System Reliability Research Articles

A comprehensive investigation of an LTE-enabled smart door system using the Arduino UNO

Traditional residential security systems frequently lack remote access and real-time reaction capabilities, necessitating home automation security technology developments. With the rise of the Internet of Things (IoT), solutions such as the Smart Door are prepared to transcend these constraints by leveraging modern communication technology. The article aims to develop, implement, and evaluate an LTE-enabled Smart Door system built on the Arduino Uno platform. The goal is to improve home security by offering keyless entry, real-time surveillance, and remote operation, increasing user convenience and system reliability. We created a Smart Door system with an Arduino Uno microcontroller and LTE connectivity. The system's performance was measured using a variety of measures, including response time, reliability, and security against unwanted access. Testing included simulation environments and real-world scenarios to ensure the system's operational efficacy and security. The Smart Door system performed admirably, with a reaction time of less than two seconds in 95% of commands and strong resilience to brute force attacks. It effectively incorporated keyless entry, live video streaming, and real-time alarms, all controlled by a smartphone app, demonstrating the system's dependability and efficiency in live scenarios. The LTE-enabled Smart Door system considerably improves over traditional home security options. It provides enhanced security, simple operation, and more outstanding remote management capabilities by utilizing IoT technology. These enhancements fill essential holes in existing systems, providing a safer, dependable, and convenient answer for modern home security requirements.

Towards Double-Layer Dynamic Heterogeneous Redundancy Architecture for Reliable Railway Passenger Service System

Researchers have proposed the dynamic heterogeneous redundancy (DHR) architecture, which integrates dynamic, heterogeneous, redundant, and closed-loop feedback elements into the system, to fortify the reliability of the railway passenger service system (RPSS). However, there are at least two weaknesses with the common DHR architectures: (1) they need system nodes with enough computing and storage resources; (2) they have hardly considered the reliability of DHR architecture. To this end, this paper proposes a double-layer DHR (DDHR) architecture to ensure the reliability of RPSS. This architecture introduces a set of algorithms, which are optimized co-computation and ruling weight optimization algorithms for the data processing flow of the DDHR architecture. This set improves the reliability of the DDHR architecture. For the evaluation of the reliability of DDHR architecture, this paper also proposes two metrics: (1) Dynamic available similarity metric. This metric does not rely on the overall similarity of the double-layer redundant executor sets but evaluates the similarity of their performance under the specified interaction paths within a single scheduling cycle. The smaller its similarity, the higher its reliability. (2) Scheduling cycle under dual-layer similarity threshold. This metric evaluates the reliability of the RPSS under actual conditions by setting the schedulable similarity thresholds between the same and different layers of the dual-layer redundant executives in the scheduling process. Finally, analog simulation experiments and prototype system building experiments are carried out, whose numerical experimental results show that the DDHR architecture outperforms the traditional DHR architecture in terms of reliability and performance under different redundancy and dynamically available similarity thresholds, while the algorithmic complexity and multi-tasking concurrency performance are slightly weaker than that of the DHR architecture, but can be applied to the main operations of the RPSS in general.

Optimising structural health monitoring systems: A 2D numerical investigation on impedance matching for FML with integrated sensors

