Real-time Dynamic System Research Articles

Design of Intelligent Firefighting and Smart Escape Route Planning System Based on Improved Ant Colony Algorithm.

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system's capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties.

Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving

Automated vehicles are a significant advancement in transportation technique, which provides safe, sustainable, and reliable transport. Lane detection, maneuver forecasting, and traffic sign recognition are the fundamentals of automated vehicles. Hence, this research focuses on developing a dynamic real-time decision-making system to obtain an effective driving experience in autonomous vehicles with the advancement of deep learning techniques. The deep learning classifier such as deep convolutional neural network (Deep CNN), SegNet and are utilized in this research for traffic signal detection, road segmentation, and lane detection. The main highlight of the research relies on the proposed Finch Hunt optimization, which involves the hyperparameter tuning of a deep learning classifier. The proposed real-time decision-making system achieves 97.44% accuracy, 97.56% of sensitivity, and 97.83% of specificity. Further, the proposed segmentation model achieves the highest clustering accuracy with 90.37% and the proposed lane detection model attains the lowest mean absolute error, mean square error, and root mean error of 17.76%, 11.32%, and 5.66% respectively. The proposed road segmentation model exceeds all the competent models in terms of clustering accuracy. Finally, the proposed model provides a better output for lane detection with minimum error, when compared with the existing model.

Real-Time Direction Judgment System for Dual-Frequency Laser Interferometer.

Current real-time direction judgment systems are inaccurate and insensitive, as well as limited by the sampling rate of analog-to-digital converters. To address this problem, we propose a dynamic real-time direction judgment system based on an integral dual-frequency laser interferometer and field-programmable gate array technology. The optoelectronic signals resulting from the introduction of a phase subdivision method based on the amplitude resolution of the laser interferometer when measuring displacement are analyzed. The proposed system integrates the optoelectronic signals to increase the accuracy of its direction judgments and ensures these direction judgments are made in real time by dynamically controlling the integration time. Several experiments were conducted to verify the performance of the proposed system. The results show that, compared with current real-time direction judgment systems, the proposed system makes accurate judgements during low-speed motions and can update directions within 0.125 cycles of the phase difference change at different speeds. Moreover, a sweep frequency experiment confirmed the system's ability to effectively judge dynamic directions. The proposed system is capable of accurate and real-time directional judgment during low-speed movements of a table in motion.

Real-Time Three-dimensional Dynamic Navigation for Post Space Preparation in Root Canal-Treated Teeth: An In vitro Study

IntroductionThe aim of this study was to investigate the feasibility of a real-time three-dimensional dynamic navigation system (3D-DNS) for post space preparation (PSP) in root canal-treated teeth and to compare the accuracy and efficiency of 3D-DNS to freehand (FH) for PSP. MethodsFifty-four maxillary molars were divided into two groups: 3D-DNS (n = 27) and FH group (n = 27). Cone beam computed tomography (CBCT) scans were taken preoperatively and postoperatively. The drilling path for the PSP was virtually planned in the preoperative CBCT scan in the X-guide software (X-Nav Technologies, Lansdale, PA). For the 3D-DNS group, the PSP drilling was conducted under dynamic navigation. The 3D deviations and angular deflections were calculated. The residual dentin thickness (RDT) was determined after PSP. The operation time and the total number of mishaps were recorded. Shapiro-Wilk, t-test or Mann-Whitney rank sum, weighted Cohen's kappa, and Fisher exact tests were used (P < .05). ResultsThe PSP was completed in all samples (54/54). The 3D-DNS was more accurate than the FH, with significantly fewer 3D deviations and angular deflections (all, P < .05). The 3D-DNS required less operating time than the FH (P < .05). For the 3D-DNS, no teeth had RDT < 1 mm, whereas 6/27 in the FH showed RDT < 1 mm after the PSP. There was no difference in the total number of mishaps (P > .05). ConclusionWithin the limitations of this in vitro study, the 3D-DNS is feasible for PSP. The 3D-DNS improved the accuracy and efficiency of PSP. The dynamic navigation system can potentially become a safe and reliable technology for PSP.

