System Reliability Research Articles

Low-cost, portable, easy-to-use kiosks to facilitate home-cage testing of non-human primates during vision-based behavioral tasks.

Non-human primates (NHPs), especially rhesus macaques, have significantly contributed to our understanding of the neural computations underlying human vision. Besides the established homologies in the visual brain areas between these species and our ability to probe detailed neural mechanisms in monkeys at multiple scales, NHPs' ability to perform human-like visual behavior makes them an extremely appealing animal model of human vision. Traditionally, such behavioral studies have been conducted in controlled laboratory settings, offering experimenters tight control over variables like luminance, eye movements, and auditory interference. However, in-lab experiments have several constraints, including limited experimental time, the need for dedicated human experimenters, additional lab space requirements, invasive surgeries for head-post implants, and extra time and training for chairing and head restraints. To overcome these limitations, we propose adopting home-cage behavioral training and testing of NHPs, enabling the administration of many vision-based behavioral tasks simultaneously across multiple monkeys with reduced human personnel requirements, no NHP head restraint, and monkeys' unrestricted access to experiments. In this article, we present a portable, low-cost, easy-to-use kiosk system developed to conduct home-cage vision-based behavioral tasks in NHPs. We provide details of its operation and build to enable more open-source development of this technology. Furthermore, we present validation results using behavioral measurements performed in the lab and in NHP home cages, demonstrating the system's reliability and potential to enhance the efficiency and flexibility of NHP behavioral research.

Teleoperated Driving with Virtual Twin Technology: A Simulator-Based Approach

This study introduces an innovative Teleoperated Driving (ToD) system integrated with virtual twin technology using the MORAI simulator. The system minimizes the need for extensive video data transmission by utilizing text-based vehicle information, significantly reducing the communication load. Key technical advancements include the use of high-precision GNSS devices for accurate vehicle location tracking, robust data communication via the MQTT protocol, and the implementation of the Ego Ghost mode in the MORAI simulator for precise vehicle simulation. The integration of these technologies enables efficient data transmission and enhanced system reliability, effectively mitigating issues such as communication blackouts and delays. Our findings demonstrate that this approach ensures stable and efficient operation, optimizing communication resource management and enhancing operational stability, which is crucial for scenarios requiring high video quality and real-time response. This research represents a significant advancement in ToD technology, establishing a precedent for integrating virtual twin systems to create more resource-efficient and reliable autonomous driving backup solutions. The virtual twin-based ToD system provides a robust platform for remote vehicle operation, ensuring safety and reliability in various driving conditions.

KNOWLEDGE, ATTITUDE, AND PRACTICE REGARDING ARTIFICIAL INTELLIGENCE (AI) AND ITS USAGE IN DENTAL ACADEMICS CURRICULUM AMONG DENTAL UNDERGRADUATES AND POSTGRADUATES

Background: Artificial intelligence (AI) is increasingly being recognized for its potential to revolutionize dental education and practice. However, the extent of knowledge, attitudes, and practices (KAP) regarding AI among dental undergraduates and postgraduates remains underexplored. Objectives: This study aims to assess the knowledge, attitudes, and practices related to AI and its integration into the dental academic curriculum among dental undergraduates and postgraduates. Methods: A cross-sectional questionnaire-based study was conducted among 545 participants, including final-year dental undergraduates and postgraduate dental students . The structured questionnaire covered demographics, knowledge of AI, attitudes towards its integration into dental education, and current practices involving AI. Results: The study found significant variations in knowledge levels, with postgraduates demonstrating a more comprehensive understanding of AI applications in dentistry compared to undergraduates. Attitudes towards AI were generally positive across both groups, with participants recognizing its potential to enhance diagnostic accuracy and streamline administrative tasks. However, there were concerns about the reliability of AI systems and the potential for reduced human oversight. Practical application of AI was limited, particularly among undergraduates, highlighting a gap between theoretical knowledge and practical use. Conclusion: The study underscores the need for integrating comprehensive AI education into the dental curriculum, encompassing both theoretical knowledge and practical training. Continuous professional development and a balanced approach to AI use in dental practice are essential to prepare future dental professionals for an AI-driven healthcare landscape. Gender-wise, the survey reveals consistent support across male and female dental professionals for AI's potential in enhancing dental research, with minimal variation in beliefs. KEYWORDS: Artificial intelligence, dental education, knowledge, attitudes, practices, dental undergraduates, postgraduates.

Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation

AbstractThe increasing global need for renewable energy sources, driven by environmental concerns and the limited availability of traditional energy, highlights the significance of solar energy. However, weather fluctuations challenge the efficiency of solar systems, making maximum power point tracking (MPPT) systems crucial for optimal energy harvesting. This study compares ten MPPT approaches, including both conventional and artificial intelligence (AI)‐based techniques. These controllers were designed and implemented using MATLAB Simulink, and their performance was evaluated under real environmental conditions with fluctuating irradiance and temperature. The results demonstrate that conventional techniques, such as incremental conductance (INC), Perturb and Observe (P&O), Incremental conductance and Particle Swam Optimization (INC‐PSO), Fuzzy Logic Control and Particle Swam Optimization (FLC‐PSO), and Perturb and Observe and Particle Swam Optimization (P&O‐PSO), achieved accuracies of 94%, 97.6%, 98.9%, 98.7%, and 99.3% respectively. In contrast, AI‐based intelligent techniques, including Artificial Neural Network (ANN), Artificial Neural Fuzzy Interference System (ANFIS), Fuzzy Logic Control (FLC), Particle Swam Optimization (PSO), and Artificial Neural Network and Particle Swam Optimization (ANN‐PSO), outperform achieving higher accuracies of 97.8%, 99.9%, 98.9%, 99.2%, and 99%, respectively. Compared to available research, which often reports lower accuracies for conventional techniques, our study highlights the enhanced performance of AI‐based methods. This study provides a comprehensive comparative analysis, delivering critical analysis and practical guidance for engineers and researchers in selecting the most effective MPPT controller optimized to specific environmental conditions. By improving the efficiency and reliability of solar power systems, our research supports the advancement of sustainable energy solutions.

A Survey on Anomalies and Faults That May Impact the Reliability of Renewable-Based Power Systems

The decarbonization of the electricity grid is one of the actions that can help reduce fossil fuel emissions, and thus their impact on global warming in the future. This decarbonization will be achieved mainly through the integration and widespread diffusion of renewable power sources. This is also going to be supported by the shift from the paradigm of production–transmission–distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will very likely reduce the reliability of the grid, unless appropriate countermeasures are taken/implemented, hopefully at the same time that the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might possibly affect some of the central renewable (wind, PV, hydrogen) and ancillary technologies that will be used to establish future renewable-based power systems. Accordingly, this paper presents a literature survey, also extending the focus to related datasets that can be used for deeper investigation. It is highlighted that the gaps mainly refer to a lack of a common taxonomy that prevents the establishment of structured knowledge in the scope of renewable-based power systems, a lack of contributions to anomalies/faults specific to wind turbines, and a lack of datasets related to electrolyzers, fuel cells, DC/x conversion, and monitoring and communication systems. Further, in the case of monitoring and communication systems, the scientific literature is both very dated, therefore not considering possible new aspects that would be currently worthy of investigation, and not oriented toward the particular domain addressed, thus considering peculiar aspects that are left out.

Evaluation of the effect of agroclimatic variables on the probability and timing of olive fruit fly attack

Agroclimatic variables may affect insect and plant phenology, with unpredictable effects on pest populations and crop losses. Bactrocera oleae Rossi (Diptera: Tephritidae) is a specific pest of Olea europaea plants that can cause annual economic losses of more than one billion US dollars in the Mediterranean region. In this study, we aimed at understanding the effect of olive tree phenology and other agroclimatic variables on B. oleae infestation dynamics in the Umbria region (Central Italy). Analyses were carried out on B. oleae infestation data collected in 79 olive groves during a 7-year period (from 2015 to 2021). In July–August, B. oleae infestation (1% attack) was negatively affected by altitude and spring mean daily temperatures and positively by higher winter mean daily temperatures and olive tree cumulative degree days. In September–October, infestation was negatively affected by a positive soil water balance and high spring temperatures. High altitude and cumulative plant degree days were related to delayed attacks. In contrast, high winter and spring temperatures accelerated them. Our results could be helpful for the development of predictive models and for increasing the reliability of decision support systems currently used in olive orchards.

