Search

The traditional text based system engineering method has some problems in the development of high complexity satellite Internet simulation platform, such as poor system design coordination and insufficient early simulation verification. The model-based systems engineering (MBSE) method was proposed to model the architecture of satellite Internet simulation platform. Firstly, a DV-MBSE and an integration verification architecture with external software were proposed. Secondly, based on MBSE methodology, the requirements analysis, functional decomposition and interaction structure modeling of the top architecture of the satellite Internet simulation platform were carried out. Finally, the validity of the model was verified by running logic verification and external model integration verification, thus supporting the design of satellite Internet. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of technology, unmanned vehicles have become a hot research topic in the field of intelligent transportation. Unmanned vehicles have many advantages, such as improving traffic efficiency, reducing traffic accidents, and reducing energy consumption. However, the controllability and safety of unmanned vehicles have always been a key issue in research. The Internet of Things can achieve information exchange and data sharing between vehicles, transportation facilities, traffic management centers, etc., providing real-time traffic and road condition information, and providing accurate data support for intelligent decision-making and path planning of unmanned vehicles. Therefore, the artificial intelligence unmanned vehicle control device based on IoT intelligent transportation has become an important research direction. This paper used deep reinforcement learning as the decision-making control algorithm, and designed a set of unmanned vehicle control system based on the DDPG (Deep Deterministic Policy Grad) algorithm, supplemented by meta DDPG algorithm, which is the knowledge of meta learning. Through the test of the simulation platform, it was concluded that the control system has good generalization. This study combined the Internet of Things and artificial intelligence algorithms, which has certain guiding significance for the development and application of unmanned vehicles in the future. [ABSTRACT FROM AUTHOR]

Copyright of South Architecture / Nanfang Jianzhu is the property of South Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Addressing the challenge of rehearsing large-scale equipment assembly, particularly for oversized components like wind turbine towers, hubs, nacelles, and blades, which often face quality issues such as substandard workmanship and wide tolerances during on-site assembly, a system has been developed for simulating wind turbine assembly. This system enables digital wind turbine assembly by creating virtual and production process models and employing intelligent database analysis. It resolves the problem of the absence of pre-production for large-scale equipment, meets the batch production needs of the wind turbine manufacturing industry, enhances the safety of wind turbine operations, improves operator assembly skills, and boosts production efficiency. This platform has already been implemented in the wind turbine manufacturing industry, yielding significant economic benefits. [ABSTRACT FROM AUTHOR]

In recent years, with the development of smart grids, people have increasingly high requirements for the safety, stability, and reliability of smart grid operation. In actual operation, smart grids require real-time monitoring and control, while for low-voltage power lines, power line carrier communication is required for data transmission. However, in practical applications on site, the low-voltage power line environment is complex, with ground faults, electrical equipment interference, and other situations that can affect communication quality and reliability, leading to a decrease in the effectiveness of real-time monitoring and control. The research in this paper is based on a complex low voltage platform simulation platform to restore the on-site complex substation communication environment. The purpose is to improve the efficiency of automatic meter reading systems by using various communication methods in low-voltage substations, such as wireless public network point-to-point data transmission, optical fiber communication data transmission, power line carrier data transmission, etc. Automatic meter reading systems are an important means of low voltage substation communication. This article experimentally tested the maximum personnel allocation for meter reading using low voltage carrier communication technology, which was 8 people smaller than the maximum personnel allocation for traditional meter reading, and the minimum personnel allocation was 8 people smaller than the traditional meter reading personnel allocation. The electricity accuracy of meter reading using low voltage carrier communication was up to 96 %, while the electricity accuracy of traditional meter reading was 70 %. The maximum accuracy of line loss statistics for low voltage carrier communication technology meter reading was 90 %, while the maximum accuracy of line loss statistics for traditional meter reading was 68 %. The frequency of load anomalies found in low-voltage carrier communication meter readings within a month ranged from 30 to 60 times, while the traditional meter readings concentrated on 10–25 times. From these data, it is clear that communication meter reading in low-voltage station areas has advantages over traditional meter reading. [ABSTRACT FROM AUTHOR]

