Real-time State Research Articles

A Physical Model of Quantum Bit Behavior Based on a Programmable FPGA Integrated Circuit

The rapidly developing field of quantum computing and the ongoing lack of widely available quantum computers create the need for scientists to build their simulators. However, mathematical simulation of such circuits usually ignores many aspects and problems found in real quantum systems. In this article, the authors describe a quantum bit emulator based on FPGA integrated circuits. In this case, FPGA technology provides real-time massive parallelism of the modeled physical phenomena. The modeled QUBIT is represented using a Bloch sphere. Its quantum state is set and modified only by precise pulses of an electrical signal, and with the help of similar pulses, it manifests its current state in real time. The constructed QUBIT was additionally equipped with decoherence mechanisms and with circuits that intentionally respond to internal and external noises that distort its current quantum state. This article presents and discusses how such a physically built emulator works.

Intersection decision model based on state adversarial deep reinforcement learning

The decision-making model for autonomous vehicles based on deep reinforcement learning faces challenges in robustness in complex and dynamic scenarios, such as crossroads. This study proposes a state adversarial deep reinforcement learning decision model for autonomous driving to address this concern. The model incorporates an adversarial state space standard deviation to accommodate the significant differences in the decision model’s state space between the initial and final steps. Adversarial state space standard deviation, coupled with real-time state space, creates an adversarial state space set. The decision model obtains an adversarial strategy under the adversarial state space set and integrates it with the strategy of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. This simultaneous optimization of model parameters is designed to improve the robustness of the decision model for autonomous driving. Experimental validation was performed using the Carla simulation environment, involving the creation of four scenarios with varying traffic flow densities and collision rates. In these scenarios, the enhanced model achieved success rates ranging from 89.9% to 98.6%, with the average vehicle travel time staying below 10 s. By contrast, the TD3 and Soft Actor-Critic (SAC) models exhibited success rates between 78.4% and 93.4%, with average vehicle travel times ranging from 10.03 to 20.01 s. When tested across various scenarios, the improved model demonstrated greater robustness compared to the TD3 and SAC models.

Prototype-assisted multiscale graph representation learning-based mechanical fault detection method under complex operating conditions

Effective anomaly detection and timely fault warning are essential to ensure the continuous and safe operation of mechanical equipment and to prevent equipment deterioration. In the unsupervised modeling and detection scenario, fault detection methods based on the autoencoder framework have been widely concerned and applied. Unfortunately, such methods can only be applied to specific or constant operating conditions, and their detection performance is greatly reduced due to the different data distribution in the face of complex operating conditions. Aiming at the problem of unsupervised fault detection under complex operating conditions, this article proposes a prototype-assisted multiscale graph representation learning-based mechanical fault detection method. First, the vibration data of the equipment is fed into the multiscale decomposition module (MDM) to obtain multiscale feature maps that can express rich detail information. Then, the multiscale feature maps are fed into the graph representation learning module (GRLM) to fully learn the potential relationships and interactions between different scales and provide a more comprehensive representation of the dynamic characteristics of the equipment. Finally, multiple MDMs and GRLMs are cascaded to construct a feature extractor to map the data of each operating condition to the latent space, and the proposed prototype-assisted strategy is used to determine the real-time state of the equipment. Case studies have been carried out on two different pieces of mechanical equipment. The experimental results show that the average accuracy of the proposed method is as high as 98.44% and 98.90%, respectively, and it maintains a low missed detection rate and zero false alarm rate in the two validation processes, which is more in line with the needs of engineering applications than other comparison methods.

Research on deterioration state evaluation of hydropower units based on successive variational mode decomposition and Mahalanobis distance

Abstract In order to better acquire the real-time operating status of hydropower units and realize early fault warning, a deterioration state evaluation method for hydropower units based on Successive Variational Mode Decomposition (SVMD) and Mahalanobis Distance (MD) is proposed. In the offline stage, SVMD is optimized with Dispersion Entropy (DE) as the fitness function, and historical health data is used to obtain a health baseline. In the online stage, the real-time monitoring signal is input into the optimized SVMD model first. The features of the Intrinsic Mode Functions (IMFs) are then extracted. Subsequently, Synthetic Detection Index (SDI) and Detection Index (DI) are utilized for feature parameter selection. Finally, a degradation indicator is constructed based on Gaussian mixture model (GMM) and MD, and the degradation curve is drown to evaluate the real-time deterioration state of the unit. Experimental results demonstrate that the proposed method can effectively characterize the real-time operating status of units, identify abnormal changes, and issue timely warnings.