Performance analysis of real-time multi-wavelength nonlinear DRA for the next-generation photonic network

A new disintegrated channelization approach for the backward pumped nonlinear distributed Raman amplifier is proposed by enacting the differential coupled equations of Raman amplification, in fiber, trailing by the solution of NLSE using the first integral method first time to the best of our knowledge. A nonlinear channel modeling-based simulation is adapted for the approximation of the Raman gain profile. To realize the real-time dynamic system, 16 multiplexed channels of prime C-band are employed in a QPSK/NRZ modulation environment with a 100 Gbps bit rate for optical network reconfiguration. A non-uniform gain is reported while the disparity in BER is observed from 10−12 to 10−8.

Prospects of Digital Twin for Dynamic Life Cycle Assessment of Smart Manufacturing Systems

Smart manufacturing systems are poised to revolutionize industrial processes by leveraging advanced technologies for increased efficiency and productivity. However, alongside these advancements, there is a growing imperative to address environmental sustainability concerns. Conventional static life cycle assessment (LCA) methods often provide valuable insights into the environmental impacts of such manufacturing systems but often fall short in capturing real-time data and dynamic system interactions. Further, using the digital twin technology, physical assets can be virtually replicated in order to monitor, evaluate, and improve the particular manufacturing system. The dynamic properties can be effectively brought to LCA investigations by utilizing this technique. This paper explores the prospects of integrating digital twin technology for facilitating the dynamic LCA to enable comprehensive and timely environmental performance evaluation of smart manufacturing systems. We discuss the concepts, technological components, and potential applications of digital twin-enabled dynamic LCA, along with challenges and future research directions.

Demand-Oriented Review of a Dynamic Energy-Loss Monitoring System for Primary School Buildings through Micro-Environmental Data Monitoring and Occupant Behavior Analysis

The utilization of primary school buildings is multifaceted, primarily due to the high occupancy density, varying thermal preferences among occupants, diverse indoor activities (such as walking, sports, and conversation), and a constant flow of individuals entering and exiting the building. This results in the frequent opening and closing of external windows and doors and fluctuations in internal heat gain. Consequently, frequent interactions between the indoor and outdoor microenvironments lead to energy losses. This study conducts a comprehensive literature review on building energy loss stemming from occupant behavior and the interactions between indoor and outdoor microenvironments. Furthermore, it proposes a dynamic real-time monitoring system based on a foundation of computer data capture and a visualization platform for building energy loss. The research methods include data crawling, data association rule mining, and data association analysis. The research findings yield a universally applicable and informative building energy-saving design system based on extensive data analysis. Additionally, the system presents information on occupants’ behavior and the microclimate data of indoor and outdoor environments on a computer screen, facilitating human–machine communication and enabling timely adjustments to be made, thus facilitating the construction of design strategies for new buildings and operation and maintenance strategies for existing buildings.

Realtime Recognition of Dynamic Hand Gestures in Practical Applications

Dynamic hand gesture acting as a semaphoric gesture is a practical and intuitive mid-air gesture interface. Nowadays benefiting from the development of deep convolutional networks, the gesture recognition has already achieved a high accuracy, however, when performing a dynamic hand gesture such as gestures of direction commands, some unintentional actions are easily misrecognized due to the similarity of the hand poses. This hinders the application of dynamic hand gestures and cannot be solved by just improving the accuracy of the applied algorithm on public datasets, thus it is necessary to study such problems from the perspective of human-computer interaction. In this article, two methods are proposed to avoid misrecognition by introducing activation delay and using asymmetric gesture design. First the temporal process of a dynamic hand gesture is decomposed and redefined, then a realtime dynamic hand gesture recognition system is built through a two-dimensional convolutional neural network. In order to investigate the influence of activation delay and asymmetric gesture design on system performance, a user study is conducted and experimental results show that the two proposed methods can effectively avoid misrecognition. The two methods proposed in this article can provide valuable guidance for researchers when designing realtime recognition system in practical applications.

