Autonomic System Research Articles

The evolution of AI: What does the future hold in the next two years

Artificial intelligence (AI) has evolved rapidly in recent years, transforming several industries and reshaping cultural norms. This article examines the current state of AI technology, focusing on significant trends and developments that have accelerated its growth. This paper sheds light on the evolving environment of AI innovation by examining current breakthroughs in machine learning, deep learning, and ethical concerns surrounding AI deployment. The following two years should see substantial advances in natural language processing, computer vision, and autonomous systems. Despite the excitement, difficulties such as ethical considerations, regulatory frameworks, and societal repercussions loom large, needing careful study and decisive action. This study aims to inform stakeholders about the transformative potential of AI and the methods required to properly navigate its growing landscape by conducting a balanced examination of opportunities and challenges.

Organisational Dynamics and Local Government Autonomy in Nigeria. An Interrogation

This research paper attempt to investigate the critical relationship between the interplay of organizational dynamics and perceptions of local government autonomy in Nigeria political system. A comprehensive analysis of grade level distribution, departmental structure, and length of service among respondents forms the fulcrum of this study. The study unveils the diverse tapestry of influences shaping attitudes towards local government autonomy in Kogi State, Nigeria. Key findings highlight the pivotal role of mid-level professionals (grade levels 11 to 13), the varied priorities across departments, and the dynamic evolution of perceptions over time. Informed by these insights, our recommendations emphasized tailored strategies, leadership empowerment, and continuous engagement to foster an organizational environment that values and champions local government autonomy in Nigeria. This research contributes to a deeper understanding of the organizational intricacies influencing local governance and offers practical insights for policymakers and administrators seeking to enhance local government autonomy in Nigeria political systems.

Revealing trends and persistent cycles of non-autonomous systems with autonomous operator-theoretic techniques

An important problem in modern applied science is to characterize the behavior of systems with complex internal dynamics subjected to external forcings. Many existing approaches rely on ensembles to generate information from the external forcings, making them unsuitable to study natural systems where only a single realization is observed. A prominent example is climate dynamics, where an objective identification of signals in the observational record attributable to natural variability and climate change is crucial for making climate projections for the coming decades. Here, we show that operator-theoretic techniques previously developed to identify slowly decorrelating observables of autonomous dynamical systems provide a powerful means for identifying nonlinear trends and persistent cycles of non-autonomous systems using data from a single trajectory of the system. We apply our framework to real-world examples from climate dynamics: Variability of sea surface temperature over the industrial era and the mid-Pleistocene transition of Quaternary glaciation cycles.

Participatory planning in designing autonomous mobility systems: A design thinking approach

Participatory planning in designing autonomous mobility systems: A design thinking approach

Recent advances on control strategies for microgrid islanding operation

In order to achieve the national strategic goals of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, the electricity-energy-ecosystem is undergoing significant transformation, presenting new opportunities for the development of new energy generation technologies. With the widespread adoption of renewable energy sources, such as solar and wind energy, microgrids have begun to play a prominent role as small-scale power systems. Microgrids, as small, autonomous power systems, have received significant attention in recent years. Microgrids can enhance energy sustainability by integrating renewable energy resources and energy storage systems, while also providing the capability to maintain power supply during main grid failures or planned maintenance. This article aims to review the advances in control strategy research for microgrid islanding operation, with a focus on the classification of control strategies, design principles, and their impact on microgrid stability. The article also explores potential directions for future research to enhance the efficiency and reliability of microgrid islanding operation, providing valuable references for future studies.

Path to autonomous soil sampling and analysis by ground-based robots

Good site characterization is essential for the selection of remediation alternatives for impacted soils. The value of site characterization is critically dependent on the quality and quantity of the data collected. Current methods for characterizing impacted soils rely on expensive manual sample collection and off-site analysis. However, recent advances in terrestrial robotics and artificial intelligence offer a potentially revolutionary set of tools and methods that will help to autonomously explore natural environments, select sample locations with the highest value of information, extract samples, and analyze the data in real-time without exposing humans to potentially hazardous conditions. A fundamental challenge to realizing this potential is determining how to design an autonomous system for a given investigation with many, and often conflicting design criteria. This work presents a novel design methodology to navigate these criteria. Specifically, this methodology breaks the system into four components – sensing, sampling, mobility, and autonomy – and connects design variables to the investigation objectives and constraints. These connections are established for each component through a survey of existing technology, discussion of key technical challenges, and highlighting conditions where generality can promote multi-application deployment. An illustrative example of this design process is presented for the development and deployment of a robotic platform characterizing salt-impacted oil & gas reserve pits. After calibration, the relationship between the in situ robot chloride measurements and laboratory-based chloride measurements had a good linear relationship (R2-value = 0.861) and statistical significance (p-value = 0.003).