This paper introduces a research method for locomotive-assisted driving systems based on simulation platform. Locomotive-assisted driving systems have become a key technology in improving driving safety and efficiency. However, conducting system testing directly on actual locomotives may pose risks and incur high costs. Therefore, the development of a simulation platform suitable for locomotive-assisted driving systems is necessary. This paper provides an overview of existing simulation platforms, analyzes their advantages and limitations, and proposes a design scheme for a simulation platform based on virtual reality technology. This platform can simulate various driving scenarios and traffic conditions, allowing researchers to conduct accurate performance evaluations and functional tests of locomotive-assisted driving systems. Finally, the effectiveness and feasibility of the proposed simulation platform are validated through tests, providing strong support for the development of locomotive-assisted driving systems. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Overhead Contact System (OCS) is the core equipment supporting the operation of electrified railway. Due to the need of robustness research and fault monitoring and diagnosis of OCS, a large number of different types of fault characteristic data are required, but this data is difficult to obtain from the railway in operation. Therefore, generating credible fault data through effective simulation is an effective means to study OCS fault in theory. Based on finite element method, using the force equivalent method to simulate all kinds of OCS faults, build the corresponding simulation model and establish the fault simulation platform, so as to generate a large number of OCS fault simulation data through the platform, which solves the problem that it is difficult to obtain fault data. According to the mechanism and mathematical characteristics of the fault, the fault model is divided into five categories, the equivalent methods of different fault types are established, the main framework of the platform is proposed, and the four functional modules of OCS parameter input, static solution, dynamic solution and data analysis are completed. The calculation example calculates the dynamic response of the OCS in a short time when the suspension string breaks and the shackle of the locator falls off at the specified position. It is found that when the suspension string fault occurs, the acceleration at the fault changes the most, and shows a attenuation trend with time. The farther away from the fault location, the less obvious the vertical vibration is. When the shackle fault occurs, the acceleration in the pull-out direction changes significantly, and the changes in the line direction and vertical direction fluctuate less, It shows that the platform can provide relevant qualitative and quantitative conclusions in OCS fault diagnosis, and provide theoretical support for OCS design, fault diagnosis and identification. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The JiuTian intelligent network simulation platform was proposed, which could provide wireless communication simulation data services for the open innovation platform.The platform contained a series of scalable simulator functionalities, offering open services that enable users to use reinforcement learning algorithms for model training and inference based on simulation environments and data.Additionally, it allowed users to address optimization tasks in different scenarios by uploading and updating parameter configurations.The platform and its open services were primarily introduced from the perspectives of background, overall architecture, simulator, business scenarios, and future directions.

Featured Application: The findings presented herein can be directly applied for the development of navigation algorithms in gnss-denied environments. Accurate localization is the foundation of unmanned aerial vehicle (UAV) swarm applications in the global navigation satellite system (GNSS)-denied environment. However, the implementation of UAV formation in the real world is costly and time-consuming, which leads to difficulties in developing navigation algorithms. A real-time simulator for navigation in GNSS-denied environments is proposed, which includes world, model, controller, scene matching navigation (SMN), relative navigation and formation controller modules. Each module can be modified, which means that users can test their own algorithms. A novel inertial-aided SMN (ISMN) algorithm is developed and a relative navigation method that does not rely on inter communication is proposed. ISMN and relative navigation based on a camera and ultrawideband (UWB) are tested on the platform. Based on the developed simulation system, the navigation algorithms can be verified easily, which can reduce the time and personnel requirements during flight testing. [ABSTRACT FROM AUTHOR]

