Redundancy Strategy Research Articles

Integrating Cognition Cost With Reliability QoS for Dynamic Workflow Scheduling Using Reinforcement Learning

The rapid rise of microservice architecture poses severe challenges to workflow scheduling, resource allocation, and goal optimization. However, faults and failures usually happen during workflow running. To ensure the workflow's successful execution during scheduling microservices, this paper proposes a dynamic workflow scheduling algorithm by integrating cognition cost and reliability QoS for using reinforcement learning (WS-CCR). Firstly, we explore the ‘restart policy’ of containers in the Kubernetes architecture, which lays the foundation for our work that adopts the redundancy strategy to ensure workflow operation. Then we consider the cognitive cost based on the fact that users have a cognitive process for different microservices in selecting microservices. Additionally, another optimization goal is the reliability of workflows. On this basis, we design a reasonable reward function in reinforcement learning to generate dynamic strategies. Furthermore, following some generated strategies, our engine will schedule candidate microservices for tasks to execute step by step. A series of experiments on Alibaba and business areas workflows have proven the superior performance of our algorithm. Our WS-CCR can generate better Pareto solution sets than other baselines in terms of improving the reliability of running workflows. Finally, we give a case study to prove the practicability of our method.

Good Helper Is around You: Attention-Driven Masked Image Modeling

It has been witnessed that masked image modeling (MIM) has shown a huge potential in self-supervised learning in the past year. Benefiting from the universal backbone vision transformer, MIM learns self-supervised visual representations through masking a part of patches of the image while attempting to recover the missing pixels. Most previous works mask patches of the image randomly, which underutilizes the semantic information that is beneficial to visual representation learning. On the other hand, due to the large size of the backbone, most previous works have to spend much time on pre-training. In this paper, we propose Attention-driven Masking and Throwing Strategy (AMT), which could solve both problems above. We first leverage the self-attention mechanism to obtain the semantic information of the image during the training process automatically without using any supervised methods. Masking strategy can be guided by that information to mask areas selectively, which is helpful for representation learning. Moreover, a redundant patch throwing strategy is proposed, which makes learning more efficient. As a plug-and-play module for masked image modeling, AMT improves the linear probing accuracy of MAE by 2.9% ~ 5.9% on CIFAR-10/100, STL-10, Tiny ImageNet, and ImageNet-1K, and obtains an improved performance with respect to fine-tuning accuracy of MAE and SimMIM. Moreover, this design also achieves superior performance on downstream detection and segmentation tasks.

Pathogenesis of nontypeable Haemophilus influenzae infections in chronic suppurative lung disease.

The respiratory tract antimicrobial defense system is a multilayered defense mechanism that relies upon mucociliary clearance and components of both the innate and adaptive immune systems to protect the lungs from inhaled or aspirated microorganisms. One of these potential pathogens, nontypeable Haemophilus influenzae (NTHi), adopts several, multifaceted redundant strategies to successfully colonize the lower airways and establish a persistent infection. NTHi can impair mucociliary clearance, express multiple multifunctional adhesins for various cell types within the respiratory tract and evade host defenses by surviving within and between cells, forming biofilms, increasing antigenic drift, secreting proteases and antioxidants, and by host-pathogen cross-talk, impair macrophage and neutrophil function. NTHi is recognized as an important pathogen in several chronic lower respiratory disorders, such as protracted bacterial bronchitis, bronchiectasis, cystic fibrosis, and primary ciliary dyskinesia. The persistence of NTHi in human airways, including its capacity to form biofilms, results in chronic infection and inflammation, which can ultimately injure airway wall structures. The complex nature of the molecular pathogenetic mechanisms employed by NTHi is incompletely understood but improved understanding of its pathobiology will be important for developing effective therapies and vaccines, especially given the marked genetic heterogeneity of NTHi and its possession of phase-variable genes. Currently, no vaccine candidates are ready for large phase III clinical trials.