High-Speed Surface Property Recognition with a 140 GHz Frequency

In the field of integrated sensing and communication, there is a growing need for advanced environmental perception. The terahertz (THz) frequency band, significant for ultra-high-speed data connections, shows promise in environmental sensing, particularly in detecting surface textures crucial for autonomous systems’ decision-making. However, traditional numerical methods for parameter estimation in these environments struggle with accuracy, speed, and stability, especially in high-speed scenarios like vehicle-to-everything communications. This study introduces a deep learning approach for identifying surface roughness using a 140-GHz setup tailored for such conditions. A high-speed data acquisition system was developed to mimic real-world scenarios, and a diverse set of rough surface samples was prepared for realistic high-speed datasets to train the models. The model was trained and validated in three challenging scenarios: random occlusions, sparse data, and narrow-angle observations. The results demonstrate the method’s effectiveness in high-speed conditions, suggesting terahertz frequencies’ potential in future sensing and communication applications.

Sustainable CO2 Refrigeration System for Fish Cold Storage Facility Using a Renewable Integrated System with Solar, Wind and Tidal Energy for Cape Verde—Analyzing Scenarios

This study compares four feasible alternative solutions for an integrated cold storage system in the city of Tarrafal, Santiago, Cape Verde. Integrated systems using grid electricity are compared with autonomous systems generating electrical energy from renewable sources, alongside various types of refrigeration facility systems. Its objective is to assess the energy efficiency, financial feasibility, and environmental impact across four scenarios. Scenario 1 utilizes two R134a refrigeration units powered by the public grid. Scenario 2 employs a transcritical R744 (CO2) system using grid electricity. Scenario 3 incorporates R744 and autonomous renewable energy. Scenario 4 employs R744 for refrigeration with seawater heat exchange and autonomous renewable energy sources. The findings favor Scenario 4, emitting 15,882 kg CO2 eq with a 5-year return on investment. Autonomous electricity production in this scenario reduces emissions by 95%. Despite an initial cost of EUR 769,172.00, Scenario 3 demonstrates financial viability, contributing to energy sustainability. This autonomous production reduces emissions by 360,697 kg CO2 compared to conventional systems, highlighting the positive impact of local renewable energy integration.

Addressing corrigibility in near-future AI systems

AbstractWhen we discuss future advanced autonomous AI systems, one of the worries is that these systems will be capable enough to resist external intervention, even when such intervention is crucial, for example, when the system is not behaving as intended. The rationale behind such worries is that such intelligent systems will be motivated to resist attempts to modify or shut them down so they can preserve their objectives. To mitigate and face these worries, we want our future systems to be corrigible, i.e., to tolerate, cooperate or assist many forms of outside correction. One important reason for considering corrigibility as an important safety property is that we already know how hard it is to construct AI agents with a generalized enough utility function; and the more advanced and capable the agent is, the more it is unlikely that a complex baseline utility function built into it will be perfect from the start. In this paper, we try to achieve corrigibility in (at least) systems based on known or near-future (imaginable) technology, by endorsing and integrating different approaches to building AI-based systems. Our proposal replaces the attempts to provide a corrigible utility function with the proposed corrigible software architecture; this takes the agency off the RL agent – which now becomes an RL solver – and grants it to the system as a whole.

Multi-Device Security Application for Unmanned Surface and Aerial Systems

The use of autonomous and unmanned systems continues to increase, with uses spanning from package delivery to simple automation of tasks and from factory usage to defense industries and agricultural applications. With the proliferation of unmanned systems comes the question of how to secure the command-and-control communication links among such devices and their operators. In this work, we look at the use of the Messaging Layer Security (MLS) protocol, designed to support long-lived continuous sessions and group communication with a high degree of security. We build out MAUI—an MLS API for UxS Integration that provides an interface for the secure exchange of data between a ScanEagle unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) in a multi-domain ad-hoc network configuration, and experiment on system limits such as the ciphersuite set-up time and message handling rates. The experiments in this work were conducted in virtual and physical environments between the UAV, USV, and a controller device (all of different platforms). Our results demonstrate the viability of capitalizing on MLS’s capabilities to securely and efficiently transmit data for distributed communication among various unmanned system platforms.

