Energy-Efficient Deterministic Fault-Tolerant Scheduling for Embedded Real-Time Systems

By combining fault-tolerance with power management, this paper developed a new method for aperiodic task set for the problem of task scheduling and voltage allocation in embedded real-time systems. The scbedulability of the system was analyzed through checkpointing and the energy saving was considered via dynamic voltage and frequency scaling. Simulation results showed that the proposed algorithm had better performance compared with the existing voltage allocation techniques. The proposed technique saves 51.5% energy over FT-Only and 19.9% over FT ＋ EC on average. Therefore, the proposed method was more appropriate for aperiodic tasks in embedded real-time systems.

Partitioned k-Exclusion Real-Time Locking Protocol Motivated by Multicore Multi-GPU Systems

Graphic processing units （GPUs） have been widely recognized as cost-efficient co-processors with acceptable size, weight, and power consumption. However, adopting GPUs in real-time systems is still challenging, due to the lack in framework for real-time analysis. In order to guarantee real-time requirements while maintaining system utilization ~in modern heterogeneous systems, such as multicore multi-GPU systems, a novel suspension-based k-exclusion real-time locking protocol and the associated suspension-aware schedulability analysis are proposed. The proposed protocol provides a synchronization framework that enables multiple GPUs to be efficiently integrated in multicore real-time systems. Comparative evaluations show that the proposed methods improve upon the existing work in terms of schedulability.

Learning the parameters of a class of stochastic Lotka-Volterra systems with neural networks

In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

Mechanistic insights into the amelioration effects of diabetic cardiomyopathy by berberine:an integrated systems pharmacology study and experimental validation

Background:Diabetic cardiomyopathy(DCM)is a type of cardiomyopathy caused by long-term diabetes,characterized by abnormal myocardial structure and function,which can lead to heart failure.Berberine(BBR),a quaternary ammonium alkaloid isolated from Coptidis Rhizoma,a traditional Chinese medicine,has superior anti-diabetic and heart-protective properties.The purpose of this study is to assess the impact of BBR on DCM.Methods:This study used a systems pharmacology approach to evaluate the related proteins and signalling pathways between BBR and DCM targets,combined with experimental validation using diabetic mouse heart sections.Microstructural and pathological changes were observed using Hematoxylin-eosin,Masson’s trichrome stain and wheat germ agglutinin staining.Immunofluorescence and western blot were used to determine protein expression.Results:The results indicate that BBR and DCM share 21 core relevant targets,with cross-targets predominantly located in mitochondrial,endoplasmic reticulum,and plasma membrane components.BBR exerts its main effects in improving DCM by maintaining mitochondrial integrity,particularly involving the PI3K-AKT-GSK3βand apoptosis signalling pathways.In addition,post-treatment changes in the key targets of BBR,including cysteine aspartate specific protease(Caspase)-3,phosphoinositide 3-kinase(PI3K)and mitochondria-related proteins,are suggestive of its efficacy.Conclusion:BBR crucially improves DCM by maintaining mitochondrial integrity,inhibiting apoptosis,and modulating PI3K-AKT-GSK3βsignaling.Further studies must address animal model limitations and validate clinical efficacy to understand BBR’s mechanisms fully and its potential clinical use.

Neurotransmitter-mediated artificial synapses based on organic electrochemical transistors for future biomimic and bioinspired neuromorphic systems

Organic electrochemical transistors have emerged as a solution for artificial synapses that mimic the neural functions of the brain structure,holding great potentials to break the bottleneck of von Neumann architectures.However,current artificial synapses rely primarily on electrical signals,and little attention has been paid to the vital role of neurotransmitter-mediated artificial synapses.Dopamine is a key neurotransmitter associated with emotion regulation and cognitive processes that needs to be monitored in real time to advance the development of disease diagnostics and neuroscience.To provide insights into the development of artificial synapses with neurotransmitter involvement,this review proposes three steps towards future biomimic and bioinspired neuromorphic systems.We first summarize OECT-based dopamine detection devices,and then review advances in neurotransmitter-mediated artificial synapses and resultant advanced neuromorphic systems.Finally,by exploring the challenges and opportunities related to such neuromorphic systems,we provide a perspective on the future development of biomimetic and bioinspired neuromorphic systems.

Analysis of Renewable Energy Absorption and Economic Feasibility in Multi-Energy Complementary Systems under Spot Market Conditions

As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy output limit its profitability in the electricity market and hinder its market-based integration.This paper first constructs a wind-solar-thermalmulti-energy complementary system,analyzes its external game relationships,and develops a bi-level market optimization model.Then,it considers the contribution levels of internal participants to establish a comprehensive internal distribution evaluation index system.Finally,simulation studies using the IEEE 30-bus system demonstrate that the multi-energy complementary system stabilizes nodal outputs,enhances the profitability of market participants,and promotes the market-based integration of renewable energy.

