Real-time Operating System Research Articles

Spark Distributed Real-Time Data and GPU Parallel Computing Based on 5G Virtual Reality

5G virtual reality has attracted many manufacturers and users with its unique immersion, interactivity and imagination characteristics, which has become the focus of new markets. The purpose of this article is to use Spark distributed real-time data system and GPU parallel computing to quickly process and analyze data. This article mainly designs a general-purpose real-time data analysis and processing system based on Spark, which mainly includes new ETL and real-time processing engine modules, and is committed to achieving higher real-time performance than traditional Hadoop. And realize fast calculation. At the same time there is universality and stability. Includes real-time flow calculations. Fast batch processing and machine learning The various types of data computers are included in this article by preparing the cutting device and adjusting the cutting output. The device is ready to effectively terminate the CUDA environment. The cudamalloc function is used to allocate a linear space of bytes to the device, and then transfer the data from the host to the device to determine the number of GPU blocks and threads. GPU parallel computing can increase the data processing speed by 27%, while the secondary programming algorithm can reduce the optimization time of the cup by 12%.

Evaluation of General-Purpose and Real-Time Operating Systems Performance for Multi-Robot Systems Path Planning

Evaluation of General-Purpose and Real-Time Operating Systems Performance for Multi-Robot Systems Path Planning

Research on electrical automation devices under embedded real-time operating system design

Abstract As the scale of the electrical system becomes more and more large and the structure becomes more and more complex, the performance of the electrical automation device is therefore required to be higher. This paper studies the electrical system automation device, and from the perspective of universality, designs and establishes the hardware platform of the electrical system automation device composed of six plug-ins, such as human-machine interface, auxiliary switch input, AC input, communication, power supply, and central processor. On this basis, μC/OS-II is adopted as the embedded real-time operating system of this paper to develop and design the software of the electrical system automation device. Then, the principle of the protection device for embedded system electrical automation is analyzed, and a microcomputer line protection device based on the full-circle Fuchs algorithm is proposed. The empirical analysis of the embedded system electrical automation device shows that the maximum system task response time is 145 μs and the minimum is 10 μs, which are all no more than 1 ms. When testing including the single board state, the keyboard, the alphabetical mode, the basic function, and the single board line module sending and receiving, the pass rate of all the testing items is more than 99%, which verifies that this paper is based on the embedded real-time operating system of the feasibility and effectiveness of the electrical automation device.

Exploration of Power-Savings on Multi-Core Architectures With Offloaded Real-Time Operating System

Exploration of Power-Savings on Multi-Core Architectures With Offloaded Real-Time Operating System

A Frequency Domain Approach to Predict Power System Transients

The dynamics of power grids are governed by a large number of nonlinear differential and algebraic equations (DAEs). To safely operate the system, operators need to check that the states described by these DAEs stay within prescribed limits after various potential faults. However, current numerical solvers of DAEs are often too slow for real-time system operations. In addition, detailed system parameters are often not exactly known. Machine learning approaches have been proposed to reduce the computational efforts, but existing methods generally suffer from overfitting and failures to predict unstable behaviors. This paper proposes a novel framework to predict power system transients by learning in the frequency domain. The intuition is that although the system behavior is complex in the time domain, there are relatively few dominant modes in the frequency domain. Therefore, we learn to predict by constructing neural networks with Fourier transform and filtering layers. System topology and fault information are encoded by taking a multi-dimensional Fourier transform, allowing us to leverage the fact that the trajectories are sparse both in time and spatial frequencies. We show that the proposed approach does not need detailed system parameters, greatly speeds up prediction computations and is highly accurate for different fault types.