Estimation of Soil Characteristic Parameters for Electric Mountain Tractor Based on Gauss–Newton Iteration Method

Future field work tasks will require mountain tractors to pass through rough terrain with limited human supervision. The wheel–soil interaction plays a critical role in rugged terrain mobility. In this paper, an algorithm for the estimation of soil characteristic parameters based on the Simpson numerical integration method and Gauss–Newton iteration method is presented. These parameters can be used for passability prediction or in a traction control algorithm to improve tractor mobility and to plan safe operation paths for autonomous navigation systems. To verify the effectiveness of the solving algorithm, different initial values and soils were selected for simulation calculations of soil characteristic parameters such as internal friction angle, settlement index, and the joint parameter of soil cohesion modulus and friction modulus. The results show that the error was kept within 2%, and the calculation time did not exceed 0.84 s, demonstrating high robustness and real-time performance. To test the applicability of the algorithm model, further research was conducted using different wheel parameters of electric mountain tractors under wet clay conditions. The results show that these parameters also have high accuracy and stability with only a few iterations. Thus, the estimation algorithm can meet the requirements of quickly and accurately identifying soil characteristic parameters during tractor operation. A criterion for the passability of wheeled tractors through unknown terrain is proposed, utilizing identified soil parameters.

Learning an Autonomous Dynamic System to Encode Periodic Human Motion Skills.

Learning an autonomous dynamic system (ADS) encoding human motion rules has been shown as an effective way for human motion skills transfer. However, most existing approaches focus on goal-directed motion skills transfer, and the study on periodic motion skills transfer is rare. One popular approach for periodic motion skills transfer is learning periodic dynamic movement primitive (DMP); however, periodic DMP is sensitive to spatial disturbances due to the introduction of the phase parameters. To solve this issue, this brief presents a novel approach to learn an ADS with a stable limit cycle without introducing phase parameters. First, a data-driven Lyapunov function (energy function) is learned, such that one of its level surfaces is consistent with periodic human demonstration trajectories. Then, an ADS is learned by sequentially solving energy function-related constrained optimization problems. With a proper design of constraint functions, we can ensure that the trajectory generated by the ADS will converge to an energy function-level surface, of which the shape is similar to periodic human demonstration trajectories. Experiments are conducted to show the effectiveness of the proposed approach (PA).

Optimizing Ambiance: Intelligent RGB Lighting Control in Structures Using Fuzzy Logic

Managing red–green–blue (RGB) lighting conditions within structures may evoke emotions and positively influence behavior. Intelligent RGB lighting systems based on environmental data measurements can substantially enhance the perception of comfort. This study presents a challenge that requires a holistic and integrated approach to implement an automatic RGB artificial lighting control system that can be utilized in various structures and indoor environments. Initially, the challenge spans the identification of environmental variables directly impacting comfort up to the careful selection of suitable sensors. The result is the development of a sophisticated and autonomous system that can adjust RGB lighting in real time, creating environments that are both comfortable and energy-efficient. This automated system fosters the creation of appropriate atmospheres across different contexts. The identification and monitoring of environmental variables are achieved through a neuro-fuzzy control mechanism, where fuzzy rules and membership functions are defined based on late positive potential timings and the influence of artificial lighting on human emotions. The outcomes from this study are an interconnected system capable of performing both online and offline operations to enable the monitoring of environmental variables and the efficient management of artificial lighting based on these metrics. A pilot study, with reference to an EEG wave registry system, yielded significant results. These tests had a statistically relevant result with an average frequency of approximately 9.8 Hz, indicative of a state of comfort among people. Despite a 10% deviation margin, 87% of measurements during the test remained consistent. This research study contributes to human behavior by fostering a relaxing environment and enabling a reduction in energy consumption through the use of efficient lighting. Moreover, the environment intention enables the creation of stimuli in three emotional states: activation, relaxation, and neutral, allowing behavioral adaptation to an intention to occur automatically in fluctuating environmental conditions.