Real-time debris flow monitoring and automated warning system

At present,debris flow warning uses precipitation threshold and issues regional warning throughout the world.Precipitation threshold warning is less accurate and in most of the time large portion of unaffected population are evacuated.More precise warning should use direct monitoring.There are many debris flow monitoring stations but no real time warning system in use.The main reason is that the identification and confirmation of debris flow occurrence requires human interaction and it is too slow.A debris flow monitoring and warning system has been installed in the midstream section of Yusui Stream,Taiwan China.The monitoring station operates fully automatically,providing early warnings without the need for manual intervention.The system comprises two webcam cameras,two Micro-Electro-Mechanical Systems(MEMS),and a rain gauge.The arrival of debris flows is detected and confirmed through both webcam images and MEMS signals.Once debris flow is detected,the system automatically issues a warning to the affected areas via voice messages,line messages,broadcasts,and web-based alerts.The webcam cameras are also used to estimate debris flow velocity and flow height,while the MEMS sensors are utilized to determine the phase speed and flow rate.On July 24th,2014,Typhoon Gaemi triggered several debris flows,and the system successfully issued several warnings automatically.The entire video record,along with depth variation data,was recorded automatically.

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Improving gastrointestinal scoring systems for predicting short-term mortality in critically ill patients

Shen et al’s retrospective study aims to compare the utility of two separate scoring systems for predicting mortality attributable to gastrointestinal(GI)injury in critically ill patients[the GI Dysfunction Score(GIDS)and the Acute Gastroin-testinal Injury(AGI)grade].The authors note that this study is the first proposal that suggests an equivalence between the ability of both scores to predict mor-tality at 28 days from intensive care unit(ICU)admission.Shen et al retrospec-tively analysed an ICU cohort of patients utilising two physicians administering both the AGI grade and GIDS score,using electronic healthcare records and ICU flowsheets.Where these physicians disagreed about the scores,the final decision as to the scores was made by an associate chief physician,or chief physician.We note that the primary reason for the development of GIDS was to create a clear score for GI dysfunction,with minimal subjectivity or inter-operator variability.The subjectivity inherent to the older AGI grading system is what ultimately led to the development of GIDS in 2021.By ensuring consensus between physicians administering the AGI,Shen et al have controlled for one of this grading systems biggest issues.We have concerns,however,that this does not represent the real-world challenges associated with applying the AGI compared to the newer GIDS,and wonder if this arbitration process had not been instituted,would the two scoring systems remain equivalent in terms of predicted mortality?

An Efficient Real-Time Fault-Tolerant Scheduling Algorithm Based on Multiprocessor Systems

In the context of real-time fault-tolerant scheduling in multiprocessor systems, Primary-backup scheme plays an important role. A backup copy is always preferred to be executed as passive backup copy whenever possible because it can take the advantages of backup copy de-allocation technique and overloading technique to improve schedulability. In this paper, we propose a novel efficient fault-tolerant ratemonotonic best-fit algorithm efficient fault-tolerant rate-monotonic best-fit （ERMBF） based on multiprocessors systems to enhance the schedulability. Unlike existing scheduling algorithms that start scheduling tasks with only one processor. ERMBF pre-allocates a certain amount of processors before starting scheduling tasks, which enlarge the searching spaces for tasks. Besides, when a new processor is allocated, we reassign the task copies that have already been assigned to the existing processors in order to find a superior tasks assignment configuration. These two strategies are all aiming at making as many backup copies as possible to be executed as passive status. As a result, ERMBF can use fewer processors to schedule a set of tasks without losing real-time and fault-tolerant capabilities of the system. Simulation results reveal that ERMBF significantly improves the schedulability over existing, comparable algorithms in literature.

Scheduling transactions in mobile distributed real-time database systems

A DMVOCC-MVDA （distributed multiversion optimistic concurrency control with multiversion dynamic adjustment） protocol was presented to process mobile distributed real-time transaction in mobile broadcast environments. At the mobile hosts, all transactions perform local pre-validation. The local pre-validation process is carried out against the committed transactions at the server in the last broadcast cycle. Transactions that survive in local pre-validation must be submitted to the server for local final validation. The new protocol eliminates conflicts between mobile read-only and mobile update transactions, and resolves data conflicts flexibly by using multiversion dynamic adjustment of serialization order to avoid unnecessary restarts of transactions. Mobile read-only transactions can be committed with no-blocking, and respond time of mobile read-only transactions is greatly shortened. The tolerance of mobile transactions of disconnections from the broadcast channel is increased. In global validation mobile distributed transactions have to do check to ensure distributed serializability in all participants. The simulation results show that the new concurrency control protocol proposed offers better performance than other protocols in terms of miss rate, restart rate, commit rate. Under high work load （think time is ls） the miss rate of DMVOCC-MVDA is only 14.6%, is significantly lower than that of other protocols. The restart rate of DMVOCC-MVDA is only 32.3%, showing that DMVOCC-MVDA can effectively reduce the restart rate of mobile transactions. And the commit rate of DMVOCC-MVDA is up to 61.2%, which is obviously higher than that of other protocols.