Cocv: A compression algorithm for time-series data with continuous constant values in IoT-based monitoring systems

Sensor-generated time-series data now constitutes a significant and growing portion of the world’s data due to the rapid proliferation of the Internet of Things (IoT). The transmission and storage of such voluminous data have emerged as enormous challenges. Data compression and reduction strategies have been instrumental in mitigating these challenges to some extent. However, they have exhibited limitations when applied to real-time IoT-based monitoring systems. This stems from their failure to adequately consider the stringent requirements of real-time data transmission and the continuous constant-value redundancy within periodic monitoring data. Consequently, we introduce a dedicated compression algorithm tailored specifically for time-series data within periodic IoT-based monitoring systems, namely Cocv. It takes advantage of the continuous constant-value repetition of the time-series data to compress data by discarding redundant data points. It can not only compress static batches of data but also dynamically compress data streams to improve system performance in real-time IoT-based monitoring systems. The offline Cocv outperforms traditional compressors on gas-leak monitoring data with a compression ratio of 98.5%, maintaining a decent speed for both compression and decompression. In an actual IoT-based gas-leak monitoring system, the online Cocv improves handling capacity by 255%, reading speed by 728%, reduces bandwidth consumption by 94%, and storage space consumption by 98% compared to the original scheme.

The Real-time Operating System Capabilities for Control of Power Electronic Devices

The Real-time Operating System Capabilities for Control of Power Electronic Devices

Fuzzy proportional integral differential control of magnetorheological clutch torque based on bee colony algorithm optimization

As an intelligent transmission device, the magnetorheological (MR) clutch cannot realize its accurate torque control because it is difficult to establish its accurate torque control mathematical model. This paper designs a squeeze shear MR clutch. In order to achieve accurate torque control of the MR clutch, this paper proposes a fuzzy proportional integral differential (PID) control strategy based on the optimization of the bee colony algorithm. Based on the mechanical transmission experiment platform of the MR clutch, the performance of the torque control of the MR clutch is experimentally studied by using the real-time operating system Phar Lap ETS built by LabVIEW real-time module, it is found that the fuzzy PID control strategy based on bee colony algorithm can not only improve the torque control accuracy of the MR clutch by 70% compared to traditional PID control strategies but also increase the response speed by more than 50%.

Comparison and Verification of Three Algorithms for Accuracy Improvement of Quartz Resonant Pressure Sensors.

Pressure measurement is of great importance due to its wide range of applications in many fields. AT-cut quartz, with its exceptional precision and durability, stands out as an excellent pressure transducer due to its superior accuracy and stable performance over time. However, its intrinsic temperature dependence significantly hinders its potential application in varying temperature environments. Herein, three different learning algorithms (i.e., multivariate polynomial regression, multilayer perceptron networks, and support vector regression) are elaborated in detail and applied to establish the prediction models for compensating the temperature effect of the resonant pressure sensor, respectively. The AC-cut quartz, which is sensitive to temperature variations, is paired with the AT-cut quartz, providing the essential temperature information. The output frequencies derived from the AT-cut and AC-cut quartzes are selected as input data for these learning algorithms. Through experimental validation, all three methods are effective, and a remarkable improvement in accuracy can be achieved. Among the three methods, the MPR model has exceptionally high accuracy in predicting pressure. The calculated residual error over the temperature range of -10-40 °C is less than 0.008% of 40 MPa full scale (FS). An intelligent automatic compensation and real-time processing system for the resonant pressure sensor is developed as well, which may contribute to improving the efficiency in online calibration and large-scale industrialization. This paper paves a promising way for the temperature compensation of resonant pressure sensors.

Applying Learning and Self-Adaptation to Dynamic Scheduling

Real-world production scheduling scenarios are often not discrete, separable, iterative tasks but rather dynamic processes where both external (e.g., new orders, delivery shortages) and internal (e.g., machine breakdown, timing uncertainties, human interaction) influencing factors gradually or abruptly impact the production system. Solutions to these problems are often very specific to the application case or rely on simple problem formulations with known and stable parameters. This work presents a dynamic scheduling scenario for a production setup where little information about the system is known a priori. Instead of fully specifying all relevant problem data, the timing and batching behavior of machines are learned by a machine learning ensemble during operation. We demonstrate how a meta-heuristic optimization algorithm can utilize these models to tackle this dynamic optimization problem, compare the dynamic performance of a set of established construction heuristics and meta-heuristics and showcase how models and optimizers interact. The results obtained through an empirical study indicate that the interaction between optimization algorithm and machine learning models, as well as the real-time performance of the overall optimization system, can impact the performance of the production system. Especially in high-load situations, the dynamic algorithms that utilize solutions from previous problem epochs outperform the restarting construction heuristics by up to ~24%.