System-level operational cyber risks identification in industrial control systems

ABSTRACT In Industrial Control Systems (ICS), where complex interdependencies abound, cyber incidents can have far-reaching consequences. Dependency modelling, a valuable technique for assessing cyber risks, aims to decipher relationships among variables. However, its effectiveness is often hampered by limited data exposure, hindering the analysis of direct and indirect impacts. We present a unique method that transforms dependency modelling data into a Bayesian Network (BN) structure and leverages causality and reasoning to extract inferences from seemingly unrelated events. Using operational ICS data, we confirm our method enables stakeholders to make better decisions about system security, stability, and reliability.

A functional data‐driven method for modeling degradation of waxy lubrication layer

AbstractWax is a prevalent lubrication material extensively employed in various engineering applications. Understanding the degradation characteristics of the waxy lubrication layer under diverse stress variables and levels is crucial for ensuring system security and reliability. Due to the unclear mechanism governing the degradation of the waxy lubrication layer under different stress variables, existing degradation models are unsuitable for modeling waxy lubrication layer degradation data. To address this challenge, we propose a functional data‐driven method leveraging dense observations of waxy degradation. Through extensive simulations and a case study, we demonstrate the superior performance and effectiveness of the proposed approach.

Deep Learning for Anomaly Detection in IoT Systems: Techniques, Applications, and Future Directions

The proliferation of Internet of Things (IoT) devices has revolutionized various sectors by enabling real-time data collection and processing, leading to unprecedented levels of efficiency and convenience. However, this increased connectivity and data flow also introduce significant security risks, particularly in the realm of anomaly detection. This study delves into the use of deep learning techniques for identifying abnormalities in IoT systems, providing a thorough analysis of state-of-the-art methods, assessing their effectiveness across different IoT scenarios, and exploring future research directions. We review current deep learning-based anomaly detection techniques, examine their applications in real-world settings, and discuss potential improvements and innovations. By synthesizing the latest research and developments, this paper aims to offer a comprehensive understanding of how deep learning can bolster the security and performance of IoT systems. Our findings highlight the importance of robust anomaly detection mechanisms in safeguarding IoT networks and underscore the need for continued advancements in this area. Through this study, we seek to contribute valuable insights into the application of deep learning for enhancing IoT system security and reliability, ultimately supporting the sustainable growth and integration of IoT technologies across various domains.

Intelligent optimal sitting and sizing of distributed resources in presence of renewable energy and fuel cells based on reliability and emission factors

As the integration of renewable energy sources—such as wind, solar, and fuel cells—into power systems increases, challenges related to system reliability and cost efficiency become paramount. This paper examines the reliability of a standard power network composed of diverse energy sources including wind turbines, solar panels, fuel cells, batteries, and diesel generators. This paper proposes a novel hybrid optimization approach utilizing a Grey Wolf Optimizer combined with another advanced algorithm to minimize the total cost of energy production, inclusive of emission penalties for CO2, NO2, and SO2. The proposed approach is tested on IEEE 57-bus and IEEE 118-bus systems, demonstrating significant improvements over existing algorithms. Quantitative results show that our method reduces the total cost by up to 18 % and enhances system reliability by reducing voltage deviations in various scenarios by 12 %. The paper also provides insights into the optimal sizing and placement of wind turbines, photovoltaics, diesel generators, and fuel cells under different emission constraints (15000, 25000, and 45000 kg), further emphasizing the flexibility and effectiveness of our method in addressing environmental and economic challenges in modern power systems.

The Concurrent Validity and Reliability of a Global Positioning System for Measuring Maximum Sprinting Speed and Split Times of Linear and Curvilinear Sprint Tests

This study investigated the reliability and validity of linear sprint tests (LS) and curvilinear sprint tests (CS) using a GPS device (GPEXE lt). Twenty-one (21) student athletes completed a 40 m LS as well as a left and right CS. Maximum sprint speed (MSS) and split times at short (0–10 m), intermediate (0–20 m), and long (0–30 m) distances were determined using the GPEXE lt and single-beam timing gates (TG). Intrasession reliability and concurrent validity of GPEXE lt were assessed. The GPEXE lt consistently showed high reliability for MSS (ICC ≥ 0.95; CV ≤ 1.8%), despite underestimation compared to TG (bias = −2.57 to −0.95%; ICC ≥ 0.87). Acceptable reliability was observed for CS and LS intermediate and long distance split times (ICC ≥ 0.91; CV ≤ 2.1%), with lower reliability for short distances (ICC ≥ 0.70; CV ≤ 3.6%). The GPEXE lt split times for CS and LS showed good agreement with TG (ICC ≥ 0.66), but were overestimated at long distances (5.5–9.7%) and short distances (11.1–14.6%). Although the MSS was found to be the most reliable and valid variable to assess LS and CS speed performance with GPEXE lt, caution is needed due to limited validity.