Gaussian–Student’s t Mixture Distribution-Based Robust Kalman Filter for Global Navigation Satellite System/Inertial Navigation System/Odometer Data Fusion

Multi-source heterogeneous information fusion based on the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/odometer is an important technical means to solve the problem of navigation and positioning in complex environments. The measurement noise of the GNSS/INS/odometer integrated navigation system is complex and non-stationary; it approximates a Gaussian distribution in an open-sky environment, and it has heavy-tailed properties in the GNSS challenging environment. This work models the measurement noise and one-step prediction as the Gaussian and Student’s t mixture distribution to adjust to different scenarios. The mixture distribution is formulated as the hierarchical Gaussian form by introducing Bernoulli random variables, and the corresponding hierarchical Gaussian state-space model is constructed. Then, the mixing probability of Gaussian and Student’s t distributions could adjust adaptively according to the real-time kinematic solution state. Based on the novel distribution, a robust variational Bayesian Kalman filter is proposed. Finally, two vehicle test cases conducted in GNSS-friendly and challenging environments demonstrate that the proposed robust Kalman filter with the Gaussian–Student’s t mixture distribution can better model heavy-tailed non-Gaussian noise. In challenging environments, the proposed algorithm has position root mean square (RMS) errors of 0.80 m, 0.62 m, and 0.65 m in the north, east, and down directions, respectively. With the assistance of inertial sensors, the positioning gap caused by GNSS outages has been compensated. During seven periods of 60 s simulated GNSS data outages, the RMS position errors in the north, east, and down directions were 0.75 m, 0.30 m, and 0.20 m, respectively.

Students' Ethical, Privacy, Design, and Cultural Perspectives on Visualizing Cognitive-Affective States in Online Learning

While teachers often monitor and adjust their learning design based on students' emotional states in physical classrooms, synchronous online environments often limit their ability to perceive the emotional climate of the class. Drawing from the concept of \textit{social translucence}, it is suggested that making students' emotional states ``visible" in online settings can help foster empathetic interactions. Recent advancements in emotion recognition technology are enabling the creation of learning analytics (LA) systems that can estimate students' cognitive-affective states in real time. Yet, the adoption of these systems raises ethical, cultural, and practical concerns when implemented in learning environments, including potential challenges related to accuracy, privacy, and data integrity. To address these concerns, we conducted an in-depth qualitative study exploring the perspectives of 12 undergraduate students on modelling and visualizing their cognitive-affective states in the context of a Mexican higher-education institution. The study provides insights within a particular cultural context, which can guide the design of more human-centred emotion recognition--based LA tools for online educational contexts or contribute to informed decisions about the necessity of modelling and visualizing cognitive states.

The legal status of an individual as an entrepreneur in Ukraine

The article examines new theoretical and practical approaches to understanding the concept and legal status of an individual as an entrepreneur in Ukraine’s civil law. Significant attention is paid to the issues of online registration of individual entrepreneurs (IE) in automatic mode. The current Law of Ukraine «On State Registration of Legal Entities, Individual Entrepreneurs, and Public Formations» provides new modern possibilities for real-time state registration of IE without the involvement of a state registrar, minimizing the time required for the process. Based on the analysis of Article 25-1 of the Law of Ukraine «On State Registration of Legal Entities, Individual Entrepreneurs, and Public Formations,» it is concluded that today the state registration of IE can be carried out in real time without the participation of a state registrar using the Unified State Web Portal of Electronic Services and the software of the Unified State Register. First, it is verified that there is no information about the individual entrepreneur in the Unified State Register. If such information is absent, the software of the Unified State Register ensures the state registration. For state registration, the individual uses electronic identification tools with a high level of trust. It is argued that an individual who has exercised their constitutional right to engage in entrepreneurial activity, after undergoing the appropriate registration and other procedures provided by law, does not lose or change their status as an individual, which they acquired from birth. Instead, they acquire a new characteristic - that of an entrepreneur. The legal status of an individual entrepreneur (IE) itself does not affect any legal capacity of the individual, which is determined by their civil legal capacity, nor does it limit it. It is proven that from the moment of state registration as an IE, the individual effectively holds two legal statuses: as an individual and as an IE. The presence of entrepreneurial status does not imply that the individual acts as an entrepreneur in all legal relations or that all subsequent legal relations involving this person are entrepreneurial in nature, as the individual continues to act as a participant in civil relations, for example, entering into transactions to meet their personal needs.