Simulating real-time tweet sentiment analysis by different machine learning methods based on spark

Sentiment analysis is essential since it benefits many fields, such as politics and economics. Because much data is generated every moment, a real-time processing system can efficiently analyze sentiment. This paper uses Spark to simulate real-time tweet sentiment analysis, and compares the performances of three machine learning methods, Logistic Regression, Naive Bayes, and Decision Tree. The idea of the real-time tweet sentiment analysis system is using Spark Streaming to send a batch of tweets every fixed period to a machine learning pipeline to predict the emotions of tweets. In the pipeline, tweets will be tokenized first, then the stop words in tweets will be removed. After that, the author uses TF-IDF to extract features, transferring data from unstructured to structured. The last stage is using the machine learning method to predict the sentiments of tweets. By comparing, Logistic Regression has the best performance, and the second one is Naive Bayes, Decision Tree performs not as well as the other two methods.

Fixed time adaptive fault tolerant sliding mode control of PEMFC air supply system

Guaranteeing rapid management of oxygen excess ratio (OER) for proton exchange membrane fuel cells (PEMFC) under load variations is a challenge with practical significance. To improve the controller response of PEMFC air supply system, a fixed time fault-tolerant control strategy is proposed in this paper. A fixed convergence time of PEMFC air supply control system is quantified and guaranteed by the proposed method and the control response can be accelerated by reducing the setting time. The contributions of this paper is fourfold. Firstly, the manifold leakage fault is estimated by fixed time fault observer, which can improve the real-time fault tracking performance of the control system. Secondly, a hierarchical fixed time sliding mode controller is proposed to regulate the OER rapidly. Furthermore, a novel adaptive term is employed to reduce the effects of the chattering from fault observer in the outer loop. Finally, the effectiveness of the proposed method is verified on a real-time PEMFC simulation platform. Experimental results on a Hardware-in-the-loop (HIL) platform show that the proposed fixed time fault tolerant control strategy has the characteristics of fast response and high robustness.

A Fused Deep Fuzzy Neural Network Controller and Its Application to Pneumatic Flexible Joint

With multilayered deep neural networks (DNNs) distinguished from the traditional single-layer neural networks (SNNs) by the depth, deep learning is a powerful paradigm possessing the capability of feature learning from big data. However, as a completely deterministic model, deep learning does not perform well in dealing with uncertain information and must be pretrained before being applied, which hinders the broad application of deep learning in real-time control. In this article, we introduce the knowledge-based fuzzy logic system (FLS) into deep learning and propose a fused deep fuzzy neural network (FDFNN) controller. The FDFNN controller derives the control signal from both deep fuzzy neural networks and the knowledge-based FLS. At the beginning of control, the FDFNN controller performs like a traditional fuzzy logic controller (FLC), which eliminates the pretraining procedure of deep learning. With another FLS providing supervised signal, the network weights of DFNN's hidden layers can be updated during the control process, which endows the FDFNN controller with online learning capability. A Lyapunov-based convergence analysis is conducted to guarantee the stability of the learning procedure. Both numerical simulations and real-world experiments validate that the proposed method effectively improves the real-time tracking control performance of a pneumatic flexible joint system.