AbstractFibre metal laminates (FML) represent an innovative class of advanced composite materials that integrate the mechanical properties of both metals and fibre‐reinforced composites (FRP). Combining the strength and ductility of metals with the lightweight and high stiffness of FRP and FMLs have emerged as new material compositions for applications in chemical, nuclear, automobile, and aerospace engineering disciplines. Structural health monitoring (SHM) using guided ultrasonic waves (GUW) is the state‐of‐the‐art for non‐destructive testing of thin‐walled structures. When applied to FML, SHM plays a crucial role in monitoring the integrity over time and detecting potential damage such as delamination, fibre breakage, or other structural anomalies. In SHM with GUW, a wave‐field is emitted by actuators. This wave‐field can be affected by damage in the structure, thereby changing its propagation characteristics. Sensors monitor the interaction between damage and GUW, which can be utilized to locate and classify the damage and ascertain the overall health state of the structure. In this study, an advanced integration of measurement hardware, that is, sensors and actuators, within the laminate structure is investigated. Sensor integration into FML allows for improved and more sophisticated monitoring capabilities in comparison to measuring techniques like laser vibrometers, which are limited to measuring displacements on the surface of the structure. However, the integration of sensors and actuators yields the technical difficulty of distorting the wave‐fields and may result in an over‐ or underestimation of the damage. Similar to damage, the distortion of the wave‐field is caused by the changes in acoustic impedance resulting from different material properties. In a previous study, incorporating a functionally graded artificial interphase through acoustic impedance matching between the sensor and host material showed notable and significant outcomes. The current contribution extends the prior graded artificial interphase for an isotropic homogeneous material to an FML structure. This paper presents a comprehensive numerical simulation study on a two‐dimensional model of FML with integrated sensors. The interphases are designed based on impedance matching, which improves signal transmission and reduces disturbing reflections. The conducted investigations hold for several interphase configurations for a wide frequency range. The optimised integration of sensors demonstrates promising results for enhancing the reliability and accuracy of SHM systems. This research serves as a foundation for further experimental validation and the development of advanced sensor‐integrated FML structures with improved monitoring capabilities.

Network reliability evaluation and management science applications in resource allocation for police organizations

The effective allocation of resources within police patrol departments is crucial for maintaining public safety and operational efficiency. Traditional methods often fail to account for uncertainties and variabilities in police operations, such as fluctuating crime rates and dynamic response requirements. This study introduces a fuzzy multi-state network (FMSN) model to evaluate the reliability of resource allocation in police patrol departments. The model captures the complexities and uncertainties of patrol operations using fuzzy logic, providing a nuanced assessment of system reliability. Virtual data were generated to simulate various patrol scenarios. The model’s performance was analyzed under different configurations and parameter settings. Results show that resource sharing and redundancy significantly enhance system reliability. Sensitivity analysis highlights critical factors affecting reliability, offering valuable insights for optimizing resource management strategies in police organizations. This research provides a robust framework for improving the effectiveness and efficiency of police patrol operations under conditions of uncertainty.

Wavelet denoising analysis on vacuum-process monitoring signals of aerospace vacuum vessel structures

Abstract The monitoring of micro-defects or external actions induced vacuum degradation in aerospace vacuum vessels is an important challenge. A vacuum-process monitoring method based on quasi-distributed fiber Bragg grating (FBG) sensing technology is proposed. Due to the influence of environmental noise and vacuum pump operation noise, the raw signals measured by FBG sensors contain a large amount of noises, which affects the measurement accuracy and data analysis. Therefore, the wavelet threshold (WT) denoising method is proposed to analyze the influence of noises on the monitoring signals measured by two different kinds of FBG sensors. The evaluation of the monitoring signals after denoising indicates that the proposed method can effectively remove the noise and significantly improve signal quality. The highest signal-to-noise ratio of the processed signals can reach 37.61 dB and the mean square error is 3.68 × 10−7, while retaining the key features of the original signal. The proposed WT denoising method demonstrates better performance and feasibility compared with moving average filtering and Kalman filtering methods. The study provides critical supports for improving the performance and reliability of the vacuum vessel monitoring system.

A Novel Proposal in Wind Turbine Blade Failure Detection: An Integrated Approach to Energy Efficiency and Sustainability

This paper presents a novel methodology for detecting faults in wind turbine blades using computational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure.

Numerical Analysis of Leading-Edge Roughness Effects on the Aerodynamic Performance of a Thick Wind Turbine Airfoil

The aerodynamic performance of wind turbine airfoils is crucial for the efficiency and reliability of wind energy systems, with leading-edge roughness significantly impacting blade performance. This study conducts numerical simulations on the DU 00-W-401 airfoil to investigate the effects of leading-edge roughness. Results reveal that the rough airfoil exhibits a distinctive “N”-shaped lift coefficient curve. The formation mechanism of this nonlinear lift curve is primarily attributed to the development of the trailing-edge separation vortex and variations in the adverse pressure gradient from the maximum thickness position to the trailing-edge confluence. The impact of different roughness heights is further investigated. It is discovered that when the roughness height is higher than 0.3 mm, the boundary layer can be considered fully turbulent, and the lift curve shows the “N” shape stably. When the roughness height is between 0.07 mm and 0.1 mm, a transitional state can be observed, with several saltation points in the lift curve. The main characteristics of different flow regimes based on different lift curve segments are summarized. This research enhances the understanding of the effects of leading-edge roughness on the aerodynamic performance of a thick wind turbine airfoil, and the simulation method for considering the effect of leading-edge roughness is practical to be applied on large-scale wind turbine blade to estimate the aerodynamic performance under rough leading-edge conditions, thereby supporting advancements in wind turbine technology and promoting the broader adoption of renewable energy.