Retracted: Researching a Simulation of Real-Time Nonlinear Dynamical Systems for Digital Power Grids in Massive IoT

Retracted: Researching a Simulation of Real-Time Nonlinear Dynamical Systems for Digital Power Grids in Massive IoT

MADM-Q, an efficient multi-attribute decision-making support system for offshore decommissioning

With decades of development, some offshore facilities are reaching their design lives and need to be decommissioned. However, due to the limitation of regulations, technologies, and other aspects, which decommissioning options should be chosen is a challenging task. This paper introduces a multi-attribute quantitative evaluation system called MADM-Q (Multi-Attribute Decision Making-Quantitative), which can show decision makers with decommissioning options’ cost, risk, and environmental impact assessment results. The system consists of an input, output, and three processing modules. The Engineering Cost Evaluation System (ECES) is the first bottom-up approach cost assessment model in offshore facility decommissioning research. The Hierarchical Analyst Domino Evaluation System (HADES) acts as the risk assessment module, considering Domino Effect Accidents (DEAs) and providing accurate and rapid quantitative risk assessment results. The third sub-model, Composite Impact Evaluation System (CIES) assesses the environmental and socioeconomic impacts of the project. The system can obtain the basic cost range and the Individual Risk Per Annum (IRPA) value of the project quickly by using some simple inputs. The results of ECES and HADES reflects excellent accuracy - 12% cost evaluation average deviation and 1.43E-04 IRPA evaluation bias. Although currently, the HADES is a quasi-dynamic risk assessment method, it would be easy to develop it into a real-time dynamic risk monitoring and assessment system by combining advanced technologies such as digital twin technology and sensor technology.

Enhanced reinforcement learning in two-layer economic model predictive control for operation optimization in dynamic environment

Economic model predictive control (EMPC) has attracted an abundance of interest in both academic and industrial communities in recent years because it is able to increase the economic profits of dynamic systems. For greater computational efficiency, two-layer EMPC schemes are applied in some complex process control. However, the performance of two-layer EMPC is significantly influenced by the accuracy of the chosen process model. Reinforcement learning (RL) has been studied as a model-free strategy of model-based control approaches, but its safety and stability remain a concern. In order to estimate the model parameters of nonlinear dynamic systems in real time, this work introduces a unique scheme for merging two-layer EMPC and RL. In this scheme, the two-layer EMPC technique maintains closed-loop stability and recursive feasibility while operating the closed-loop dynamic system optimally. And the RL agent continually compares the observed states to the predictions made by the EMPC and modifies the time-varying parameters as necessary. The usability of the proposed scheme is shown on a chemical process of ethylene oxide production in dynamic environment. This work enables online and continuous control, optimization, and model correction, and makes process production optimization more feasible and profitable.

Intelligent real-time identification technology of stratum characteristics during slurry TBM tunneling

The rapid and accurate identification of geological characteristics during TBM tunneling is a common concern in engineering. A set of real-time dynamic response monitoring and analysis systems was installed in the slurry TBM of a water delivery tunnel in Beijing to analyze the variation of geological characteristics during TBM tunneling. The response mechanism and time–frequency characteristics of slurry TBM acceleration in five strata were deeply studied. The variation trends of thrust, torque and acceleration response characteristic values when strata change were compared, and the results show that the acceleration response is extremely sensitive to strata change. An LSTM network was established that considers slurry TBM driving parameters and acceleration response characteristic values as input parameters and realizes the intelligent identification of five kinds of strata with an accuracy of 97.4% and an F1-Score of 96.38%.

Virtual reality in medical emergencies training: benefits, perceived stress, and learning success

Medical graduates lack procedural skills experience required to manage emergencies. Recent advances in virtual reality (VR) technology enable the creation of highly immersive learning environments representing easy-to-use and affordable solutions for training with simulation. However, the feasibility in compulsory teaching, possible side effects of immersion, perceived stress, and didactic benefits have to be investigated systematically. VR-based training sessions using head-mounted displays alongside a real-time dynamic physiology system were held by student assistants for small groups followed by debriefing with a tutor. In the pilot study, 36 students rated simulation sickness. In the main study, 97 students completed a virtual scenario as active participants (AP) and 130 students as observers (OBS) from the first-person perspective on a monitor. Participants completed questionnaires for evaluation purposes and exploratory factor analysis was performed on the items. The extent of simulation sickness remained low to acceptable among participants of the pilot study. In the main study, students valued the realistic environment and guided practical exercise. AP perceived the degree of immersion as well as the estimated learning success to be greater than OBS and proved to be more motivated post training. With respect to AP, the factor “sense of control” revealed a typical inverse U-shaped relationship to the scales “didactic value” and “individual learning benefit”. Summing up, curricular implementation of highly immersive VR-based training of emergencies proved feasible and found a high degree of acceptance among medical students. This study also provides insights into how different conceptions of perceived stress distinctively moderate subjective learning success.