Originating from the concept of cognitive networks (CNs), which are becoming popular in wireless terrestrial communication scenarios, underwater acoustic cognitive networks (UACNs) are drawing more and more attention in the field of the Internet of Underwater Things (IoUT). However, as the implementation of cognitive mechanisms in underwater acoustic networks is different from that of wireless scenarios, it is impossible or difficult for traditional simulation platforms to carry out simulations of UACNs. There is a lack of specialized simulation tools in terms of UACNs. To enable the quantitative evaluation of the effectiveness and performance enhancement of a UACNs in an adverse underwater environment, a simulation platform of acoustic cognitive networks (SPACNet) was designed and investigated in this article. First, based on a state machine-based protocol programming framework, the SPACNet is capable of supporting the implementation of different state-transform types associated with cognitive networking protocols. Moreover, to facilitate the realization of cognitive function at comprehensive levels of signal, information, and link, an underwater acoustic channel model with an environmental parameter input is integrated in SPACNet to generate underwater environment-driven multiple-aspect behaviors. Moreover, a simplified collision model consisting of an environment factor, channel response, and node location is used to reduce the complexity of the simulation of UACNs signal reception. A simulation was carried out to verify the effectiveness of SPACNet in evaluating the cognitive capabilities of UACNs. Finally, a field UACNs experiment was performed to validate the general consistency between the conclusion obtained with the SPACNet-based simulation and that from the field test. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The autonomous driving technology based on deep reinforcement learning (DRL) has been confirmed as one of the most cutting-edge research fields worldwide. The agent is enabled to achieve the goal of making independent decisions by interacting with the environment and learning driving strategies based on the feedback from the environment. This technology has been widely used in end-to-end driving tasks. However, this field faces several challenges. First, developing real vehicles is expensive, time-consuming, and risky. To further expedite the testing, verification, and iteration of end-to-end deep reinforcement learning algorithms, a joint simulation development and validation platform was designed and implemented in this study based on VTD–CarSim and the Tensorflow deep learning framework, and research work was conducted based on this platform. Second, sparse reward signals can cause problems (e.g., a low-sample learning rate). It is imperative for the agent to be capable of navigating in an unfamiliar environment and driving safely under a wide variety of weather or lighting conditions. To address the problem of poor generalization ability of the agent to unknown scenarios, a deep deterministic policy gradient (DDPG) decision-making and planning method was proposed in this study in accordance with a multi-task fusion strategy. The main task based on DRL decision-making planning and the auxiliary task based on image semantic segmentation were cross-fused, and part of the network was shared with the main task to reduce the possibility of model overfitting and improve the generalization ability. As indicated by the experimental results, first, the joint simulation development and validation platform built in this study exhibited prominent versatility. Users were enabled to easily substitute any default module with customized algorithms and verify the effectiveness of new functions in enhancing overall performance using other default modules of the platform. Second, the deep reinforcement learning strategy based on multi-task fusion proposed in this study was competitive. Its performance was better than other DRL algorithms in certain tasks, which improved the generalization ability of the vehicle decision-making planning algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digital twins can improve the level of control over physical entities and help manage complex systems by integrating a range of technologies. The autonomous agricultural machine has shown revolutionary effects on labor reduction and utilization rate in field works. Autonomous vehicles in precision agriculture have the potential to improve competitiveness compared to current crop production methods and have become a research hotspot. However, the development time and resources required in experiments have limited the research in this area. Simulation tools in unmanned farming that are required to enable more efficient, reliable, and safe autonomy are increasingly demanding. Inspired by the recent development of an open-source virtual simulation platform, this study proposed an autoware-based simulator to evaluate the performance of agricultural machine guidance based on digital twins. Oblique photogrammetry using drones is used to construct threedimensional maps of fields at the same scale as reality. A communication format suitable for agricultural machines was developed for data input and output, along with an inter-node communication methodology. The conversion, publishing, and maintenance of multiple coordinate systems were completed based on ROS (Robot Operating System). Coverage path planning was performed using hybrid curves based on Bézier curves, and it was tested in both a simulation environment and actual fields with the aid of Pure Pursuit algorithms and PID controllers. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: The model and analysis results established in this paper can be used for dynamic analyses of the autonomous driving control of articulated steering vehicles, as well as for the design of hybrid or pure electric power articulated steering vehicles. Articulated steering vehicles (ASVs), with brilliant maneuverability and efficiency, are being widely applied in mining, construction, agriculture, and forestry. However, their special structures result in them having complex dynamic characteristics, but there are no reliable models for further research. This study established a simulation platform with the dynamic model of ASVs, where the subsystems of the power train, steering systems, tires, and frames were also included. The dynamic model was validated with field test data of typical working cycles, in which the focus was on longitudinal and lateral motions and the characteristics of steering and power train systems. Then, the distribution of hydraulic and drive power was revealed using the simulation platform and test data. For a load–haul–dump (LHD) vehicle with a 6 m3 capacity, the maximum power of the system was about 289 kW; the power of the motor accounted for the majority of the power at the beginning stage of loading, being about 74%, and then the hydraulic power dominated in the later stage of loading. During the transport stage, the power of the motor accounted for about 79% of the total power. Finally, the influence of the dynamic parameters on lateral and longitudinal motions was analyzed based on the validated platform. [ABSTRACT FROM AUTHOR]

