Reliability Considerations Research Articles

Belief rule mining using the evidential reasoning rule for medical diagnosis

A belief rule mining approach is proposed to generate belief rules with a customized set of criteria by mining from multiple belief rules that are trained using data with varied sets of criteria. As the theoretical basis of the belief rule mining approach, the key concepts are defined, including the weights and reliabilities of cases, criteria, models, and belief rules. Based on the key concepts, multiple sub-models composed of belief rules with varied sets of criteria are initialized and optimized. Then, the optimized sub-models are integrated using the evidential reasoning rule into belief rules with a customized set of criteria. In the belief rule mining process, the weights and reliabilities of the sub-models are considered according to the weight and reliability calculation procedures of models proposed in this study. The proposed approach is used to help diagnose thyroid nodules with 527 medical cases, in which its applicability is demonstrated. By comparative experiments, the diagnostic correctness of the proposed approach is verified to be higher than those of the directly-optimized model and the approach without the consideration of reliability.

Reliability considerations of modern design codes for CFST columns

Concrete filled steel tubular (CFST) columns have been increasingly used in tall buildings and bridges due to offering excellent structural and economic benefits. Current design codes for such columns exhibit certain limits in terms of material strengths and section slenderness. This paper aims to evaluate the reliability and the applicability of the current design equations in American code AISC 360–16, European code EC 4 and Australian/New Zealand code ASNZS 2327 for the design of the columns beyond their material and slenderness limits. A comprehensive database with over 3200 tests was collected to develop the statistics of the model errors for different types of columns. Monte Carlo and subset simulation techniques were developed based on Markov Chain Monte Carlo algorithms, to accurately and efficiently predict the reliability index of structures with small failure probability because they account for all uncertainties in material and geometric properties, loads and model errors. The results from the reliability analysis indicate that the reliability index of the concentric column designed by three considered codes is much higher than that of the eccentric column (i.e. beam-column). The results from a parametric study suggest that all three codes can be safely extended to the design of columns beyond the current material and section slenderness code limits.

Power Quality and Reliability Considerations of Photovoltaic Distributed Generation

Worldwide energy consumption is increasing at a faster pace than energy generation because of enhanced industrialization, growing population and, improved living standards. Using the Distributed Generation (DG) near the end consumers can support the electrical grid stability and enhance the power system quality. The DG is consisting of a small-scale technology to generate electrical power near the end consumers, which can be renewable energy or generators. Solar Photovoltaic (PV) system is considered as one of the best renewable energy sources, due to its low running cost and low environmental affection comparing with traditional power plants. The proposed PVDG units are employing maximum power point tracking (MPPT) system to track the maximum power available in PV arrays. The proposed DG system used Particle Swarm Optimization (PSO) technique to optimally allocate the PVDG units for minimum cost, transmission line losses, and improved voltage profile of busbars under different operating conditions. This paper proposes a smart optimization technique employing PSO in the design, operation, and control of the proposed system. Also, the paper discusses the PSO algorithm for optimal DGs sizing and allocations. The simulation in this paper uses the IEEE-7 busbars system with an extension of IEEE-3 busbars as a new remote load. The simulation has been carried out by the power world simulator software to compare the voltage and the power losses with and without PVDGs connected to the system. The simulation results showed an acceptable reduction in generating cost, transmission line losses, and improvement of the busbars voltage profile which proves the success of the proposed system.

Model Predictive Control for Virtual Synchronous Generator with Improved Vector Selection and Reconstructed Current

Due to the large-scale renewable energy connected to the power grid by power electronic converters, the inertia and stability of the power grid is declining. In order to improve the inertia and support the grid recovery, the three-phase converter works as a virtual synchronous generator (VSG) to respond to the frequency and voltage changes of the power grid. This paper proposes a model predictive control for the virtual synchronous generator (MPC-VSG) strategy, which can automatically control the converter output power with the grid frequency and voltage changes. Further consideration of fault-tolerant ability and reliability, the method based on improved voltage vector selection, and reconstructed current is used for MPC-VSG to ensure continuous operation for three-phase converters that have current-sensor faults, and improve the reconstruction precision. The proposed method can respond to the frequency and voltage changes of the power grid and has fault-tolerant ability, which is easy to realize without pulse width modulation (PWM) and a proportional-integral (PI) controller. The effectiveness of the proposed control strategy is verified by experiment.