Research on mood monitoring and intervention for anxiety disorder patients based on deep learning wearable devices

Background Anxiety disorders are common mental health issues that have a significant effect on people's quality of life. Conventional techniques for tracking emotional states frequently lack the accuracy and sensitivity needed for successful intervention. Objectives This project aims to create a sophisticated monitoring system that uses deep learning methods to evaluate physiological data from wearables, emphasizing heart rate variability (HRV), to forecast patients’ emotional states who suffer from anxiety disorders. Methods Wearable equipment monitors physiological characteristics, which we used to obtain patient HRV data. We processed the data using a Bidirectional Long-Short-Term Memory (Bi-LSTM) network to evaluate time-dependent variables and enhance the precision of emotional state predictions. The physiological signals were used to teach the model to recognize different emotional states, such as neutral, happy, and sad. Results Outperforming conventional machine learning models, the Bi-LSTM model showed a high accuracy rate of up to 97% in predicting emotional states. The findings suggest that ongoing HRV monitoring can accurately track shifts in emotional states and enable prompt responses. Conclusion This work emphasizes the possibility of real-time emotional state monitoring in patients with anxiety disorders with wearable technology and deep learning. The results point to the potential benefits of this strategy for improving emotional regulation and improving anxiety sufferers’ quality of life, opening new avenues for investigation and advancement in the field of mental health therapies.

SARM: A network State-Aware Adaptive Routing Mutation method for power IoT

With the rapid development of Power Internet of Things (PIoT), the application of Internet of Things technology in smart grids is becoming increasingly widespread, but it also enlarges the attack surface of the system. Against this backdrop, traditional defense methods are limited by the asymmetry of network attack and defense, which makes it difficult to effectively resist the ievolving sniffing attacks and link flooding attacks. In order to improve the security of PIoT, this paper proposes an Adaptive Route Mutation based on Network State Awareness (SARM). It generates a route mutation space using a path state matrix and optimizes the time complexity of mutation space generation with a backtracking method. Furthermore, the SARM can dynamically adjust the route mutation strategy according to the real-time network state to realize self-adaptive defense. In conclusion, SARM is evaluated through simulations conducted with Mininet. Compared to Random Routing Mutation (RRM), it enhances defense against Sniffing and Distributed Denial of Service attacks by approximately 30% and 35% respectively. Additionally, in various example topologies, SARM consistently outperforms RRM.

Dynamic docking algorithm for UGV to UAV based on single planning under disturbed conditions.

This paper investigates the initial dynamic docking problem to mobile and trajectory-disturbed targets for tracking and recovering drones by Unmanned Ground Vehicles (UGVs). First, the target status is estimated by employing the Extended Kalman Filter (EKF). Then, the drone's perturbation is mapped to a dynamic docking point, quantifying the target motion deviation. Within this point frame, an initial path planning algorithm based on Bezier curve with polar offset points is designed. By integrating the initial path and real-time target states into the control layer, a docking control algorithm within single planning is developed. This algorithm enables the UGV to dynamically dock to a trajectory-disturbed drone in position and angle, while also achieving target tracking after simplification. Finally, simulations and experiments have been conducted to demonstrate its effectiveness.