Event-Triggered Collaborative Fault Diagnosis for UAV–UGV Systems

The heterogeneous unmanned system, which is composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV), has been broadly applied in many domains. Collaborative fault diagnosis (CFD) among UAVs and UGVs has become a key technology in these unmanned systems. However, collaborative fault diagnosis in unmanned systems faces the challenges of the dynamic environment and limited communication bandwidth. This paper proposes an event-triggered collaborative fault diagnosis framework for the UAV–UGV system. The framework aims to achieve autonomous fault monitoring and cooperative diagnosis among unmanned systems, thus enhancing system security and reliability. Firstly, we propose a fault trigger mechanism based on broad learning systems (BLS), which utilizes sensor data to accurately detect and identify faults. Then, under the dynamic event triggering mechanism, the network communication topology between the UAV–UGV system and BLS is used to achieve cooperative fault diagnosis. To validate the effectiveness of our proposed scheme, we conduct experiments on a software-in-the-loop (SIL) simulation platform. The experimental results demonstrate that our method achieves high diagnosis accuracy for the UAV–UGV system.

Методологічний підхід до розв’язання проблеми надійності функціонування автомобільних транспортних систем

An analysis of methods and techniques for solving the problem of increasing and ensuring the appropriate level of reliability of technical systems was carried out, which made it possible, based on the use of analogies, to find out the directions for ensuring the reliability of automobile transport systems. The essence of the concept of the reliability of the functioning of automobile transport systems for the transportation of goods and passengers from a methodological point of view is clarified. The insufficiency of regulatory and legal documentation for the development of methods and techniques for assessing the reliability of transport systems is substantiated. The synthesis of the concepts of "management" and "reliability" of the functioning of transport systems in comparison with similar concepts of technical systems is considered. The expert method and methodology of expert assessment of the reasons for failures of the production subsystem of the motor vehicle enterprise and its services are considered. It was found that factor analysis in combination with expert assessment makes it possible to assess the significance of the reasons for failure of transport systems and their frequency. In accordance with the classification of failures of technical systems, an analysis of their belonging to automobile transport systems was carried out. The classification of failures of technical systems according to different sets of features is analyzed. It was established that the classification of failures of automobile transport systems is based on the use of analogy with the classification of failures of technical systems, as well as on the results of the analysis of the factors of the causes of failures that occur in the production system of the carrier's enterprise. It was revealed that the selection of significant factors affecting the failure of automobile transport systems is based on the method of expert evaluations, their ranking by importance was carried out, and their relative frequencies were calculated. The results of the factor analysis of the reasons for the failure of the production subsystem of the motor vehicle enterprise are given. It is assumed that by analogy with technical automobile transport systems, they are also subject to decomposition by stages of the life cycle. Therefore, a sudden failure of automobile transport systems is characterized by a sudden change in the values of one or more initial parameters agreed with the customer, and a gradual one occurs as a result of a slow, gradual deterioration of the functioning characteristics of the subsystem or the system as a whole or a participant in the transportation of goods and passengers.

Simulation-based system reliability estimation of a multi-state flow network for all possible demand levels

Simulation-based system reliability estimation of a multi-state flow network for all possible demand levels

A method for estimating the system reliability of ship grillage structure

A method for estimating the system reliability of ship grillage structure

ChatGPT: Advocacy or Opposition Through Mixed Methods

ABSTRACT Since its inception, ChatGPT has sparked debates regarding its benefits and drawbacks, which ultimately influence users’ decision-making about its usage. This study aims to investigate the effects of ChatGPT’s system reliability and utilitarian value on users’ behavioral responses. It employs a mixed-method technique comprising topic modeling analysis of 211,301 tweets and structural equation modeling using data collected from 210 full-time working professionals. The results demonstrate the significant influence of users’ perceptions of ChatGPT system reliability, which are shaped by perceived intelligence, system availability, immaturity, and privacy concerns. Additionally, the study reveals that ease of use and information quality have substantial effects on users’ perception of its utilitarian value, which, in conjunction with system reliability, contribute positively to users’ satisfaction. Furthermore, the findings affirm the positive effects of users’ satisfaction on their continuous use and recommendation intentions of ChatGPT to others. The findings provide theoretical and practical implications.