An Improved Whale Algorithm for Setting Standard Scheduled Block Time Based on the Airline Fairness

Standard scheduled flight block time (SBT) setting is of great concern for Civil Aviation Administration of China (CAAC) and airlines in China. However, the standard scheduled flight block times are set in the form of on-site meetings in practice and current literature has not provided any efficient mathematical models to calculate the flight block times fairly among the airlines. The objective of this paper is to develop and solve a mathematical model for standard SBT setting with consideration of both fairness and reliability. We use whale optimization algorithm (WOA) and an improved version of the whale optimization algorithm (IWOA) to solve the SBT setting problem. A novel nonlinear update equation of convergence factor for random iterations is used in place of the original linear one in the proposed IWOA algorithm. Experimental results show that the suggested approach is effective, and IWOA performs better than WOA in the concerned problem, whose solutions are better compared to the flight block times released by CAAC. In particular, it is interesting to find that MSE, RMSE, MAE, MAPE and Theil of the reliability in 60%–70% range are always the smallest and the average fairness of airlines is better than that of 60%–75% range. The model and solving approach presented in this article have great potential to be applied by CAAC to determine the standard SBTs strategically.

Intrinsic Structure Modification of Electrode Materials for Aqueous Metal‐Ion and Metal‐Air Batteries

AbstractIn consideration of low cost, simple operation, safe reliability, and environmental benignity, aqueous rechargeable batteries (ARBs) have attracted great interest among portable electronic devices, energy storage, and power source batteries. As the key components of ARBs, the electrode materials lead to a crucial effect for the overall battery properties, such as working voltage, electrocatalytic activity, capacity, and cycling stability. However, owing to the highly reactive aqueous environment, the electrode materials typically demonstrate a series of issues, including active material dissolution, structural instability, and poor catalytic activity, restricting their application. So far, several researchers have devoted much effort to improve the properties of electrode materials for ARBs. In particular, intrinsic structure modification in terms of vacancy regulation, interlayer engineering, and element doping have been applied to optimize the electronic and phase structure of electrode materials, contributing to elevated ion diffusion, fast charge transfer, and adequate active sites for electrochemical reaction. In this review, recent reports are demonstrated about the intrinsic structure modification of electrode materials in aqueous metal‐ion and metal‐air batteries, focusing on various regulation strategies and functional mechanisms. Finally, a brief conclusion and perspective is presented to demonstrate constructive suggestions and opinions for further research.

Ride-Hailing Order Dispatching at DiDi via Reinforcement Learning

Order dispatching is instrumental to the marketplace engine of a large-scale ride-hailing platform, such as the DiDi platform, which continuously matches passenger trip requests to drivers at a scale of tens of millions per day. Because of the dynamic and stochastic nature of supply and demand in this context, the ride-hailing order-dispatching problem is challenging to solve for an optimal solution. Added to the complexity are considerations of system response time, reliability, and multiple objectives. In this paper, we describe how our approach to this optimization problem has evolved from a combinatorial optimization approach to one that encompasses a semi-Markov decision-process model and deep reinforcement learning. We discuss the various practical considerations of our solution development and real-world impact to the business.