Shared Redundancy Strategy to Improve the Reliability and Fault-Tolerant Capability of Modular Multilevel Converter

Utilizing redundant sub-modules is a well-known strategy to improve the reliability and fault-tolerant capability of the modular multilevel converter (MMC). Redundancy-based fault-tolerant strategy makes it possible to restore the full performance of the converter in the post-fault condition. The main drawback of this strategy is the high cost. In this configuration, each redundant sub-module (RSM) can only be utilized in a specific arm and it cannot be used in other arms. This limitation leads to inefficient utilization of the RSMs in conventional MMC. This paper proposes a shared redundancy strategy which allows RSMs to be used either in upper or lower arms. The derived reliability models in various case studies illustrate that proposed strategy significantly improves the reliability and useful lifetime of MMC. To investigate the effectiveness of the proposed strategy, simulation and experimental results are provided.

Reliable design optimization for industrial hybrid energy systems with uncertain sustainable energy

More and more attention has been paid to the industrial hybrid energy systems which comprise fossil energy and renewable energy for achieving carbon neutrality. Wherein, the uncertainty of renewable energy, fluctuation of energy requirement from process units, and failure rate of energy conversion devices have significant effects on the reliability of industrial hybrid energy systems and thus restrict the application and promotion. How to simultaneously address these issues for realizing the reliable design of industrial hybrid energy systems has been a matter of great concern and challenge. Hence, a systematic reliable design optimization method is presented in this study to tackle these challenges. In this study, the two-stage stochastic programming method is implemented to conduct the uncertain optimization in dealing with the uncertainty of renewable energy and fluctuation of energy requirement, while a mixed stand-by redundancy allocation problem is proposed for simultaneously enhancing the reliability and minimizing the number of stand-by devices. A multi-objective mixed integer non-linear programming framework using reliability and total annual cost as the dual objectives is then formulated and applied to a refinery site for the reliable design optimization of the industrial hybrid energy system. According to the solution results, compared to the minimum total annual cost scheme without stand-by redundancy, the maximum reliability of the industrial hybrid energy system can reach 0.9878 using the stand-by redundancy strategy. As a result, the reliability can be significantly enhanced by 44%, with only a 5% increment of the total annual cost. Moreover, a Pareto frontier under multi-objective optimization is constructed, and the optimization schemes at the Pareto frontier can realize the trade-off between reliability and the total annual cost of the industrial hybrid energy system. Subsequently, a reliability-gain index is proposed to further evaluate the trade-off schemes and thus guide the choice of appropriate design schemes.

Experimental investigations on structural improvement of morphing mechanism for nose cone of aerospace vehicle adopting a bionic redundant constraint strategy

Experimental investigations on structural improvement of morphing mechanism for nose cone of aerospace vehicle adopting a bionic redundant constraint strategy

Redundancy allocation under state‐dependent distributional uncertainty of component lifetimes

In this paper, we consider a redundancy allocation problem in a series‐parallel system with uncertain component lifetimes of multiple states, which involves multitype components and mixed redundancy strategy. We develop a distributionally robust redundancy allocation model using state‐dependent ambiguity set, which describes the mean, expected cross‐deviation, and the support conditional on each state of the component lifetime distribution. Our structural analysis of the worst‐case system reliability function identifies a concave equivalent for the conditional worst‐case reliability function in each state and achieves an averaged finite piece‐wise affine representation for the overall worst‐case system reliability function, which enjoys valuable operational insights for reliability evaluation and scalable optimization of the redundancy designs. Furthermore, we analyze the reliability guarantees for the worst‐case reliability in optimality under possible misspecification of ambiguity‐set parameters, which admit the structure of “Reliability Target + Target Surplus + Estimated Elasticity,” and insightfully imply that the auxiliary dual variables obtained via solving the design problem can be regarded as the scale of elasticity. Computationally, the redundancy design problem can be linearized and reformulated as a mixed 0–1 second‐order cone program. Exploiting the above finite piece‐wise affine structure of the worst‐case reliability function, the design problem also admits a practical iterative decomposition scheme with finite convergence, which is more scalable especially for the possible situations of large number of states in practice. Finally, numerical experiments including a case with real‐life data of braking system components well justify the value of state information incorporated by our model in producing favorable cost‐effective redundancy designs for reliability operations.