Real-Time Driver State Detection Based on OpenCV and MediaPipe Libraries

Empowering street security requires being able to distinguish occupied driving. A real- time way to keep an eye on a driver's condition is to utilize driver-state diversion. We will screen varieties within the driver's eye and look developments by utilizing the eye-aspect proportion (EAR) and the look score. A dashboard-mounted camera gives us with video, which we record and prepare utilizing the OpenCV and Mediapipe modules. Deciding the driver's sign of languor is made simpler by the EAR. Discover out in the event that the drivers are occupied utilizing the look score. As a implies of instantly distinguishing and settling such issues, the framework persistently checks the driver's confront. With broad testing, we appear that our approach can effectively caution drivers, lessening the chance of collisions and progressing everyone's security when driving

In-situ monitoring of clamp-repair-induced displacements in the submarine pipeline using a MEMS accelerometer array: A sea trial

The positioning and displacement monitoring of submarine pipelines is crucial for delivering operational feedback and early warning during its external structural repair processes, particularly in turbid sea areas reliant on diver operations. However, conventional numerical simulation and engineering geophysical techniques are unable to obtain real-time state data amidst repairs. In this study, we develop a monitoring system based on Micro-Electro-Mechanical Systems accelerometer arrays (MEMS-AA) to capture positional and displacement changes in the pipeline. By employing a positioning ring to connect the MEMS-AA with the pipeline, the spatial attitude changes in the MEMS-AA can accurately reflect the displacement of the pipeline. Moreover, a novel method is introduced to reconstruct the spatial attitude of the MEMS-AA, which solves the rotation matrix by the three-axis components of the accelerometer in the gravity field utilizing the greedy search algorithm. Through simulations, we validated the accuracy and effectiveness of the proposed method under the influence of varying ocean noises. The sea trials conducted in Hangzhou Bay demonstrate that our monitoring system can successfully capture real-time pipeline displacement associated with repair operations, providing valuable guidance for engineering operations to ensure the structural integrity of the pipeline.

A Novel Real-time Phase Prediction Network in EEG Rhythm.

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.

VNN-DM: a vector neural network-based detection model for time synchronization attacks in park-level energy internet

Micro phasor measurement units (µPMUs) provide high-precision voltage and current phasor data, allowing real-time state estimation and fault detection, which are critical for the stability and reliability of modern power systems. However, their reliance on accurate time synchronization makes them vulnerable to time synchronization attacks (TSAs), which can disrupt grid monitoring and control by corrupting µPMU data. Addressing these vulnerabilities is essential to ensure the secure and resilient operation of smart grids and energy internet technologies. To address these challenges, intelligent detection methods are essential. Therefore, this paper proposes a µPMU measurement data TSA detection model based on vector neural networks (VNNs). This model initially employs a vector neural network to process raw data, effectively extracting and analyzing temporal features. During the same time, a capsule network is employed to classify these temporal features. On this basis, a reconstruction network is used to verify the representational capacity of the model. Simulations based on µPMU measurement data demonstrate that the model exhibits excellent detection capacity in various performance metrics, underscoring its precision and robustness.

Prospects and Challenges of Affective AI Technology in Supporting Remote Learning

This study analyzes the potential and challenges of introducing affective AI technologies to promote remote education. The growing popularity of remote learning highlights the significant upside of affective AI in real-time emotional monitoring and feedback, especially for improving learning motivation, optimizing personalized educational paths, and supporting students' mental health. This study explores the particular applications of affective AI technologies and the ethical and privacy concerns they present. The research indicates that affective AI technology, based on the principles of affective computing, may strengthen students' emotional management abilities, improve the overall educational experience by identifying their emotional states in real time, and create more possibilities for teacher-student interactions. Affective AI enhances student motivation and optimizes personalized learning paths in remote education, especially for fostering positive emotional regulation and mental health support outcomes. However, affective AI encounters challenge related to technical prejudice, privacy protection, ethical fairness, and user acceptance in extensive applications, particularly notable disparities in multicultural contexts, which require solutions through privacy safeguards, algorithm refinement, and culturally adaptive design. This study is significant as it systematically reveals the potential of affective AI technologies in remote learning, identifies their critical challenges, and offers valuable recommendations and references for future advancement in global education.