Contextual Assessment of Retinal Injuries – Tasks of the Ophthalmological Expert

The ophthalmologic assessment of causal relationships is subject to formal guidelines, depending on the legal field (social law in the statutory accident insurance, civil law in the private accident insurance). After determining all objective and subjective findings of the individual case with complete recording of the medical facts, the ophthalmologist has the task of making a summarizing assessment of the existing cause-and-effect relationship. With regard to the distinction between retinal damage caused by an accident or retinal disease not caused by an accident, it is necessary to weigh up the natural causality according to the state of medical experience on the basis of the criteria strength of association, consistency, specificity, temporal sequence, dose dependence, agreement with previous findings, experimental reliability and analogous consideration. All records of medical findings from the patient's medical history and the individual description of the accident must be included in the expert opinion. In the case of several competing causes (often accident and pre-existing damage), the social law in the statutory accident insurance must present the causal contributions with roughly estimated probabilities. In civil law, valid for the private accident insurance, the existence of partial causality (approx. 25, 50, 75%) must be evaluated.

Modes of administering sexual health and blood-borne virus surveys in migrant populations: A scoping review

There has been a growing number of sexual health and blood-borne virus (SHBBV) surveys specifically administered to migrant populations. The purpose of this scoping review is to collate available information about how SHBBV surveys have been administered in migrant populations and the effect that mode of administration has on data quality, reliability and other practical considerations, e.g. response rates (RR) and social desirability bias. A methodological framework for scoping reviews was applied. SHBBV survey studies administered to international migrants published since 2000 were included if they contained some description of mode of administration. Ninety one studies were identified for inclusion from Embase, Medline, Web of Science, Google Scholar and supplementary grey literature. 'Interview only' was the most common mode of administration (n = 48), predominately comprising face-to-face interviews. Thirty six studies reported data from 'self-completed' surveys only, with pen-and-paper being most common (n = 17). Few studies (n = 7) combined interview and self-completed methods of survey administration. Sixty one studies did not report (or only partially reported) RR or the data necessary to calculate RR. Of the studies that reported RR, most were missing other key information including method of recruitment, consent procedures and whether incentives were offered. Strengths and limitations of all administration modes are summarised. Guidelines to inform future SHBBV survey research in migrant populations are presented.

The post-installed screw pull-out test: Development of a method for assessing in-situ concrete compressive strength

In concrete structures, the current methodology for concrete strength estimation combines the use of in-situ test methods with destructive techniques (taking cores). In-situ tests are indirect approaches, and can be affected by several uncontrolled factors, thus inducing uncertainty in the strength estimate. Low cost in-situ methods are often deployed in practice, without taking due consideration of the low reliability. Thus, the reliability of the estimated concrete strength is always a questionable issue. In this context, this paper seeks to develop an in-situ test method as an alternative to the available techniques to assess in-situ compressive strength of concrete. The PSP test is proposed based on the modified pullout testing of post-installed screw. The test is a partially destructive technique that involves insertion of an oversized screw into a predrilled hole in the hardened concrete and subsequent pullout of the screw compelling the concrete to follow a specific failure pattern – a complete pullout failure. The peak load obtained in the process is then correlated to the concrete compressive strength. In determining an appropriate and robust configuration for the PSP test, commercially available screws of different sizes were investigated along with different sizes of drilled hole. High strength correlation of the PSP test in mortar with selected test configuration reveals the potential of the test to be used in the strength assessment. Further investigations are proposed to confirm the suitability of the test to be applied in concrete.

Optimal Sizing of Fuel Cell Hybrid Power Sources with Reliability Consideration

This paper addresses the issue of optimal sizing reliability applied to a fuel cell/battery hybrid system. This specific problem raises the global problem of strong coupling between hardware and control parameters. To tackle this matter, the proposed methodology uses nested optimization loops. Furthermore, to increase the optimal design relevance, a reliability assessment of the optimal sizing set is introduced. This new paradigm enables showing the early impact of the reliability criteria on design choices regarding energetic performance index. It leads to a smart design methodology permitting to avoid complexity and save computing time. It considerably helps design engineers set up the best hybridization rate and enables practicing tradeoffs, including reliability aspects in the early design stages.