Application of A* Algorithm Based on Extended Neighborhood Priority Search in Multi-Scenario Maps

The robustness of the traditional A* algorithm of path planning is poor due to its excessive number of traversal nodes, slow search speed, and large turning angle. Aiming to solve the above problems, a multi-scenario adaptive A* algorithm based on extended neighborhood priority search is proposed. Firstly, this algorithm designs the heuristic function that can adapt to various scene changes by quantifying the scene map information, and the search weight is adjusted adaptively to enhance the robustness and adaptability of the algorithm. Secondly, the search strategy based on extended neighborhood priority is adopted to improve the orientation of the algorithm, and the redundant node removal strategy is used to smooth the path to reduce the number of traversed nodes and the turning angle. Finally, simulation tests are conducted in several representative map environments. The test results show that the proposed algorithm is superior to the traditional A* algorithm due to its stronger robustness and significantly improved performance metrics, with an 84.95% reduction in the number of traversal nodes, an 83.84% reduction in the number of path nodes, a 62.28% reduction in turning points on the path, a 77.38% reduction in the total turning angle, and a 58.47% reduction in the search time.

Efficient data-driven behavior identification based on vision transformers for human activity understanding

With the development of computer vision, the research on human activity understanding has been greatly promoted. The recognition algorithm based on vision transformer has made some achievements in a large number of computer vision tasks, but it still needs to be driven by a large amount of data. How to get rid of the constraints of large amounts of data is crucial for human behavior recognition based on vision transformer. This paper focuses on solving the dilemma of big data, and tries to achieve a high-performance model through a small amount of high information human activity data. The advantage of our work is that by studying feature distribution, we proposed a core weight entropy data information evaluation method for obtaining high information data, and through redundant information elimination strategy, we can avoid introducing similar data. A large number of experimental results show the effectiveness of the proposed method. Compared with existing methods, our method reduces the data consumption by 5% to 30%, and can achieve the performance of using only 50% of 100% data. More importantly, the data our method selected has no redundancy, which is not available in other methods. In addition, we carried out a large number of ablation experiments to prove the rationality of the method. The work of this paper solves the challenge of relying on a large amount of data when using the visual converter to recognize human behavior, which is of practical significance for realizing efficient human activity understanding research with low data.

Reliability analysis and optimization for a redundant system with dependent failures and variable repair rates

This paper considers the reliability analysis and the bi-objective optimal problem for a redundant system with two dependent failures: common cause failure (CCF) and load-sharing failure, where the system contains N active components, W warm standbys and C cold standbys, a K-mixed redundancy strategy is considered. In the system, there is a repair team providing variable repair rates according to differential situations: when there are standby components available in the system, the repair team assigns a regular repairman to repair the failed components, with the known fact that regular repairman is not as skilled as an expert repairman in doing some complex repair; when all the spares are exhausted, the repair team assigns an expert repairman to attend failed components; when the system breaks down due to a CCF’s occurrence, the repair team provides a very special repair service to restore the system to normal working state. The steady-state distribution of the system is obtained by matrix-analytic method. The sets of equations satisfied by the reliability functions and transient availabilities from arbitrary initial state are respectively presented and solved by Markov renewal theory, Laplace–Stieltjes transform (LST) and Laplace transform (LT) technique. Based on the obtained results, sensitivity analysis is performed by some numerical experiments. From economic view point, the bi-objective optimization problem is constructed and solved by NSGA-II algorithm for the minimal expected total cost and maximal availability.

