A real-time performance improvement method for composite time scale

The composite time scale(CTS)provides a stable,accurate,and reliable time scale for modern society.The improvement of CTS’s real-time performance will improve its stability,which strengths related applications’performance.Aiming at this goal,a method achieved by determining the optimal calculation interval and accelerating adjustment stage is proposed in this paper.The determinants of the CTS’s calculation interval(characteristics of the clock ensemble,the measurement noise,the time and frequency synchronization system’s noise and the auxiliary output generator noise floor)are studied and the optimal calculation interval is obtained.We also investigate the effect of ensemble algorithm’s initial parameters on the CTS’s adjustment stage.A strategy to get the reasonable initial parameters of ensemble algorithm is designed.The results show that the adjustment stage can be finished rapidly or even can be shorten to zero with reasonable initial parameters.On this basis,we experimentally generate a distributed CTS with a calculation interval of 500 s and its stability outperforms those of the member clocks when the averaging time is longer than1700 s.The experimental result proves that the CTS’s real-time performance is significantly improved.

Seismic performance evaluation of VCFPB isolated storage tank using real-time hybrid simulation

Variable curvature friction pendulum bearings(VCFPB)effectively reduce the dynamic response of storage tanks induced by earthquakes.Shaking table testing is used to assess the seismic performance of VCFPB isolated storage tanks.However,the vertical pressure and friction coefficient of the scaled VCFPB in the shaking table tests cannot match the equivalent values of these parameters in the prototype.To avoid this drawback,a real-time hybrid simulation(RTHS)test was developed.Using RTHS testing,a 1/8 scaled tank isolated by VCFPB was tested.The experimental results showed that the displacement dynamic magnification factor of VCFPB,peak reduction factors of the acceleration,shear force,and overturning moment at bottom of the tank,were negative exponential functions of the ratio of peak ground acceleration(PGA)and friction coefficient.The peak reduction factors of displacement,acceleration,force and overturning moment,which were obtained from the experimental results,are compared with those calculated by the Housner model.It can be concluded that the Housner model is applicable in estimation of the acceleration,shear force,and overturning moment of liquid storage tank,but not for the sliding displacement of VCFPBs.

Performance analysis model for real-time Ethernet-based computer numerical control system

In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.

ROS2 Real-time Performance Optimization and Evaluation

Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception,force interaction,spatial obstacle avoidance,and motion planning.To ensure the reliability and determinism of system execution,a flexible real-time control system architecture and interaction algorithm are required.The ROS framework was designed to improve the reusability of robotic software development by providing a distributed structure,hardware abstraction,message-passing mechanism,and application prototypes.Rich ecosystems for robotic development have been built around ROS1 and ROS2 architectures based on the Linux system.However,because of the fairness scheduling principle of the default Linux system design and the complexity of the kernel,the system does not have real-time computing.To achieve a balance between real-time and non-real-time computing,this paper uses the transmission mechanism of ROS2,combines it with the scheduling mechanism of the Linux operating system,and uses Preempt_RT to enhance the real-time computing of ROS1 and ROS2.The real-time performance evaluation of ROS1 and ROS2 is conducted from multiple perspectives,including throughput,transmission mode,QoS service quality,frequency,number of subscription nodes and EtherCAT master.This paper makes two significant contributions:firstly,it employs Preempt_RT to optimize the native ROS2 system,effectively enhancing the real-time performance of native ROS2 message transmission;secondly,it conducts a comprehensive evaluation of the real-time performance of both native and optimized ROS2 systems.This comparison elucidates the benefits of the optimized ROS2 architecture regarding real-time performance,with results vividly demonstrated through illustrative figures.

Performance evaluation of near real-time condition monitoring in haul trucks

Strategic maintenance plays a key role in ensuring high availability and utilization of the haul trucks,and as equipment began to grow more complex towards the end of the 20th century,there was a need for a proactive maintenance strategy,which led to the development of condition-based maintenance.Realtime condition monitoring(RTCM)is the ability to perform condition monitoring in real-time and has the ability to alert maintenance and operations of abnormal conditions.These alarms can be used as an indication leading to a problem,and if a suitable corrective action is initiated in time,it could result in significant savings of equipment downtime and repair costs.This study aims to compare some maintenance performance indicators prior to and after implementation of RTCM strategy at a mine site using some tests of statistical significance.The study also indicated the presence of seasonality in the data,and thus the data was deseasonalized and detrended prior to being subjected to the statistical tests.Finally,the results indicated that RTCM strategy has proven to be successful in improving the availability for some of the failure categories chosen in this study.

Optimizing Real-Time Performance of 3D Virtual Mining Environment with MultiGen Creator

System optimization plays a crucial role in developing VR system after 3D modeling, affecting the system's Immersion and Interaction performance enormously. In this article, several key techniques of optimizing a virtual mining system were discussed: optimizing 3D models to keep the polygon number in VR system within target hardware's processing ability; optimizing texture database to save texture memory with perfect visual effect; optimizing database hierarchy structure to accelerate model retrieval; and optimizing LOD hierarchy structure to speed up rendering.