Simulation platforms are critical and indispensable tools for application developments of unmanned aerial vehicles (UAVs) because the UAVs are generally costly, have certain requirements for the test environment, and need professional licensed operators. Thus, developers prefer (or have) to test their applications on simulation platforms before implementing them on real machines. In the past decades, a considerable number of simulation platforms for robots have been developed, which brings convenience to developers, but also makes them hard to choose a proper one as they are not always familiar with all the features of platforms. To alleviate this dilemma, this paper provides a survey of open-source simulation platforms and employs the simulation of a multi-copter UAV swarm as an example. The survey covers seven widely used simulators, including Webots, Gazebo, CoppeliaSim, ARGoS, MRDS, MORSE, and USARSim. The paper outlines the requirements for multi-copter UAV swarms and shows how to select an appropriate platform. Additionally, the paper presents a case study of a UAV swarm based on Webots. This research will be beneficial to researchers, developers, educators, and engineers who seek suitable simulation platforms for application development, (not only multi-copter UAV swarms but also other types of robots), which further helps them to save expenses for testing, and speed up development progress. [ABSTRACT FROM AUTHOR]

Biosensors are a type of sensor that have gained prominence in recent years due to their advantages over traditional sensing methods, which are expensive and time-consuming. They are composed of two main parts: a recognizer bioelement and a transducer—both can be of different types depending on the desired application. An optical biosensor based on Surface Plasmon Resonance (SPR) achieves high sensitivity, is label-free and its multilayer construction allows an increase in the selectivity of the target analyte. As the choice of layers in SPR-based biosensors and the analysis of the obtained multilayer configuration is very difficult and expensive, in this work, we present an SPR-based biosensor simulation tool, developed through mathematical modeling, with an easy-to-use interface and several design options for calculating and analyzing the reflectance and angle of incidence of this type of device. The application, developed in Matlab, behaved as expected, offering the user the possibility to export data to Excel and becoming a useful alternative for mathematical analysis of SPR biosensors and its parameters, such as quality factor, detection accuracy and sensitivity. [ABSTRACT FROM AUTHOR]

The study of human factors in aviation makes an important contribution to safety. Within this discipline, real-time simulations (RTS) are a very powerful tool. The use of simulators allows for exercises with controlled air traffic control (ATC) events to be designed so that their influence on the performance of air traffic controllers (ATCOs) can be studied. The CRITERIA (atC event-dRiven capacITy modEls foR aIr nAvigation) project aims to establish capacity models and determine the influence of a series of ATC events on the workload of ATCOs. To establish a correlation between these ATC events and neurophysiological variables, a previous step is needed: a methodology for defining the taskload faced by the ATCO during the development of each simulation. This paper presents the development of this methodology and a series of recommendations for extrapolating the lessons learnt from this line of research to similar experiments. This methodology starts from a taskload design, and after RTS and through the use of data related to the subjective evaluation of workload as an intermediate tool it allows the taskload profile experienced by the ATCO in each simulation to be defined. Six ATCO students participated in this experiment. They performed four exercises using the SkySim simulator. As an example, a case study of the analysis of one of the participants is presented. [ABSTRACT FROM AUTHOR]

AC/DC hybrid microgrids are becoming potentially more attractive due to the proliferation of renewable energy sources, such as photovoltaic generation, battery energy storage systems, and wind turbines. The collaboration of AC sub-microgrids and DC sub-microgrids improves operational efficiency when multiple types of power generators and loads coexist at the power distribution level. However, the voltage stability analysis and software validation of AC/DC hybrid microgrids is a critical concern, especially with the increasing adoption of power electronic devices and various types of power generation. In this manuscript, we investigate the modeling of AC/DC hybrid microgrids with grid-forming and grid-following power converters. We propose a rapid simulation technique to reduce the simulation runtime with acceptable errors. Moreover, we discuss the stability of hybrid microgrids with different types of faults and power mismatches. In particular, we examine the voltage nadir to evaluate the transient stability of the hybrid microgrid. We also design a droop controller to regulate the power flow and alleviate voltage instability. During our study, we establish a Simulink-based simulation platform for operational analysis of the microgrid. [ABSTRACT FROM AUTHOR]

With the enormous growth of sensing devices tending to the use of Internet of everything, data aggregated by these devices are the biggest data streams generated in the history of IT. Thus, aggregating such data in the cloud for leveraging powerful cloud computing processing and storage is essential, and it eventually led to the emergence of Sensor-Cloud concept. This has allowed aggregation of the sensors' data to the cloud for further processing, storage, and visualization. Furthermore, virtualization makes the sensors accessible to other end-user applications that require such data. All of these features are expected to be provided by the Sensor-Cloud invisibly, without the end-user application developer being aware of the sensor location or hardware specifications. For these reasons, a simulation platform where Sensor-Cloud infrastructure agents and components may be modeled, scheduling policies defined, and execution time assessed is essential to assure performance and quality of service. The aim of this study is to develop such a platform by enhancing CloudSim, the most well-known and powerful simulation tool for cloud computing. A user-friendly Java Script Swing-based graphical user interface (GUI) has been carefully designed and implemented for this purpose. The user can then utilize the specific interface to define the Cloudlet type as well as the scheduling on a single virtual machine. Finally, a simulation study is carried out on the platform to demonstrate its efficiency and accuracy. We were able to fully model the needed scenarios and acquire real-time results, displaying good accuracy in terms of application response time with a mean absolute percentage error (MAPE) of 3.37%, demonstrating the increased proposed platform's proper operation. [ABSTRACT FROM AUTHOR]