On Board Intelligence for Public Transportation in Developing Countries using IoT

This study is carried out for indigenous development of an ITS (Intelligent Transportation System) device aimed particularly for public transport buses in the developing countries with poor infrastructure and unaccountable transportation services. The developed system uses IoT technology, extendable hardware and open source software in order to achieve commercial scalability by government and transportation authorities in such countries. The proposed device essentially collects and store data from the bus fleet and communicates the information to a central server from which software applications for passengers and transport operators are developed accordingly. The device functionality is divided into three categories which are Environment Sensing, Vehicle Management and Safety. The acquired dataset can be used to produce useful insights that can benefit the social and commercial sector of the city. Analytics such as fuel consumption, vehicle health, predicting driver behaviour, forecasting of rush-hours and vehicle maintenance requirements can be estimated. The device is packaged into a box with proper considerations of reliability in terms of power, weather proofing, storage and cabling. The proposed device will allow the passengers, especially in developing cities, to plan their journeys ahead of time and provide a holistic decision support system to the transport authorities.

Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs.

Due to reliability and performance considerations, employing multiple software-defined networking (SDN) controllers is known as a promising technique in Wireless Sensor Networks (WSNs). Nevertheless, employing multiple controllers increases the inter-controller synchronization overhead. Therefore, optimal placement of SDN controllers to optimize the performance of a WSN, subject to the maximum number of controllers, determined based on the synchronization overhead, is a challenging research problem. In this paper, we first formulate this research problem as an optimization problem, then to address the optimization problem, we propose the Cuckoo Placement of Controllers (Cuckoo-PC) algorithm. Cuckoo-PC works based on the Cuckoo optimization algorithm which is a meta-heuristic algorithm inspired by nature. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. To evaluate the performance of Cuckoo-PC, we compare it against a couple of state-of-the-art methods, namely Simulated Annealing (SA) and Quantum Annealing (QA). The experiments demonstrate that Cuckoo-PC outperforms both SA and QA in terms of the network performance by lowering the average distance between sensors and controllers up to 13% and 9%, respectively. Comparing our method against Integer Linear Programming (ILP) reveals that Cuckoo-PC achieves approximately similar results (less than 1% deviation) in a noticeably shorter time.

Task Reliability Considerations in Computational Psychiatry

Measuring psychological abilities or traits is trickier than it seems from the published literatures ( 1 ). We try to study abstract psychological constructs like ‘inhibition’ or ‘impulsivity’, but we can only measure these indirectly through behaviours such as reaction times or self-report ratings. When we apply these measures in clinical and individual differences research, our goal is typically to understand why a patient group appear to be (e.g.) more ‘impulsive’ than healthy controls, or to use measures of ‘inhibition’ to predict a clinical outcome. There are many potential pitfalls and wrong turns in the path towards achieving this goal. In our recent work, we have shown that our intuitions about what makes a good cognitive task can sometimes lead us astray ( 2 , 3 ). Here, we discuss how these issues intersect with the goals of computational psychiatry.

An inventory model for a three-stage supply chain with random capacities considering disruptions and supplier reliability

This study develops an inventory model to solve the problems of supply uncertainty in response to demand which follows a Poisson distribution. A positive aspect of this model is the consideration of random inventory, delivery capacities and supplier’s reliability. Additionally, we assume supplier capacity follows an exponential distribution. This inventory model addresses the problem of a manufacturer having an imperfect production system with single supplier and single retailer and considers the quantity of product (Q), reorder points (r) and reliability factors (n) as the decision variables. The main contribution of our study is that we consider supplier may not be able to deliver the exact amount all the time a manufacturer needed. We also consider that the demand and the time interval between successive availability and unavailability of supplier and retailer follows a probability distribution. We use a genetic algorithm to find the optimal solution and compare the results with those obtained from simulated annealing algorithm. Findings reveal the optimal value of the decision variables to maximize the average profit in each cycle. Moreover, a sensitivity analysis was carried out to increase the understanding of the developed model. The methodology used in this study will help manufacturers to have a better understanding of the situation through the joint consideration of disruption of both the supplier and retailer integrated with random capacity and reliability.