ChatGPT: Advocacy or Opposition Through Mixed Methods

ABSTRACT Since its inception, ChatGPT has sparked debates regarding its benefits and drawbacks, which ultimately influence users’ decision-making about its usage. This study aims to investigate the effects of ChatGPT’s system reliability and utilitarian value on users’ behavioral responses. It employs a mixed-method technique comprising topic modeling analysis of 211,301 tweets and structural equation modeling using data collected from 210 full-time working professionals. The results demonstrate the significant influence of users’ perceptions of ChatGPT system reliability, which are shaped by perceived intelligence, system availability, immaturity, and privacy concerns. Additionally, the study reveals that ease of use and information quality have substantial effects on users’ perception of its utilitarian value, which, in conjunction with system reliability, contribute positively to users’ satisfaction. Furthermore, the findings affirm the positive effects of users’ satisfaction on their continuous use and recommendation intentions of ChatGPT to others. The findings provide theoretical and practical implications.

A Novel High-Gain Switched-Capacitor Multilevel Inverter with Reduced Components for Grid Integration

This paper introduces a novel Multi-Level Inverter (MLI) design which utilizes a single input and leverages capacitor voltages source to generate a four-fold increase in output voltage as the main problem that stays with the inverters is their low boost ability and efficiency while maintaining power quality at the same time. One capacitor is charged to match the input voltage magnitude, while the other two capacitors store twice this magnitude. Through a series-parallel combination with switching operations, all capacitors are effectively charged and discharged within each cycle, ensuring natural voltage balance. A comprehensive comparative analysis is conducted to highlight the advantages of this innovative approach, particularly in terms of component reduction and mitigation of voltage stress. Detailed assessment of power losses within the proposed circuit is undertaken, simulation studies are first carried out while extensive experimentation verifies its operational efficiency under diverse conditions such as varying modulation indices, loads, and supply-side fluctuations with an impressive maximum efficiency of 96.9 % at a 200 W power rating, our research contributes to advancing compact power converters, addressing crucial challenges in modern power electronics applications, and paving the way for enhanced performance and reliability in such systems.

Forecasting the parameters of performance monitoring of complex technical systems

The problem of monitoring the performance of technical systems is considered, corresponding to the stage of final control during the production of a product, as well as the stage of testing the product before its use. Based on the generalization and development of the existing approaches, models and methods for monitoring the performance of complex technical systems were developed, and models for predicting the parameters of their control were proposed. Methods of optimizing the number of tests of units of technical systems when monitoring their performance were developed. Methods for estimating the optimal level of the total defect rate are proposed. The efficiency of the proposed approaches is illustrated using a specific numerical example. The methods and models presented in the article can be useful for engineering and technical workers of design bureaus, research institutes, research and production associations and enterprises in ensuring the reliability of products and technical systems.

Top three intelligent algorithms for OAM mode recognitions in optical communications

Abstract Vortex optical communication employing orbital angular momentum (OAM) has been a hot research field in recent years. Thanks to the orthogonality of the OAM, several multiplexing and modulation techniques have been developed that can effectively improve communication capacity. However, to achieve this, accurate mode recognition in the OAM-based free-space optical (FSO) communication system is essential. Generally, perturbations in the free space link significantly affect the transmission efficiency and distort the helical phase-front of OAM beams, which will result in intermodal crosstalk and poses a critical challenge in the recognition of OAM modes. To date, artificial intelligence (AI) technologies have been widely applied to address the aforementioned bottleneck of insufficient accuracy of existing techniques for OAM mode detection. Therefore, a review paper that discusses the recent developments and challenges of the most widely used AI algorithms for OAM mode recognition schemes, i.e., feedforward neural network (FNN), convolutional neural network (CNN), and diffractive deep neural networks (D2NN) is urgently required. By elaborating on the principles of these algorithms and analyzing recent reports, encompassing both experimental and simulated results, we established their profound importance in enhancing the accuracy of OAM mode recognition. Moreover, this work provides an outlook on the recent trends in this newly developed field and the critical challenges faced in effectively using AI for improving the reliability of the OAM-based FSO communication system in near future.