IoT-Based Wireless System for Gait Kinetics Monitoring in Multi-Device Therapeutic Interventions.

This study presents an IoT-based gait analysis system employing insole pressure sensors to assess gait kinetics. The system integrates piezoresistive sensors within a left foot insole, with data acquisition managed using an ESP32 board that communicates via Wi-Fi through an MQTT IoT framework. In this initial protocol study, we conducted a comparative analysis using the Zeno system, supported by PKMAS as the gold standard, to explore the correlation and agreement of data obtained from the insole system. Four volunteers (two males and two females, aged 24-28, without gait disorders) participated by walking along a 10 m Zeno system path, equipped with pressure sensors, while wearing the insole system. Vertical ground reaction force (vGRF) data were collected over four gait cycles. The preliminary results indicated a strong positive correlation (r = 0.87) between the insole and the reference system measurements. A Bland-Altman analysis further demonstrated a mean difference of approximately (0.011) between the two systems, suggesting a minimal yet significant bias. These findings suggest that piezoresistive sensors may offer a promising and cost-effective solution for gait disorder assessment and monitoring. However, operational factors such as high temperatures and sensor placement within the footwear can introduce noise or unwanted signal activation. The communication framework proved functional and reliable during this protocol, with plans for future expansion to multi-device applications. It is important to note that additional validation studies with larger sample sizes are required to confirm the system's reliability and robustness for clinical and research applications.

Operational reliability assessment of complex mechanical systems with multiple failure modes: An adaptive decomposition-synchronous-coordination approach

To effectively perform Complex Mechanical Systems Operational Reliability Assessment (CSORA) with multiple failures, the Adaptive Decomposition-Synchronous-Coordination Approach (ADSCA) is proposed by integrating Decomposition-Synchronous-Coordination strategy, adaptive modeling technology, surrogate model and multi-objective reliability assessment theory. In which, the main problem is decomposed into related sub-problems using a Decomposition-Synchronization-Coordination strategy, with sub-models coordinated to achieve synchronous mapping between multiple variables, thereby enhancing efficiency in large multi-subsystem problems. The adaptive modeling technique is adopted to construct a series of interrelated sub models based on the surrogate model as the basis function, improving performance. A multi-objective reliability assessment theory is adopted to achieve CSORA under multiple fault modes, where multiple weak links are integrated to comprehensively evaluate the reliability of complex systems. The significance of the ADSCA lies in its ability to manage complexity, enhance scalability, and improve the accuracy and efficiency of reliability assessments in complex mechanical systems. To demonstrate the effectiveness of the proposed approach, two illustrative examples are used. This includes approximation and probability analysis of nonlinear functions that exhibit multiple responses, as well as reliability analysis of temperature in civil aircraft braking system. The study can provide theoretical reference for the reliability evaluation of multi-failure operation in complex mechanical systems.

Continuous-time robust frequency regulation in isolated microgrids with decentralized fixed structure μ-synthesis and comparative analysis with PID and FOPID controllers

