers in simulation, using the robotics operating system and the multipurpose simulation environment Gazebo. A set of six tests was conducted by combining two varying wind speeds that are modeled as the Harris spectrum and three wind directions (, , and ). The simulation results clearly show the advantage of the NNSEM–MPC over the following methods: backstepping controller, sliding mode controller, simplified dynamics MPC (SD‐MPC), neural ordinary differential equation MPC (NODE‐MPC), and knowledge‐based NODE MPC. The proposed NNSEM–MPC approach performs better than the rest in five out of the six test conditions, and it is the second best in the remaining test case, reducing the mean position and heading error by at least % ( m) and % (), respectively, across all the test cases. In terms of execution speed, the proposed NNSEM–MPC is at least 36% faster than the rest of the MPC controllers. The field experiments on two different ASV platforms showed that ASVs can effectively keep the station utilizing the proposed method, with a position error as low as m and a heading error as low as within time windows of at least s. This would increase the potential applications of ASVs for launch, recovery, and replenishment in long‐term surveys in collaboration with other autonomous systems.

Autonomous flight performance maximization for slung load carrying rotary wing mini unmanned aerial vehicle

Purpose This research study aims to minimize autonomous flight cost and maximize autonomous flight performance of a slung load carrying rotary wing mini unmanned aerial vehicle (i.e. UAV) by stochastically optimizing autonomous flight control system (AFCS) parameters. For minimizing autonomous flight cost and maximizing autonomous flight performance, a stochastic design approach is benefitted over certain parameters (i.e. gains of longitudinal PID controller of a hierarchical autopilot system) meanwhile lower and upper constraints exist on these design parameters. Design/methodology/approach A rotary wing mini UAV is produced in drone Laboratory of Iskenderun Technical University. This rotary wing UAV has three blades main rotor, fuselage, landing gear and tail rotor. It is also able to carry slung loads. AFCS variables (i.e. gains of longitudinal PID controller of hierarchical autopilot system) are stochastically optimized to minimize autonomous flight cost capturing rise time, settling time and overshoot during longitudinal flight and to maximize autonomous flight performance. Found outcomes are applied during composing rotary wing mini UAV autonomous flight simulations. Findings By using stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads over previously mentioned gains longitudinal PID controller when there are lower and upper constraints on these variables, a high autonomous performance having rotary wing mini UAV is obtained. Research limitations/implications Approval of Directorate General of Civil Aviation in Republic of Türkiye is essential for real-time rotary wing mini UAV autonomous flights. Practical implications Stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads is properly valuable for recovering autonomous flight performance cost of any rotary wing mini UAV. Originality/value Establishing a novel procedure for improving autonomous flight performance cost of a rotary wing mini UAV carrying slung loads and introducing a new process performing stochastic optimization of AFCS for rotary wing mini UAVs carrying slung loads meanwhile there exists upper and lower bounds on design variables.

A comprehensive overview of core modules in visual SLAM framework

Visual Simultaneous Localization and Mapping (VSLAM) technology has become a key technology in autonomous driving and robot navigation. Relying on camera sensors, VSLAM can provide a richer and more precise perception means, and its advancement has accelerated in recent years. However, current review studies are often limited to in-depth analysis of a specific module and lack a comprehensive review of the entire VSLAM framework. The VSLAM system consists of five core components: (1) The camera sensor module is responsible for capturing visual information about the surrounding environment. (2) The front-end module uses image data to roughly estimate the camera’s position and orientation. (3) The back-end module optimizes and processes the pose information estimated by the front-end. (4) The loop detection module is used to correct accumulated errors in the system. (5) The mapping module is responsible for generating environmental maps. This review provides a systematic and comprehensive analysis of the SLAM framework by taking the core components of VSLAM as the entry point. Deep learning brings new development opportunities for VSLAM, but it still needs to solve the problems of data dependence, cost and real-time in practical application. We deeply explore the challenges of combining VSLAM with deep learning and feasible solutions. This review provides a valuable reference for the development of VSLAM. This will help push VSLAM technology to become smarter and more efficient. Thus, it can better meet the needs of future intelligent autonomous systems in multiple fields.