Multiscale Effects of Predator–Prey Systems with Holling-III Functional Response

In this paper, we proposed a Holling-III predator–prey model considering the perturbation of slow-varying, carrying capacity parameters. The study aims to address how the slow changes in carrying capacity influence the dynamics of the model. Based on the bifurcation theory and the slow–fast analysis method, the existence and the equilibrium of the autonomous system are explored, and then, the critical condition of Hopf bifurcation and transcritical bifurcation is established for the autonomous system. The slow–fast coupled nonautonomous system has quasiperiodic oscillations, single Hopf bursting oscillations, and transcritical–Hopf bursting oscillations within a certain range of perturbation amplitude variation if the carrying capacity perturbation amplitude crosses some critical values, such that the predator–prey management is challenging for the extinction of predator populations under the critical value. The motion pattern of the nonautonomous system is closely related to the transcritical bifurcation, Hopf bifurcation and attractor type of the autonomous system. Finally, the effects of changes in parameters related to predator aggressiveness on system behavior are investigated. These results show how crucial the predator–prey control is for varying carrying capacities.

Multifidelity topology design of a maritime survey operation with UUVs

Advances in autonomous systems, maritime communications, and sensing technologies lead to increasing applications of unmanned underwater vehicles (UUVs). In this paper, we study a maritime survey operation topology design problem with UUVs that traverse an ocean environment and collect data from prespecified sensors or locations. This maritime scenario is analyzed via several models and simulation for the purpose of topology design for mission planning. We use a multifidelity approach to examine the trade-offs between different potential topology configurations of assigning UUVs to data collection sensors or locations. We develop three low-fidelity models that make simplifying assumptions. These models provide insight into the design characteristics and allow for sensitivity analysis with low computational cost. They are used to down-select potential configuration designs for further evaluation using a high-fidelity simulation model. A high-fidelity simulation model removes many simplifying assumptions and predicts how a topology design would perform under more realistic conditions. It gathers detailed performance metrics at the expense of higher computational cost. Our study uses this multifidelity approach to demonstrate key trade-offs for topology design. The optimal design of UUVs depends on mission-specific goals.

Autonomous and Intelligent Mobile Multimedia Cyber-Physical System with Secured Heterogeneous IoT Network

Autonomous and Intelligent Mobile Multimedia Cyber-Physical System with Secured Heterogeneous IoT Network

Role of NaV1.7 in postganglionic sympathetic nerve function in human and guinea-pig arteries.

NaV1.7 plays a crucial role in inducing and conducting action potentials in pain-transducing sensory nociceptor fibres, suggesting that NaV1.7 blockers could be effective non-opioid analgesics. While SCN9A is expressed in both sensory and autonomic neurons, its functional role in the autonomic system remains less established. Our single neuron rt-PCR analysis revealed that 82% of sympathetic neurons isolated from guinea-pig stellate ganglia expressed NaV1.7 mRNA, with NaV1.3 being the only other tetrodotoxin-sensitive channel expressed in approximately 50% of neurons. We investigated the role of NaV1.7 in conducting action potentials in postganglionic sympathetic nerves and in the sympathetic adrenergic contractions of blood vessels using selective NaV1.7 inhibitors. Two highly selective NaV1.7 blockers, GNE8493 and PF05089771, significantly inhibited postganglionic compound action potentials by approximately 70% (P<0.01), with residual activity being blocked by the NaV1.3 inhibitor, ICA121431. Electrical field stimulation (EFS) induced rapid contractions in guinea-pig isolated aorta, pulmonary arteries, and human isolated pulmonary arteries via stimulation of intrinsic nerves, which were inhibited by prazosin or the NaV1 blocker tetrodotoxin. Our results demonstrated that blocking NaV1.7 with GNE8493, PF05089771, or ST2262 abolished or strongly inhibited sympathetic adrenergic responses in guinea-pigs and human vascular smooth muscle. These findings support the hypothesis that pharmacologically inhibiting NaV1.7 could potentially reduce sympathetic and parasympathetic function in specific vascular beds and airways. KEY POINTS: 82% of sympathetic neurons isolated from the stellate ganglion predominantly express NaV1.7 mRNA. NaV1.7 blockers inhibit action potential conduction in postganglionic sympathetic nerves. NaV1.7 blockade substantially inhibits sympathetic nerve-mediated adrenergic contractions in human and guinea-pig blood vessels. Pharmacologically blocking NaV1.7 profoundly affects sympathetic and parasympathetic responses in addition to sensory fibres, prompting exploration into the broader physiological consequences of NaV1.7 mutations on autonomic nerve activity.

Harnessing Green Electricity from Food: A Split Black Gram-Based Triboelectric Nanogenerator for a Self-Powered Autonomous Lighting System and Portable Electronics.