Isolated microgrids, which are crucial for supplying electricity to remote areas using local energy sources, have garnered increased attention due to the escalating integration of renewable energy sources in modern microgrids. This integration poses technical challenges, notably in mitigating frequency deviations caused by non-dispatchable renewables, which threaten overall system stability. Therefore, this paper introduces decentralized fixed structure robust μ-synthesis controllers for continuous-time applications, surpassing the limitations of conventional centralized controllers. Motivated by the increasing importance of microgrids, this work contributes to the vital area of frequency regulation. The research challenge involves developing a controller that not only addresses the identified technical issues but also surpasses the limitations of conventional centralized controllers. In contrast to their centralized counterparts, the proposed decentralized controllers prove more reliable, demonstrating enhanced disturbance rejection capabilities amidst substantial uncertainties, represented through normalized co-prime factorization. The proposed controllers are designed using the D-K iteration technique, incorporating performance weight filters on control actions to maintain low control sensitivity and ensure specific frequency band operation for each sub-system. Importantly, the design considers unstructured uncertainty up to 40%, addressing real-world uncertainties comprehensively. Rigorous robust stability and performance tests underscore the controller's superiority, demonstrating its robustness against elevated uncertainty levels. Robust stability is verified for all controllers, with the proposed controller showing robust stability against up to 171% of the modeled uncertainty. Notably, the controller boasts a fixed structure with lower order compared to other H-infinity controllers, enhancing its practical implementation. Comparative analyses against Coronavirus Herd Immunity Optimizer tuned Proportional-Integral-Derivative (CHIO-PID) controller and CHIO tuned Fractional-Order Proportional-Integral-Derivative (CHIO-FOPID) controller further validate the superior performance of the proposed solution, offering a significant step towards ensuring the stability and reliability of microgrid systems in the face of evolving energy landscapes.

Hybrid feedback control of PMSG-based WECS with multilevel flying-capacitor inverter enhanced by fractional order extremum seeking

This paper proposes an original hybrid control strategy for a three-phase multilevel flying capacitor inverter (FCI) used in wind energy conversion system (WECS). A state feedback law, computed using a Lyapunov function and an invariance principle, is designed to manage grid current control and capacitor voltage balancing, guaranteeing the asymptotic stability of the closed-loop system. Additionally, a fractional-order extremum seeking control (FOESC) algorithm for Maximum Power Point Tracking (MPPT) is introduced, which optimizes power capture by adjusting turbine speed from the DC side without requiring a detailed system model (model-free approach). The use of fractional-order calculus leads to improved convergence and more seamless performance while preserving the robust characteristics of the system. The proposed control strategies are validated through simulation, demonstrating improved transient response and stability under varying wind conditions and parameter uncertainties. These results highlight the potential of advanced control algorithms to enhance the efficiency and reliability of wind energy systems, contributing to global efforts to achieve sustainable energy goals.

Marine mesocosm system: A reliable tool for testing bioaccumulation and effects of seawater enrichment with dissolved iron in reef organisms

In 2015, a marine mesocosm facility was designed and implemented by the Coral Vivo Project in its research station (Porto Seguro, Bahia State, Brazil) to initially study the effects of global impacts, especially ocean warming and acidification, on coral reefs. However, local impacts, including seawater contamination with metal(loid)s, are considered as a major threat to coral reefs. Also, in 2015, the largest disaster involving a mining dam occurred in Brazil. Iron (Fe) mining tailings originated from the dam failure affected not only freshwater ecosystems (rivers, lakes and lagoons), but also adjacent beaches, mangroves, restingas, reefs and other marine systems. Seawater, sediments and biota were contaminated with metal(loid)s, especially Fe, arsenic (As), mercury (Hg) and manganese (Mn). Therefore, we aimed to adapt the marine mesocosm facility of the Coral Vivo Project to evaluate the bioaccumulation and biological impacts of increasing concentrations of dissolved Fe on a diversity of reef organisms. Results obtained indicate a great versatility and reliability of the marine mesocosm system for application in biological and ecological studies on the isolated effect of seawater dissolved Fe on reef organisms of different functional groups simultaneously.•Studies involving seawater enrichment with dissolved Fe can be performed using a marine mesocosm system.•The marine mesocosm is a reliable tool to study the isolated effects of metal(loid)s on reef organisms.

A novel forward performance-driven design method for gear parameters

Gear is one of the most crucial components of the transmission system, and the performance of gear directly affects the efficiency and reliability of the transmission system. Conventional methods for designing gear parameters involve several time-consuming and complex steps, which may not guarantee optimal performance. Therefore, we propose a new method for designing gear parameters that aims to improve efficiency and accuracy. First, the tooth surface equations of spur and helical involute gears suitable for symmetric and asymmetric teeth are deduced based on the gear-forming machining principle. Second, the performance evaluation models for load capacity, dynamic performance, efficiency, and power density of the gears are established based on the precise gear surface. The design objectives are standardized and evaluated comprehensively using a linear weighting method. Finally, a forward performance-driven design method of gear parameters is established. The proposed method is applied to a helical gear pair design case, and the results show that 90.7% of the individuals in the Pareto optimal front are asymmetric gears, with 9.3% being symmetric gears. This suggests that asymmetric gears have more opportunities to be optimal than symmetric gears. The highest-ranked gear designed using the proposed method is superior to the gear designed using conventional methods.