The potential security problems of blockchain technology are constantly restricting the development process of related industrial applications. The cost of deploying a blockchain system in a real environment to conduct research on security issues is relatively high, and the related security analysis and verification are also destructive and irreproducible. Therefore, based on the idea of layered design, this paper proposes a blockchain system simulation platform. The blockchain system is divided into four layers in the simulation platform: the consensus layer, network layer, contract layer, and storage layer. In the consensus layer, the problem of computing resource waste is solved. In the network layer, a peer-to-peer network topology simulation is implemented. In the storage layer, the problem of redundant storage is solved. In the contract layer, the contract replay speed is accelerated. Finally, a prototype of an efficient blockchain simulation system is implemented based on the above methods. [ABSTRACT FROM AUTHOR]

Substantial changes are occurring in electric distribution systems due to ambitious targets towards carbon-neutrality in many regions around the world. One of the key challenges is how to analyze the interactions of massive amount of energy end-users with the electric distribution grid operator. In this paper, we introduce a comprehensive simulation platform, AI4Dist, that is capable to perform a wide collection of distribution system studies that capture multiple timescales ranging from market planning to transient event analysis. AI4Dist is designed to effortlessly integrate with off-the-shelf machine learning packages and algorithm implementations. We envision that AI4Dist will serve as a platform to empower researchers with different expertise to contribute to the development of low carbon electricity sector.

This paper describes the design and implementation of a digital twin model in the open-source MuPIF simulation platform. MuPIF enables a user-defined data model based on an ontology or schema to be created. A representation of the data model is generated in a target data management system. The data model, integrated with MuPIF, lets model entities to be linked, and model attributes can be assigned to simulation workflows inputs and outputs. The model is semantically-defined, provides full traceability, and has a web-based API for data discovery. [ABSTRACT FROM AUTHOR]

Accurate localization is the foundation of unmanned aerial vehicle (UAV) swarm applications in the global navigation satellite system (GNSS)-denied environment. However, the implementation of UAV formation in the real world is costly and time-consuming, which leads to difficulties in developing navigation algorithms. A real-time simulator for navigation in GNSS-denied environments is proposed, which includes world, model, controller, scene matching navigation (SMN), relative navigation and formation controller modules. Each module can be modified, which means that users can test their own algorithms. A novel inertial-aided SMN (ISMN) algorithm is developed and a relative navigation method that does not rely on inter communication is proposed. ISMN and relative navigation based on a camera and ultrawideband (UWB) are tested on the platform. Based on the developed simulation system, the navigation algorithms can be verified easily, which can reduce the time and personnel requirements during flight testing.

Originating from the concept of cognitive networks (CNs), which are becoming popular in wireless terrestrial communication scenarios, underwater acoustic cognitive networks (UACNs) are drawing more and more attention in the field of the Internet of Underwater Things (IoUT). However, as the implementation of cognitive mechanisms in underwater acoustic networks is different from that of wireless scenarios, it is impossible or difficult for traditional simulation platforms to carry out simulations of UACNs. There is a lack of specialized simulation tools in terms of UACNs. To enable the quantitative evaluation of the effectiveness and performance enhancement of a UACNs in an adverse underwater environment, a simulation platform of acoustic cognitive networks (SPACNet) was designed and investigated in this article. First, based on a state machine-based protocol programming framework, the SPACNet is capable of supporting the implementation of different state-transform types associated with cognitive networking protocols. Moreover, to facilitate the realization of cognitive function at comprehensive levels of signal, information, and link, an underwater acoustic channel model with an environmental parameter input is integrated in SPACNet to generate underwater environment-driven multiple-aspect behaviors. Moreover, a simplified collision model consisting of an environment factor, channel response, and node location is used to reduce the complexity of the simulation of UACNs signal reception. A simulation was carried out to verify the effectiveness of SPACNet in evaluating the cognitive capabilities of UACNs. Finally, a field UACNs experiment was performed to validate the general consistency between the conclusion obtained with the SPACNet-based simulation and that from the field test.