ORB-Livox: A real-time dynamic system for fruit detection and localization

Accurate fruit localization plays an essential role in many agricultural tasks and is one of the most vital components of an autonomous harvesting robot. Although fruit localization in stationary conditions has been extensively studied, the method to perform fruit localization in dynamic conditions is rarely studied. Fruit localization under dynamic conditions is much more challenging, since movement will introduce continuously irregular ego-motion on visual sensors, which can lead to considerable accuracy degeneration or even system failure. However, fruit localization under dynamic conditions is an essential task since it can significantly improve the harvesting efficiency. In this study, a fruit localization method is proposed to perform accurate fruit localization under dynamic conditions. Firstly, an ORB-SLAM3 SLAM algorithm is utilized to estimate the camera’s pose under dynamic conditions. Secondly, YOLO-V5 is used to identify apples in RGB images. Moreover, Lidar is used to measure the depth of each apple accurately. Finally, a clustering technique based on the Gaussian distribution is proposed to mitigate the accuracy loss under dynamic conditions to optimize apple positions. Our method is extensively evaluated in indoor and outdoor experiments. The results of indoor experiments show that the average localization error is 21.1 mm, with 90% of the errors are less than 30 mm. The experimental result in an orchard environment demonstrates that our proposed method can detect and localize apples under dynamic conditions in unstructured natural environments.

Real-time tracking and dynamic berthing information extraction system with 2D LiDAR data

In smart ports, accurately perceiving the state of a berthing vessel relative to the berth is essential for ensuring the safety and efficiency of vessels and ports. This paper presents a real-time tracking and dynamic berthing information extraction system based on two-dimensional light detection and ranging (LiDAR). First, data preprocessing, fast segmentation, and filtering are performed in polar coordinates based on the continuous measurements and high angular resolution of the LiDAR. Moreover, the ship clusters are merged owing to “blind spot”. Then, simple online and real-time tracking with ship features is proposed by modifying the simple online and real-time tracking with a deep association metric framework to remove invalid frames and adapt to different berthing scenarios. Qualitative and quantitative analyses of the dynamic distance and approach angle berthing parameters relative to the dock are performed. The frequency of the calculation results can reach a rate of 20 Hz for LiDAR. On-site monitoring experiments were conducted on the training ship YuKun berthing/unberthing in the Dalian Port and a simulated berthing scenario was also established to verify the algorithm. The proposed method proved feasible and effective for dynamic ship target tracking and ensuring safe ship berthing.

Real-time modular dynamic modeling for compression system of altitude ground test facilities

Modeling of a centrifugal compressor is of great significance to surge characteristics and fluid dynamics in the Altitude Ground Test Facilities (AGTF). Real-time Modular Dynamic System Greitzer (MDSG) modeling for dynamic response and simulation of the compression system is introduced. The centrifugal compressor, pipeline network, and valve are divided into pressure output type and mass flow output type for module modeling, and the two types of components alternate when the system is established. The pressure loss and thermodynamics of the system are considered. An air supply compression system of AGTF is modeled and simulated by the MDSG model. The simulation results of mass flow, pressure, and temperature are compared with the experimental results, and the error is less than 5%, which demonstrates the reliability, practicability, and universality of the MDSG model.

Research on a real-time dynamic monitoring method for silent aspiration after stroke based on semisupervised deep learning: A protocol study.