Performance indicator-based multi-objective reliability optimization for multi-type production systems with heterogeneous machines

• A novel system model: RRAP-CM-MCCS-based multitype production system • Realize the multiple machine deployment at each workstation (subsystem) • Present a new multi-objective application, a performance indicator-based multi-objective algorithm, ε-MOABC. • Provide quality solutions that are profitable in terms of cost savings. The demand for product manufacturing is gradually leading the direction of production. Therefore, a production line that can produce diversified products is critical for meeting the requirements of modern systems. This study investigates multi-objective optimization of the reliability of a multi-type production system. In particular, machine deployment at each workstation can be associated with different reliability depending on the purchasing year or brand. Consequently, this study applies a component mixing strategy to multi-type production systems to estimate the reliability of an ensemble of heterogeneous machines. The goal of the studied problem is to maximize all production lines, and the cold-standby redundancy strategy based on the continuous-time Markov chain model is employed to calculate the exact reliability of the system. A performance indicator-based multi-objective algorithm, ε-MOABC, was employed to search for a near-optimal solution. The experimental results show that the multi-objective solution effectively converges to the Pareto front of the knee region and, more importantly, it can achieve high system reliability at a streamlined cost.

A dynamic intelligent building retrofit decision-making model in response to climate change

Building energy-saving retrofitting has become an essential way for the building sector to cope with climate change. Furthermore, climate change affects building retrofit strategies. The current building stock is massive, which means there is a large demand for building retrofitting. Additionally, climatic conditions are changing, posing a significant challenge to the current time-consuming and labor-intensive decision-making process. To solve these problems, a dynamic intelligent building decision-making model was established in this study. The static and dynamic features of building retrofit decision-making were identified, four machine learning algorithms were considered, and a case base containing records for 301 retrofitted buildings was established for knowledge mining. The findings demonstrate that the XGBoost algorithm performs well in terms of building retrofit strategy prediction, with 77% accuracy for the prediction of building envelope retrofits and 76% accuracy for the prediction of HVAC system retrofits. In addition, the trends of building retrofit strategy decision-making considering dynamic climate conditions were observed. The demand for building envelope retrofitting and heating retrofitting has declined, while the demand for cooling retrofitting has increased. Some buildings are extremely sensitive to climate change, and some redundant retrofitting strategies should be avoided. The proposed intelligent decision-making model can provide valuable information for future building retrofit strategy decision-making.

Research on Control Method of Dual-Motor Load Simulator

Aiming at the output torque error of a steering gear electric load simulator caused by excess torque and backlash interference, an electric load simulator based on double-motor loading is designed. The double-motor loading mode is adopted in the structure, the mathematical model is established, and the sources of excess torque and backlash interference are analyzed. In the control strategy, firstly, a torque controller is designed as a feedback controller based on the improved error symbol robust integral control method, and then a backlash interference compensator is designed as a feedforward controller based on the drive redundancy strategy. Finally, a dual motor speed synchronization controller is designed based on the improved cross coupling control method to ensure the stable operation of the torque controller and backlash compensator in the dual-motor system. The simulation results show that the compound control method can reduce the tracking error to 1.13%, 4.44% less than the PID control method. The tracking error is only 1.54% in the case of redundant torque, backlash, and different parameters of dual motors. The method proposed in this paper can still output loading torque with high accuracy.

Sensorimotor Time Delay Estimation by EMG Signal Processing in People Living with Spinal Cord Injury.