Efficient allocation of energy storage and renewable energy system for performance and reliability improvement of distribution system

This paper aims to investigate the impact of optimal planning of renewable energy systems and independent battery energy storage systems (BESS) on the performance and reliability of distribution systems (DS). A unique energy management scheme (EMS) controls the charging and discharging schedules of the BESS. The transit search optimization (TSO) algorithm determines the optimal allocation of wind turbine (WT) and solar photovoltaic (SPV) generator units, along with BESS units. The optimization focuses on improving the DS reliability in terms of energy supply, increasing the feeder security margin, and reducing apparent power loss. Multiple case studies are designed to study the efficacy of the problem outcomes and are validated using Bus 4 of the Roy Bollington Test System (RBTS). To obtain results that are close to actual operating conditions, the modelling process takes into account the time-varying load profile and the dynamic power outputs from the SPV and WT units. The findings show that the suggested method works well by enhancing the feeder security margin by a maximum of 11.25%, reducing expected energy not supplied (EENS) by 4.3%, and improving the power loss profile by 11.5%. In addition, the feeder upgrade deferral (FUD) years also show a maximum improvement of 20.8%.

Dynamic analysis of a novel hyperchaotic system based on STM32 and application in image encryption

This paper presents a novel 4D hyperchaotic system derived from a modified 3D Lorenz chaotic system. A key aspect of this system is the presence of a single equilibrium point, and its stability is carefully analyzed. The dynamic properties, including the Lyapunov exponent spectrum, bifurcation diagram, and chaotic attractors, are investigated using MATLAB simulations. The results reveal that the system displays hyperchaotic behavior across a wide range of the parameter d\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d$$\\end{document}, with its chaotic attractor transitioning through four states: hyperchaotic, chaotic, periodic, and quasi-periodic, showcasing the system's complex dynamics. Experimental validation using STM32 embedded hardware successfully reproduces these four types of chaotic attractors, confirming the theoretical predictions. The proposed hyperchaotic system is then applied to image encryption, introducing a novel encryption method. The hyperchaotic key sequence generated by this system meets 15 tests of the NIST SP800-22 standard, and further experimental validation with STM32 hardware demonstrates the algorithm's effectiveness, simplicity, non-linearity, and high security. The encrypted images and sequences are rigorously tested key space analysis, histogram similarity analysis, information entropy analysis, statistical attack analysis, differential attack analysis, key sensitivity analysis, and correlation analysis, highlighting the robustness and reliability of the proposed system. This method is versatile and can be extended to various fields, including audio and video encryption, text encryption, IoT security, financial transaction security, and medical data protection.

An evaluation method of construction reliability of cable system of cable-stayed bridge based on Bayesian network