Real-time performance of periodic data transmission in EPA industrial Ethernet

To evaluate and improve the real-time performance of Ethernet for plant automation（EPA） industrial Ethernet,the real-time performance of EPA periodic data transmission was theoretically and experimentally studied.By analyzing information transmission regularity and EPA deterministic scheduling mechanism,periodic messages were categorized as different modes according to their entering-queue time.The scheduling characteristics and delivery time of each mode and their interacting relations were studied,during which the models of real-time performance of periodic information transmission in EPA system were established.On this basis,an experimental platform is developed to test the delivery time of periodic messages transmission in EPA system.According to the analysis and the experiment,the main factors that limit the real-time performance of EPA periodic data transmission and the improvement methods were proposed.

Influence of Design Margin on Operation Optimization and Control Performance of Chemical Processes

Operation optimization is an effective method to explore potential economic benefits for existing plants. The m.aximum potential benefit from operationoptimization is determined by the distances between current operating point and process constraints, which is related to the margins of design variables. Because of various ciisturbances in chemical processes, some distances must be reserved for fluctuations of process variables and the optimum operating point is not on some process constraints. Thus the benefit of steady-state optimization can not be fully achied（ed while that of dynamic optimization can be really achieved. In this study, the steady-state optimizationand dynamic optimization are used, and the potential benefit-is divided into achievable benefit for profit and unachievable benefit for control. The fluid catalytic cracking unit （FCCU） is used for case study. With the analysis on how the margins of design variables influence the economic benefit and control performance, the bottlenecks of process design are found and appropriate control structure can be selected.

Real-time hybrid simulation of structures equipped with viscoelastic-plastic dampers using a user-programmable computational platform

A user-programmable computational/control platform was developed at the University of Toronto that offers real-time hybrid simulation （RTHS） capabilities. The platform was verified previously using several linear physical substructures. The study presented in this paper is focused on further validating the RTHS platform using a nonlinear viscoelastic-plastic damper that has displacement, frequency and temperature-dependent properties. The validation study includes damper component characterization tests, as well as RTHS of a series of single-degree-of-freedom （SDOF） systems equipped with viscoelastic-plastic dampers that represent different structural designs. From the component characterization tests, it was found that for a wide range of excitation frequencies and friction slip loads, the tracking errors are comparable to the errors in RTHS of linear spring systems. The hybrid SDOF results are compared to an independently validated thermal- mechanical viscoelastic model to further validate the ability for the platform to test nonlinear systems. After the validation, as an application study, nonlinear SDOF hybrid tests were used to develop performance spectra to predict the response of structures equipped with damping systems that are more challenging to model analytically. The use of the experimental performance spectra is illustrated by comparing the predicted response to the hybrid test response of 2DOF systems equipped with viscoelastic-plastic dampers.

Performance verification and comparison of TianLong automatic hypersensitive hepatitis B virus DNA quantification system with Roche CAP/CTM system

AIM To investigate and compare the analytical and clinical performance of Tian Long automatic hypersensitive hepatitis B virus(HBV) DNA quantification system and Roche CAP/CTM system.METHODS Two hundred blood samples for HBV DNA testing, HBV-DNA negative samples and high-titer HBV-DNA mixture samples were collected and prepared. National standard materials for serum HBV and a worldwide HBV DNA panel were employed for performance verification. The analytical performance, such as limit of detection, limit of quantification, accuracy, precision, reproducibility, linearity, genotype coverage and cross-contamination, was determined using the Tian Long automatic hypersensitive HBV DNA quantification system(TL system). Correlation and Bland-Altman plot analyses were carried out to compare the clinical performance of the TL system assay and the CAP/CTM system. RESULTS The detection limit of the TL system was 10 IU/m L, and its limit of quantification was 30 IU/m L. The differences between the expected and tested concentrations of the national standards were less than ± 0.4 Log10 IU/m L, which showed high accuracy of the system. Results of the precision, reproducibility and linearity tests showed that the multiple test coefficient of variation(CV) of the same sample was less than 5% for 102-106 IU/m L; and for 30-108 IU/m L, the linear correlation coefficient r2 = 0.99. The TL system detected HBV DNA(A-H) genotypes and there was no cross-contamination during the "checkerboard" test. When compared with the CAP/CTM assay, the two assays showed 100% consistency in both negative and positive sample results(15 negative samples and 185 positive samples). No statistical differences between the two assays in the HBV DNA quantification values were observed(P > 0.05). Correlation analysis indicated a significant correlation between the two assays, r2 = 0.9774. The Bland-Altman plot analysis showed that 98.9% of the positive data were within the 95% acceptable range, and the maximum difference was-0.49.CONCLUSION The TL system has good analytical performance, and exhibits good agreement with the CAP/CTM system in clinical performance.

REAL-TIME SOFTWARE SYSTEM FOR PRODUCTION PROCESS SUPERVISION

The real-time software system for production process supervision is a inte-gral system,including on-line function subsystem for supervision and off-line auxiliarysubsystem for development and diagnosis.This paper introduces a real-time software sys-tem which has been used in a power station for monitoring a large capacity thermal gener-ating unit.The subsystems,environment,performance and development of the system areexplained,and the common problems about real-time software system are described.