Neuro mechanical time delay is inevitable in the sensorimotor control of the body due to sensory, transmission, signal processing and muscle activation delays. In essence, time delay reduces stabilization efficiency, leading to system instability (e.g., falls). For this reason, estimation of time delay in patients such as people living with spinal cord injury (SCI) can help therapists and biomechanics to design more appropriate exercise or assistive technologies in the rehabilitation procedure. In this study, we aim to estimate the muscle onset activation in SCI people by four strategies on EMG data. Seven complete SCI individuals participated in this study, and they maintained their stability during seated balance after a mechanical perturbation exerting at the level of the third thoracic vertebra between the scapulas. EMG activity of eight upper limb muscles were recorded during the stability. Two strategies based on the simple filtering (first strategy) approach and TKEO technique (second strategy) in the time domain and two other approaches of cepstral analysis (third strategy) and power spectrum (fourth strategy) in the time-frequency domain were performed in order to estimate the muscle onset. The results demonstrated that the TKEO technique could efficiently remove the electrocardiogram (ECG) and motion artifacts compared with the simple classical filtering approach. However, the first and second strategies failed to find muscle onset in several trials, which shows the weakness of these two strategies. The time-frequency techniques (cepstral analysis and power spectrum) estimated longer activation onset compared with the other two strategies in the time domain, which we associate with lower-frequency movement in the maintaining of sitting stability. In addition, no correlation was found for the muscle activation sequence nor for the estimated delay value, which is most likely caused by motion redundancy and different stabilization strategies in each participant. The estimated time delay can be used in developing a sensory motor control model of the body. It not only can help therapists and biomechanics to understand the underlying mechanisms of body, but also can be useful in developing assistive technologies based on their stability mechanism.

Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture

In urban air mobility (UAM), video streaming platforms have gained significant attention from media companies due to their growing necessity for on-demand video streaming services-as-you-go in flights. Video streaming services can provide constant data transactions in a huge amount, especially in its operational digital twin (ODT). As a result, the ability to provide a satisfactory user experience through video streaming platforms is critical and complex. This requires continuously operating services while handling numerous user requests for near real-time video streaming. At the same time, high-quality video with high resolution and minimal interruptions is often expected. Therefore, the availability and performance (i.e., performability) of the Back-End video streaming infrastructure are crucial parameters for these platforms. However, evaluating novel video-on-demand architectures in real-world scenarios can be costly due to the numerous parameters involved. Analytical models, such as stochastic Petri nets (SPNs), can serve as an alternative in this complex scenario as they can be used to analyze systems during the design process. In this study, we developed a set of SPN models to assess the performance of a video-on-demand system. These models were designed to illustrate and to evaluate a video-on-demand architecture while considering performance. We had a base SPN model as well as three enhanced variants available. The extended models were generated using the Design of Experiments (DoE) technique and sensitivity analysis results. The DoE identified the factors with the greatest impacts on performance, and the most significant factor interactions. Redundancy strategies were applied to the extended models to increase the availability of the most important components. This redundancy increased the availability of “9 s” from three to five. It is worth noting that this study can help the designers of video streaming systems, to plan and to optimize their ideas based on the provided models.

A Novel Perspective on Reliable System Design With Erlang Failures and Realistic Constraints for Incomplete Switching Mechanisms

This study focuses on effectively designing reliable systems. Such systems are capable of withstanding failure events by applying multiple realistic workarounds. These workarounds include allocating redundancy, component reliability, and backup strategy that are considered concurrently as decision variables. In this novel view, the strategy and reliability of components are determined freely and independently to achieve optimality. The research problem is implemented in a general case to audit the capabilities of the proposed approach in realistic situations. This case deploys an Erlang time-to-failure probability density function together with incomplete switching. With its improved reliability and resource functions, the proposed model challenges the existing presumption regarding the superiority of the cold-standby approach in the mentioned field and provides a realistic trade-off between different redundancy strategies. This practical view reflects on reliability, cost, weight, and volume of the switch, simultaneously. The findings revealed that the proposed joint reliability-redundancy allocation problem, with an added freedom of strategy choice, outperforms the pure cold-standby counterpart. Owing to the NP-hard nature of the problem, a simplified particle swarm optimization algorithm is suggested and utilized as a solution method. The performance of the novel view is assessed using multiple benchmark instances including some typical problems from the literature. Our numerical analysis demonstrates the superiority of this approach with our maximum possible index reaching up to %96 compared to the existing results in the past works. Furthermore, the selected solution algorithm is compared with a differential evolution algorithm. We show that this simplified particle swarm optimization algorithm performs considerably better in all tested scenarios.