This study aims to establish a real-time dynamic monitoring system for silent aspiration (SA) to provide evidence for the early diagnosis of and precise intervention for SA after stroke. Multisource signals, including sound, nasal airflow, electromyographic, pressure and acceleration signals, will be obtained by multisource sensors during swallowing events. The extracted signals will be labeled according to videofluoroscopic swallowing studies (VFSSs) and input into a special dataset. Then, a real-time dynamic monitoring model for SA will be built and trained based on semisupervised deep learning. Model optimization will be performed based on the mapping relationship between multisource signals and insula-centered cerebral cortex-brainstem functional connectivity through resting-state functional magnetic resonance imaging. Finally, a real-time dynamic monitoring system for SA will be established, of which the sensitivity and specificity will be improved by clinical application. Multisource signals will be stably extracted by multisource sensors. Data from a total of 3200 swallows will be obtained from patients with SA, including 1200 labeled swallows from the nonaspiration category from VFSSs and 2000 unlabeled swallows. A significant difference in the multisource signals is expected to be found between the SA and nonaspiration groups. The features of labeled and pseudolabeled multisource signals will be extracted through semisupervised deep learning to establish a dynamic monitoring model for SA. Moreover, strong correlations are expected to be found between the Granger causality analysis (GCA) value (from the left middle frontal gyrus to the right anterior insula) and the laryngeal rise time (LRT). Finally, a dynamic monitoring system will be established based on the former model, by which SA can be identified precisely. The study will establish a real-time dynamic monitoring system for SA with high sensitivity, specificity, accuracy and F1 score.

Custom Soft-Core RISC Processor Validation Based on Real-Time Event Handling Scheduler FPGA Implementation

In dynamic real-time systems (RTS), the synchronous communication model is a source of unpredictable behaviors caused by the difficulty of estimating the maximum lockdown time in a process. Inter-task communication is a critical issue in RTS, even in the case of uniprocessor architectures. Using an FPGA-based development platform, through an SoC project, the implementation of HW_nMPRA_RTOS (a unified acronym for multi pipeline register architecture (nMPRA) where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> is the degree of datapath resource multiplication, hardware scheduler engine (nHSE) for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> threads and RTOS application programming interface (API)), dedicated processor architecture was developed, simulated and validated. This paper propose an innovative soft-core implementation to reduce interrupt latencies, while maintaining strong spatial and temporal isolation. Among the results which contain relevance and novelty, we can mention: rapid tasks context switching (1 clock cycle); The implementation of a distributed and versatile interrupt system which allows the interrupt attachment to any task; The implementation of a static scheduler and support for the dynamic tasks scheduling; Rapid response to events of up to 2 clock cycles. We demonstrate the architecture’s predictability, scalability, and performance by running a set of benchmark applications on several configurations of HW_nMPRA_RTOS synthesized on a Xilinx 7 Series FPGA.

Analysis on the Development of Subway in a Pandemic City

The outbreak of COVID-19 poses a severe challenge to the epidemic prevention and control of urban public transport system. As the backbone of urban public transport, urban rail transit is one of the important links in the epidemic prevention and control of urban public transport system. It faces the challenge of heavy tasks and great pressure. Using the methods of questionnaire survey, field investigation and case analysis, this paper aims to find out how to improve the operation of the subway under the epidemic situation, and finally comes to a conclusion that there is need to further strengthen the control measures to deal with peak passenger flow concentration, and improve the emergency plan. Besides, a traceable and reliable epidemic prevention cooperation system should be created and a real-time dynamic interaction system should be built to open up the information barrier between passengers and operating units. Moreover, it is also important to improve the normalized and refined monitoring and early warning system for high-risk areas, as well as simplifying the display of passengers' personal information and realize automation, intelligence, and integration.

Design and effects of the teacher-student interaction model in the online learning spaces

The interaction between teachers and students is vital for promoting teaching quality. Online learning spaces have various features that can support teacher-student interaction in online learning contexts. In this study, a preliminary model was developed by analyzing the principles underlying the interaction between teachers and students and the support features of online learning spaces. Then, the interaction model was refined and validated in three rounds of teaching practice involving 31 college students. A real-time dynamic artificial intelligence analysis system was used to analyze the teacher-student interaction during three rounds of design-based research. The results showed that the model significantly fostered students’ engagement during the interaction. Moreover, students significantly improved their final exam scores and their innovative problem-solving ability after the intervention.