Design of Intelligent Network Performance Analysis & Forecast Support System

A system designed for supporting the network performance analysis and forecast effort is presented, based on the combination of offline network analysis and online real-time performance forecast. The off-line analysis will perform analysis of specific network node performance, correlation analysis of relative network nodes performance and evolutionary mathematical modeling of long-term network performance measurements. The online real-time network performance forecast will be based on one so-called hybrid prediction modeling approach for short-term network, performance prediction and trend analysis. Based on the module design, the system proposed has good intelligence, scalability and self-adaptability, which will offer highly effective network performance analysis and forecast tools for network managers, and is one ideal support platform for network performance analysis and forecast effort.

A Performance Analysis Tool for PVM Parallel Programs

In this paper,we introduce the design and implementation of ParaVT,which is a visual performance analysis and parallel debugging tool.In ParaVT,we propose an automated instrumentation mechanism.Based on this mechanism,ParaVT automatically analyzes the performance bottleneck of parallel applications and provides a visual user interface to monitor and analyze the performance of parallel programs.In addition,it also supports certain extensions.

Multiserver Multichannel Real-Time System with Limited Maintenance Facilities under Maximum Load

We consider a multi server and multichannel real-time system with identical servers (e.g. unmanned aerial vehicles, machine controllers, etc.) that provide services for requests of real-time jobs arriving via several different channels (e.g. surveillance regions, assembly lines, etc.) working under maximum load regime. Each channel has its own constant numbers of jobs inside at any instant. Each channel has its own specifications, and therefore different kinds of equipment and inventory are needed to serve different channels. There is a limited number of identical maintenance teams (less than the total number of servers in the system). We compute analytically steady- state probabilities of this system, its availability, loss penalty function and other performance characteristics, when both service and maintenance times are exponentially distributed.

Performance Evaluation of Multiple Unmanned Aerial Vehicles Operating in General Regime with Shortage of Maintenance Facilities

We consider a real-world problem of military intelligence unit equipped with multiple identical unmanned aerial vehicles (UAV) responsible for several regions (with requests of real-time jobs arriving from independent sources). We suppose that there are no ample maintenance facilities, allowing simultaneous treatment of all vehicles if necessary. Under certain assumptions, these real-time systems can be treated using a queueing theory methodology and/or as Markov chains. We show how to compute steady-state probabilities of these systems, their performance effectiveness, and various performance parameters (for exponentially distributed service and maintenance times of UAVs, as well as tasks duration and their arrival pattern).

大规模无线网络中(X,S)-Bottleneck区域性能分析

基于二层QBDs(quasi birth-death models),提出了一个(X,S)-bottleneck区域性能分析模型,(X,S)-bottleneck区域的特征通过第1层QBD模型描述,而经过(X,S)-bottleneck区域的每条流的特征由第2层QBD模型描述.基于该性能分析模型,对(X,S)-bottleneck区域的性能进行了一些概率分析,如(X,S)-bottleneck区域的平衡状态概率、经过该区域通信流的成功转发概率和吞吐量等,确定了上述概率及吞吐量的取值范围.仿真实验结果表明了该性能分析模型对于分析(X,S)-bottleneck区域性能的有效性.结论有助于无线网络通信性能的改进和管理.

Probabilistic Automata-Based Method for Enhancing Performance of Deep Reinforcement Learning Systems

Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management.However, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model modifications.First, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.

Exploratory Methodology for Identification of Urban Bottlenecks Using GPS Data

Bottlenecks have been widely studied for uninterrupted flow. The study of bottlenecks in urban traffic networks implies a challenge, since delays and queuing are implicit in interrupted flows. The objective of this paper is to discuss bottlenecks in urban traffic networks and present a methodology to identify them, based on GPS （Global Position System） data from taxis in Bogota Colombia. In networks, where stops are frequent, the principle we adopted deals with finding recurrent low-speed sections, beyond expected delays. Urban bottlenecks occur in those road network segments that perform poorly in terms of speed, compared with upstream and downstream conditions, producing recurrent and larger than normal delays. The GPS devices reported, via GPRS （General Packet Radio Service）, information every 10 s. Results of this exploratory project are promising. The method allowed the identification of six urban bottlenecks out of seven randomly selected low-speed sections. The most valuable application of this methodology is the prioritization of resources investment in traffic infrastructure improvements. As a low cost option, this exploratory method might be especially attractive in identifying critical points in traffic networks in developing cities, without expensive traffic-monitoring systems.

Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

浅析数据库SQL应用性能优化处理

随着大数据和人工智能时代的到来,以及在线远程办公、网络会议等办公方式的普及,各行各业都再在逐步向办公自动化、数据一体化的转型,很多应用场景被从线下搬到了线上。大量应用场景的使用让数据提取、传输和存储在性能优化面临着巨大挑战。数据库作为存储数据的主要载体,其性能的优化也成为了保证应用高效运行的一个重要的环节。为此作者从应用性能问题出现的原因、存在的风险、排查及处理等方面入手,就如何对造成应用的性能瓶颈问题进行排查和处理,提出一些见解和看法。