Real-Time Temperature Field State Observer and Digital Replica to Support Cyber-Physical Testing

Real-Time Temperature Field State Observer and Digital Replica to Support Cyber-Physical Testing

A Study of Potential Applications of Student Emotion Recognition in Primary and Secondary Classrooms

Emotion recognition is critical to understanding students’ emotional states. However, problems such as crowded classroom environments, changing light, and occlusion often affect the accuracy of recognition. This study proposes an emotion recognition algorithm specifically for classroom environments. Firstly, the study adds the self-made MCC module and the Wise-IoU loss function to make object detection in the YOLOv8 model more accurate and efficient. Compared with the native YOL0v8x, it reduces the parameters by 16% and accelerates the inference speed by 20%. Secondly, in order to address the intricacies of the classroom setting and the specific requirements of the emotion recognition task, a multi-channel emotion recognition network (MultiEmoNet) has been developed. This network fuses skeletal, environmental, and facial information, and introduces a central loss function and an attention module AAM to enhance the feature extraction capability. The experimental results show that MultiEmoNet achieves a classification accuracy of 91.4% on a homemade classroom student emotion dataset, which is a 10% improvement over the single-channel classification algorithm. In addition, this study also demonstrates the dynamic changes in students’ emotions in the classroom through visual analysis, which helps teachers grasp students’ emotional states in real time. This paper validates the potential of multi-channel information-fusion deep learning techniques for classroom teaching analysis and provides new ideas and tools for future improvements to emotion recognition techniques.

Real-time monitoring of functional data

With the development of data acquisition technologies, huge amounts of data, which are apt to be modeled as functional data, are now generated. In this setting, standard profile monitoring methods aim to assess the stability over time of a completely observed functional quality characteristic. However, in some practical situations, assessing the presence of assignable causes is of great interest even when the functional quality characteristic is not completely observed yet, that is, to monitor the process state in realtime. To this aim, we propose a new method, referred to as functional real-time monitoring (FRTM), that is able to account for both phase and amplitude variation through the following steps: (i) registration, (ii) dimensionality reduction, and (iii) monitoring of a partially observed functional quality characteristic. An extensive Monte Carlo simulation study quantifies the performance of FRTM relative to three competing methods. Finally, a case study addresses the real-time monitoring of household daily electricity demand FRTM is implemented in the R package funcharts, available CRAN.

Pri-DDQN: learning adaptive traffic signal control strategy through a hybrid agent

Adaptive traffic signal control is the core of the intelligent transportation system (ITS), which can effectively reduce the pressure on traffic congestion and improve travel efficiency. Methods based on deep Q-leaning network (DQN) have become the mainstream to solve single-intersection traffic signal control. However, most of them neglect the important difference of samples and the dependence of traffic states, and cannot quickly respond to randomly changing traffic flows. In this paper, we propose a new single-intersection traffic signal control method (Pri-DDQN) based on reinforcement learning and model the traffic environment as a reinforcement learning environment, and the agent chooses the best action to schedule the traffic flow at the intersection based on the real-time traffic states. With the goal of minimizing the waiting time and queue length at intersections, we use double DQN to train the agent, incorporate traffic state and reward into the loss function, and update the target network parameters asynchronously, to improve the agent’s learning ability. We try to use the power function to dynamically change the exploration rate to accelerate convergence. In addition, we introduce a priority-based dynamic experience replay mechanism to increase the sampling rate of important samples. The results show that Pri-DDQN achieves better performance, compared to the best baseline, it reduces the average queue length is reduced by 13.41%, and the average waiting time by 32.33% at the intersection.

Integration of Sense and Control for Uncertain Systems Based on Delayed Feedback Active Inference.

Asa result of the time lag in transmission, the data obtained by the sensor is delayed and does not reflect the state at the current moment. The effects of input delay are often overlooked in active inference (AIF), which may lead to significant deviations in state estimation and increased prediction errors, particularly when the system is subjected to a sudden external stimulus. In this paper, a theoretical framework of delayed feedback active inference (DAIF) is proposed to enhance the applicability of AIF to real systems. The probability model of DAIF is defined by incorporating a control distribution into that of AIF. The free energy of DAIF is defined as the sum of the quadratic state, sense, and control prediction error. A predicted state derived from previous states is defined and introduced as the expectation of the prior distribution of the real-time state. A proportional-integral (PI)-like control based on the predicted state is taken to be the expectation of DAIF preference control, whose gain coefficient is inversely proportional to the measurement accuracy variance. To adaptively compensate for external disturbances, a second-order inverse variance accuracy replaces the fixed sensory accuracy of preference control. The simulation results of the trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV) show that DAIF performs better than AIF in state estimation and disturbance resistance.