Triboelectric nanogenerators (TENGs) represent a promising solution to mounting environmental concerns associated with battery disposal amid the escalating demand for portable electronics. However, prevailing TENG fabrication predominantly relies on nonbiodegradable, nonbiocompatible, and synthetic materials, posing a grave ecological threat. To mitigate this, there is a pressing need to develop eco-friendly and green TENGs leveraging sustainable, naturally occurring materials. This study pioneers the use of split black gram (SBG) as a tribo-positive material for TENGs. SBG's effectiveness as a tribo-positive material stems from its abundance of oxygen-containing functional groups, as confirmed by FTIR analysis, facilitating electron donation during the triboelectric process. SBG offers compelling advantages, including widespread availability, cost-effectiveness, biodegradability, and hydrophobic and adhesive properties due to its richness in starch and protein, positioning it as an optimal choice for eco-conscious TENG manufacturing. The fabrication process of an SBG-TENG is not only economical and facile but also solvent-free, requiring no specialized tools. Demonstrating commendable performance, the SBG-TENG achieves a maximum power density of 15.36 μW/cm2 at 1 MΩ, with an open circuit voltage of 84 V and short circuit current of 28 μA, comparable to recent studies. In practical applications, the SBG-TENG seamlessly integrates with LEDs and portable electronic devices via a full bridge rectifier, successfully powering them postcapacitor charging. Moreover, an autonomous lighting system is developed by embedding the SBG-TENG in a foot mat, enabling wireless light control through human stepping on the mat, introducing power-saving functionality for residential and office environments. In essence, the introduction of the SBG-TENG not only delivers cost-effectiveness but also minimizes the environmental impact by harnessing sustainable energy from food sources.

Impact of Artificial Intelligence on Indian Banking Sector- A Study of Banks

Artificial intelligence, or AI, is a cross-disciplinary approach to understanding, modeling, and creating intelligence of various forms. It is a critical branch of cognitive science, and its influence is increasingly being felt in other areas, including the humanities. Intelligence might be defined as the ability to learn and perform suitable techniques to solve problems and achieve goals, appropriate to the context in an uncertain, ever-varying world. A fully pre-programmed factory robot is flexible, accurate, and consistent but not intelligent. Artificial Intelligence (AI), a term coined by emeritus Stanford Professor John McCarthy in 1955, was define as “the science and engineering of making intelligent machines”. Much research has humans program machines to behave in a clever way, like playing chess, but, today, people emphasize machines that can learn, at least somewhat like human beings do. Autonomous systems can independently plan and decide sequences of steps to achieve a specified goal without micro-management. A hospital delivery robot must autonomously navigate busy corridors to succeed in its task. In AI, autonomy doesn’t have the sense of being self-governing common in politics or biology.

ADDGCN: A Novel Approach with Down-Sampling Dynamic Graph Convolution and Multi-Head Attention for Traffic Flow Forecasting

An essential component of autonomous transportation system management and decision-making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a difficult undertaking because of the intricate spatio-temporal relationships involved. Existing techniques often employ separate modules to model spatio-temporal features independently, thereby neglecting the temporally and spatially heterogeneous features among nodes. Simultaneously, many existing methods overlook the long-term relationships included in traffic data, subsequently impacting prediction accuracy. We introduce a novel method to traffic flow forecasting based on the combination of the feature-augmented down-sampling dynamic graph convolutional network and multi-head attention mechanism. Our method presents a feature augmentation mechanism to integrate traffic data features at different scales. The subsampled convolutional network enhances information interaction in spatio-temporal data, and the dynamic graph convolutional network utilizes the generated graph structure to better simulate the dynamic relationships between nodes, enhancing the model’s capacity for capturing spatial heterogeneity. Through the feature-enhanced subsampled dynamic graph convolutional network, the model can simultaneously capture spatio-temporal dependencies, and coupled with the process of multi-head temporal attention, it achieves long-term traffic flow forecasting. The findings demonstrate that the ADDGCN model demonstrates superior prediction capabilities on two real datasets (PEMS04 and PEMS08). Notably, for the PEMS04 dataset, compared to the best baseline, the performance of ADDGCN is improved by 2.46% in MAE and 2.90% in RMSE; for the PEMS08 dataset, compared to the best baseline, the ADDGCN performance is improved by 1.50% in RMSE, 3.46% in MAE, and 0.21% in MAPE, indicating our method’s superior performance.