Measurement system with real time data converter for conversion of I2S data stream to UDP protocol data

A central goal of systems neuroscience is to simultaneously measure the activities of all achievable neurons in the brain at millisecond resolution in freely moving animals. This paper describes a protocol converter which is part of a measurement acquisition system for multichannel real time recording of brain signals. In practice, in such techniques, a primary consideration of reliability leads to great necessity towards increasing the sampling rate of these signals while simultaneously increasing the resolution of A/D conversion to 24 bits or even to the unprecedented 32 bits per sample. In fact, this was the guiding principle for our team in the present study. By increasing the temporal and amplitude resolution, it is supposed that we get enabled to discover or recognize and identify new signal components which have previously been masked at a “low” temporal and amplitude resolution, and these new signal components, in the future, are likely to contribute to a deeper understanding of the workings of the brain.

Maintenance optimization of reconfigurable systems based on multi-objective Birnbaum importance

Reconfigurable systems can meet the changing requirements of system performance by several approaches, such as adjusting the system structure, improving the component performance, and reassigning components. However, it is also challengeable to find a cost-effective maintenance scheme by integrating these maintenance approaches. This article investigates the multi-objective maintenance optimization problem for reconfigurable systems with the consideration of maintenance cost and system reliability. First, the multi-objective maintenance optimization model is established to maximize the system reliability and minimize the total maintenance cost considering the constraints on budget and system performance. Second, a multi-objective Birnbaum importance is proposed to quantify the contribution of the individual component to the system reliability. The multi-objective Birnbaum importance–based non-dominated sorting genetic algorithm II is developed to obtain the optimal maintenance scheme with the maximum system reliability and minimum maintenance cost. Finally, the performance of multi-objective Birnbaum importance–based non-dominated sorting genetic algorithm II is proved by three numerical experiments. Experiment 1 verifies the advantage of multi-objective Birnbaum importance compared with Birnbaum importance to improve the system reliability in direct maintenance. Experiment 2 shows that the effectiveness of multi-objective Birnbaum importance is much better than that of the Birnbaum importance to enhance the performance of non-dominated sorting genetic algorithm II in comprehensive maintenance. Experiment 3 illustrates that the performance of multi-objective Birnbaum importance–based non-dominated sorting genetic algorithm II is better than that of other multi-objective algorithms combining with multi-objective Birnbaum importance.

Cooperative communication based on swarm intelligence: vision, model, and key technology

Inspired by natural swarms, unmanned platform swarms have been applied in military reconnaissance, civil mapping, and so on. Compared with a single unmanned platform, unmanned platform swarms exhibit higher environmental suitability and robustness, as well as better capacities for executing missions. Intelligent and reliable swarm communication is particularly critical for the activities of swarms; nonetheless, the scarce communication resources and the complex environment make swarm communications quite challenging. The existing research on communication in unmanned clusters lacks effectiveness, reliability, safety, and systematic consideration of autonomy, cooperativity, and intelligence. Hence, this paper focuses on the communication networks of unmanned aerial vehicle (UAV) swarms, such as ad hoc mesh networks, combines the swarm intelligence theory and the cognitive radio technology, and proposes a cooperative communication model and a cooperative sensing method based on swarm intelligence for UAV swarms. Finally, future developments are presented.

Test–Retest Reliability and Methodological Considerations of the Sensory Organization Test in Healthy Young Adults

Test–Retest Reliability and Methodological Considerations of the Sensory Organization Test in Healthy Young Adults

Reliability consideration costing method for LIFO Inventory model with Chemical Industry Warehouse

Reliability consideration costing method for LIFO Inventory model with Chemical Industry Warehouse