Dual-Core PLC for Cooperating Projects with Software Implementation

Development of a general-purpose PLC based on a typical dual-core processor as a hardware platform is presented. The cores run two cooperating projects involving data exchange through shared memory. Such a solution is equivalent to a single-core PLC running two tasks by means of a real-time operating system. Upgrading to a typical programming tool involves defining which of the global variables are shared, and whether a variable in a particular core is read-from or written-to the shared memory. Extensions to core runtimes consist of read-from at the beginning of the scan cycle and write-to at the end, and of an algorithm for protecting the shared memory against access conflicts. As an example, the proposed solution is implemented in an engineering tool with runtime based on a virtual machine concept. The PLC prototype is based on a heterogeneous ARM dual-core STM32 microcontroller running different projects. The innovation in the research lies in showing how to run two projects in a dual-core PLC without using an operating system. Extension to multiple projects for a multi-core processor is can be accomplished in a similar manner.

Real-time simulation for detailed wind turbine model based on heterogeneous computing

The deployment of sophisticated and costly large-capacity wind turbines continues to rise. It is urgent for precise real-time simulation to accurately identify and address potential problems. However, the existing solutions, which joint various simulation platforms, suffer from the disadvantages of complex architecture and high cost. This paper proposes a CPU-FPGA heterogeneous computing-based real-time simulation platform for detailed wind turbine model (DWTM). DWTM encompasses the turbine model (covering the inflow wind, aerodynamic, and mechanical dynamic), the electrical model (covering the electromagnetic transient), and the control system. The real-time operating system is introduced to guarantee the real-time performance of the turbine model and control system in the CPU. FPGA is used to perform real-time electrical model calculation. Furthermore, a general FPGA solver for electromagnetic transients is designed. It can accommodate various circuit topologies without the need for recompiling the FPGA code. Finally, the real-time simulation performance and accuracy are verified. The coupled dynamics between the turbine and electrical models are investigated under multiple power grid and wind conditions.

TFF-CNN: Distributed optical fiber sensing intrusion detection framework based on two-dimensional multi-features

Distributed fiber optic vibration sensing system has been widely used in safety monitoring with distinct advantages, and the feature extraction and classification methods of fiber optic signals directly determine the real-time performance and reliability of the monitoring system. The existing research feature extraction methods are unidimensional and time consuming, which cannot balance the goals of high accuracy and low time consumption for safety monitoring systems. In this paper, we propose an efficient recognition framework for fusing signal time–frequency features, called TFF-CNN, based on Gramian Angular Difference Fields(GADF) and FFT co-generation matrix(FFTT) to manually extract two-dimensional time–frequency image features of fiber optic signals, taking the significant advantages of CNN in image processing and combining a two-channel model and a fusion module that simulates human decision-making behavior. The accuracy of TFF-CNN is 99.30 % and the detection response time is only 0.6 s. Compared with other methods in this field, TFF-CNN has the advantages of low false alarm rate and short time consumption, which is more suitable for deployment in security monitoring field with distributed fiber optic sensing system.Index Terms: Distributed optical fiber vibration sensing system(DVS), Two-dimensional multi-feature, CNN, intrusion detection, real-time monitoring.

Development of a multifunctional real-time data processing system for interferometers on EAST

In the latest campaign of EAST experiments, two new interferometers were installed, a dispersion interferometer (DI) based on a Carbon Dioxide laser and a solid source interferometer (SSI) based on microwave multiplier sources. To make them available for the Plasma Control System (PCS) system, each of them needs to be provided with a real-time processing system to extract the detector output signal and afterward obtain the electron density information through signal processing. To obtain interferometer data quickly and reliably, a unified hardware template was applied to both interferometers. Three main parts are included in this hardware template — digitization, digital signal processing, and output modules. In particular, the digitization section uses a multi-channel Analog-to-Digital Convertor (ADC) to acquire the signals that need to be calculated. The digital signal processing part is implemented by an FPGA, which is the hardware basis of this multifunctional template that can be used for multiple interferometers. This section includes two main parts, wrapped density signal extraction and signal unwrapping. For DI it is a phase calculation based on the intensity ratio, while for SSI it is a phase calculation by means of FFT or phase demodulation. Both computational approaches form independent IP cores to build systems quickly, while these IP cores provide parameter interfaces to adapt to different application scenarios. The output module provides a variety of data transmission methods, including fiber optic transmission and Digital-Analog-Convertor (DAC) based analog transmission, to interface with PCS or other subsequent systems.This data processing system template has been applied to each of the above two interferometers and valid data were obtained in a recent EAST experiment campaign. It demonstrates the usefulness of the template and provides a reference for the design of data processing systems on future devices.