A new dynamic model to optimize the reliability of the series-parallel systems under warm standby components

<p style='text-indent:20px;'>Redundancy allocation problem (RAP) is a common technique for increasing the reliability of systems. In this paper, a new model for the RAP is introduced that takes into account the warm standby and mixed strategy, the model dynamics, and the type of the strategy in redundancy allocation problems. A recursive formula is first obtained for the reliability function in the dynamic warm standby and mixed redundancy strategies that leverages the success mode analysis and works for any arbitrary failure-time distribution. Failure rates for warm standby units change before and after their replacement with a damaged unit, and, therefore, the reliability function in warm standby varies with time (i.e., the model is dynamic). Although dynamic models are commonplace in practice, they are more challenging to assess than static models, which have been mainly considered in the literature. An optimization problem is then formulated to select the best redundancy strategy and redundancy levels. Genetic algorithm and particle swarm optimization are leveraged to solve the problem. Finally, the efficiency of the presented method is verified through a numerical example. The experimental results verify that the proposed model for RAP significantly improves the system reliability, which can be of vital importance for system designers.</p>

APSO: An A*-PSO Hybrid Algorithm for Mobile Robot Path Planning

Aiming at the problems of the A* algorithm in mobile robot path planning, such as multiple nodes, low path accuracy, long running time and difficult path initialization of particle swarm optimization, an APSO algorithm combining A* and PSO was proposed to calculate the optimal path. First, a redundant point removal strategy is adopted to preliminarily optimize the path planned by the A* algorithm and obtain the set of key nodes. Second, a stochastic inertia weight is proposed to improve the search ability of PSO. Third, a stochastic opposition-based learning strategy is proposed to further improve the search ability of PSO. Fourth, the global path is obtained by using the improved PSO to optimize the set of key nodes. Fifth, a motion time objective function that is more in line with the actual motion requirements of the mobile robot is used to evaluate the algorithm. The simulation results of path planning show that the path planned by APSO not only reduces the running time of the mobile robot by 17.35%, 14.84%, 15.31%, 15.21%, 18.97%, 15.70% compared with the A* algorithm in the six environment maps but also outperforms other path planning algorithms to varying degrees. Therefore, the proposed APSO is more in line with the actual movement of the mobile robot.

An Integrated Risk Management Framework for Global Supply Chains

In this study, a risk management framework is developed to support risk management decisions in global supply chains. The proposed framework covers all phases of risk management, namely, risk identification, risk miti-gation and control. In the risk identification phase of the framework, the supply chain is decomposed into either material-level or product-level sub-networks according to the decision maker’s preference. Afterwards, the most crit-ical sub-network is modelled to evaluate different risk mitigation strategies. In particular, a combination of redun-dancy and flexibility strategies is considered for risk mitigation. These strategies are evaluated by simulation models in terms of their effectiveness and efficiency. While inventory holding cost is used as efficiency measure, effective-ness of the strategies is measured by premium freight ratio. The proposed framework provides a comprehensive and reliable decision support since it covers all phases of risk management and relies on quantitative data, and statistical analysis in risk modelling. Moreover, it is flexible as it can be easily adapted to any change in supply chain environ-ment and strategy. In order to show the applicability of the framework, a practical demonstration is presented for a European automotive company. The results indicate that the proposed framework improves the supply chain perfor-mance in terms of efficiency and effectiveness.

Modeling and analysis for a repairable system with multi-state components under K-mixed redundancy strategy

In this paper, a three components repairable system with K-mixed redundancy strategy is proposed, in which the lifetimes of components, the repair time of failure components and the vacation time are distributed with different phase-type (PH) distributions. The different phases can represent different operational levels, repair levels and vacation levels. The multiple vacation policy is also adopted so that the human resources can be fully utilized. The repairable system is studied in both transient and stationary regimes with the matrix-analytic method, and not only some traditional reliability indexes are obtained, but also several new reliability indexes are obtained by employing Kronecker operator theory and aggregated stochastic theory. Finally, a numerical example is implemented to illustrate the results obtained in the paper.