Improvement Design for Distributed Real-Time Stream Processing Systems

In the era of Big Data, typical architecture of distributed real-time stream processing systems is the combination of Flume, Kafka, and Storm. As a kind of distributed message system, Kafka has the characteristics of horizontal scalability and high throughput, which is manly deployed in many areas in order to address the problem of speed mismatch between message producers and consumers. When using Kafka, we need to quickly receive data sent by producers. In addition, we need to send data to consumers quickly. Therefore, the performance of Kafka is of critical importance to the performance of the whole stream processing system. In this paper, we propose the improved design of real-time stream processing systems, and focus on improving the Kafka's data loading process.We use Kafka cat to transfer data from the source to Kafka topic directly, which can reduce the network transmission. We also utilize the memory file system to accelerate the process of data loading, which can address the bottleneck and performance problems caused by disk I/O. Extensive experiments are conducted to evaluate the performance, which show the superiority of our improved design.

End-to-End Utilization Control for Aperiodic Tasks in Distributed Real-Time Systems

An increasing number of DRTS （Distributed model. The key challenges of such DRTS are guaranteeing Real-Time Systems） are employing an end-to-end aperiodic task utilization on multiple processors to achieve overload protection, and meeting the end-to-end deadlines of aperiodic tasks. This paper proposes an end-to-end utilization control architecture and an IC-EAT （Integration Control for End-to-End Aperiodic Tasks） algorithm, which features a distributed feedback loop that dynamically enforces the desired utilization bound on multiple processors. IC-EAT integrates admission control with feedback control, which is able to dynamically determine the QoS （Quality of Service） of incoming tasks and guarantee the end-to-end deadlines of admitted tasks. Then an LQOCM （Linear Quadratic Optimal Control Model） is presented. Finally, experiments demonstrate that, for the end-to-end DRTS whose control matrix G falls into the stable region, the IC-EAT is convergent and stable. Moreover,it is capable of providing better QoS guarantees for end-to-end aperiodic tasks and improving the system throughput.

Modeling and Design of Real-Time Pricing Systems Based on Markov Decision Processes

A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.

A fast and adaptive method for automatic weld defect detection in various real-time X-ray imaging systems

A first and effective method is proposed to detect weld deject adaptively in various Dypes of real-time X-ray images obtained in different conditions. After weld extraction and noise reduction, a proper template of median filter is used to estimate the weld background. After the weld background is subtracted from the original image, an adaptite threshold segmentation algorithm is proposed to obtain the binary image, and then the morphological close and open operation, labeling algorithm and fids＇e alarm eliminating algorithm are applied to pracess the binary image to obtain the defect, ct detection result. At last, a fast realization procedure jbr proposed method is developed. The proposed method is tested in real-time X-ray image,s obtairted in different X-ray imaging sutems. Experiment results show that the proposed method is effective to detect low contrast weld dejects with few .false alarms and is adaptive to various types of real-time X-ray imaging systems.

Modeling and Analysis of Scheduling for Distributed Real-time Embedded Systems

Aimed at the deficiencies of resources based time Petri nets （RBTPN） in doing scheduling analysis for distributed real-time embedded systems, the assemblage condition of complex scheduling sequences is presented to easily compute scheduling length and simplify scheduling analysis. Based on this, a new hierarchical RBTPN model is proposed. The model introduces the definition of transition border set, and represents it as an abstract transition. The abstract transition possesses all resources of the set, and has the highest priority of each resource; the cxecution time of abstract transition is the longest time of all possible scheduling sequences. According to the characteristics and assemblage condition of RBTPN, the refinement conditions of transition border set are given, and the conditions ensure the correction of scheduling analysis. As a result, it is easy for us to understand the scheduling model and perform scheduling analysis.

A State-of-the-Art Survey on Real-Time Issues in Embedded Systems Virtualization

Virtualization has gained great acceptance in the server and cloud computing arena. In recent years, it has also been widely applied to real-time embedded systems with stringent timing constraints. We present a comprehensive survey on real-time issues in virtualization for embedded systems, covering popular virtualization systems including KVM, Xen, L4 and others.

Effects of real-time traffic information systems on traffic performance under different network structures

The effects of real-time traffic information system(RTTIS)on traffic performance under parallel,grid and ring networks were investigated.The simulation results show that the effects of the proportion of RTTIS usage depend on the road network structures.For traffic on a parallel network,the performance of groups with and without RTTIS level is improved when the proportion of vehicles using RTTIS is greater than 0 and less than 30%,and a proportion of RTTIS usage higher than 90%would actually deteriorate the performance.For both grid and ring networks,a higher proportion of RTTIS usage always improves the performance of groups with and without RTTIS.For all three network structures,vehicles without RTTIS benefit from some proportion of RTTIS usage in a system.