PurposeAs an important load-bearing component of cable-stayed bridge, the cable-stayed cable is an important load-bearing link for the bridge superstructure and the load transferred directly to the bridge tower. In order to better manage the risk of the cable system in the construction process, the purpose of this paper is to study a new method of dynamic risk analysis of the cable system of the suspended multi-tower cable-stayed bridge based on the Bayesian network.Design/methodology/approachFirst of all, this paper focuses on the whole process of the construction of the cable system, analyzes the construction characteristics of each process, identifies the safety risk factors in the construction process of the cable system, and determines the causal relationship between the risk factors. Secondly, the prior probability distribution of risk factors is determined by the expert investigation method, and the risk matrix method is used to evaluate the safety risk of cable failure quantitatively. The function expression of risk matrix is established by combining the probability of risk event occurrence and loss level. After that, the topology structure of Bayesian network is established, risk factors and probability parameters are incorporated into the network and then the Bayesian principle is applied to update the posterior probability of risk events according to the new information in the construction process. Finally, the construction reliability evaluation of PAIRA bridge main bridge cable system in Bangladesh is taken as an example to verify the effectiveness and accuracy of the new method.FindingsThe feasibility of using Bayesian network to dynamically assess the safety risk of PAIRA bridge in Bangladesh is verified by the construction reliability evaluation of the main bridge cable system. The research results show that the probability of the accident resulting from the insufficient safety of the cable components of the main bridge of PAIRA bridge is 0.02, which belongs to a very small range. According to the analysis of the risk grade matrix, the risk grade is Ⅱ, which belongs to the acceptable risk range. In addition, according to the reverse reasoning of the Bayesian model, when the serious failure of the cable system is certain to occur, the node with the greatest impact is B3 (cable break) and its probability of occurrence is 82%, that is, cable break is an important reason for the serious failure of the cable system. The factor that has the greatest influence on B3 node is C6 (cable quality), and its probability is 34%, that is, cable quality is not satisfied is the main reason for cable fracture. In the same way, it can be obtained that the D9 (steel wire fracture inside the cable) event of the next level is the biggest incentive of C6 event, its occurrence probability is 32% and E7 (steel strand strength is not up to standard) event is the biggest incentive of D9 event, its occurrence probability is 13%. At the same time, the sensitivity analysis also confirmed that B3, C6, D9 and E7 risk factors were the main causes of risk occurrence.Originality/valueThis paper proposes a Bayesian network-based construction reliability assessment method for cable-stayed bridge cable system. The core purpose of this method is to achieve comprehensive and accurate management and control of the risks in the construction process of the cable system, so as to improve the service life of the cable while strengthening the overall reliability of the structure. Compared with the existing evaluation methods, the proposed method has higher reliability and accuracy. This method can effectively assess the risk of the cable system in the construction process, and is innovative in the field of risk assessment of the cable system of cable-stayed bridge construction, enriching the scientific research achievements in this field, and providing strong support for the construction risk control of the cable system of cable-stayed bridge.

Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.

Enhanced ABLO-OFDM performance by using composite multi-mode index modulation in IM/DD systems

In intensity modulation direct detection (IM/DD) systems, an adaptively biased layered optical orthogonal frequency division multiplexing (ABLO-OFDM) scheme is proposed based on composite multi-mode index modulation (CMM-IM). The ABLO-OFDM has a simple structure, easy demodulation, low complexity, and effectively reduces the peak-to-average power ratio (PAPR). Since CMM-IM extends the index to the constellation domain, it increases the number of index bits to further improve the spectral efficiency (SE). By dynamically adjusting the number of activated subcarriers and the number of layers, various signal power can be achieved to enhance system flexibility and reliability. The system performance of four schemes is compared and analyzed in IM/DD systems. The result shows that, for the same SE, CMM-ABLO (4,3) achieves a 3 dB receiver sensitivity gain compared with CMM-ABLO (8,2). When L is 3, four schemes can transmit over about 50 km single-mode fiber under the soft decision forward error correction (SD-FEC) threshold. When L is 1 and the sample rate is less than 20 GSa/s, both CMM-ABLO (4,2) and CMM-ABLO (4,3) can achieve error-free transmission. Moreover, as the number of layers increases, PAPR decreases and it reaches a maximum reduction of 9.6 dB.

Sustainable hybrid energy system’s reliability optimization by solving RRAP-CM with integration of metaheuristic approaches

PurposeThis research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.Design/methodology/approachTo obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.FindingsThe study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.Research limitations/implicationsThe study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.Practical implicationsThe research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.Originality/valueThis work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

Calibrating Trust, Reliance and Dependence in Variable-Reliability Automation

Trust and system reliability can influence a user’s dependence on automated systems. This study aimed to investigate how increases and decreases in automation reliability affect users’ trust in these systems and how these changes in trust are associated with users’ dependence on the system. Participants completed a color identification task with the help of an automated aid, where the reliability of this aid either increased from 50% to 100% or decreased from 100% to 50% as the task progressed, depending on which group the participants were assigned to. Participants’ trust, self-confidence, and dependence on the system were measured throughout the experiment. There were no differences in trust between the two groups throughout the experiment; however, participants’ dependence behavior did follow system reliability. These findings highlight that trust is not always correlated with system reliability, and that although trust can often influence dependence, it does not always determine it.