Constant Power Control Against M/R With Expanded PT-Symmetric Range for Wireless in-Flight Charging of Drones

For the drone wireless in-flight charging system, the challenge is to keep a constant charging power under the large range of continuous fluctuation of mutual inductance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> ), the variation of battery load ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> ), and the limited payload of drones, which has been rarely studied in previous research. To address the issue, this paper presents a primary-series and secondary-series-inductor-series-parallel-capacitor (SLSPC) high-order topology for the parity-time (PT) symmetric wireless power transfer (WPT) system, which can greatly expand the exact PT-symmetric region (constant power range) by the reduction of critical coupling coefficient in comparison with the traditional series-series topology. Besides, the constant power control scheme is proposed based on the series-SLSPC high-order topology to overcome the change of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> . With the proposed control method, only the primary current needs to be detected, which enhances the real-time performance of the WPT system and reduces the payload of drones. Simulated and experimental results validate the expansion capability of the PT-symmetric range with the proposed series-SLSPC PT-WPT system and the feasibility of the proposed control scheme.

Task Scheduling of Real-Time Traffic Information Processing Based on Digital Twins

The Intelligent Transportation System under Digital Twins can provide accurate data sources for traffic control. The present work focuses on the real-time information processing and task scheduling problems of the Internet of Vehicles (IoV) system based on Virtual Reality. They are the Quality/Distance Algorithm (QDA), Task Density Algorithm, Distance Balance Algorithm (DBA), and Bionic-DBA (B-DBA). The simulation experiment analysis suggests that the DBA algorithm takes the balance of travel distance into account and effectively improves task quality. The Utility Function in B-DBA and the Biological Heuristic Search Algorithm in Pareto Ant Colony Optimization play a critically important role in enhancing the overall task quality. In addition, a Transmission based on Privacy Protection (TPP) algorithm is designed to protect the attribute-based privacy information in the traffic information transmission system. This algorithm ensures that the real-time traffic information processing system resists various attacks from malicious nodes. It has been verified that when the number of selfish nodes accounts for 30%, the transmission efficiency of the TPP algorithm reaches 0.77. The research content has a practical reference value for providing users with continuous and high-quality IoV network services.

Feasibility Constrained Online Calculation for Real-Time Optimal Power Flow: A Convex Constrained Deep Reinforcement Learning Approach

Due to the increasing uncertainties of renewable energy and stochastic demands, quick-optimal control actions are necessary to retain the system stability and economic operation. Existing optimal power flow (OPF) solution methods need to be enhanced to guarantee the solution optimality and feasibility in real-time operation under such uncertainties. This paper proposes a convex constrained soft actor-critic (CC-SAC) deep reinforcement learning (DRL) algorithm for the AC-OPF problem. First, this problem is standardized as a Markov decision process model to be solved by DRL algorithms. Second, the operational constraints are satisfied by a novel convex safety layer based on the penalty convex-concave procedure (P-CCP). Then, the control policy is updated by the state-of-the-art off-policy entropy maximization-based SAC algorithm. Therefore, the CC-SAC is a combination of data-driven and physics-driven approaches. The former speedups the solution time by predicting near-optimum control actions through a deep neural network. The latter effectively guarantees the solution feasibility. Simulation results demonstrate the computational performance of the proposed CC-SAC to effectively find AC-OPF decisions with no constraint violation, zero optimality gap and high speed up to 34 times compared to a state-of-the-art solver. The proposed approach indicates its practicability for power system real-time operation and marketing.