Cognitive state detection holds significant research value in the field of human–computer interaction and neural engineering. However, existing works are insufficient in modeling the temporal dynamics of multimodal physiological signals, which leads to heterogeneous distribution differences in cross-modal feature interactions. In addition, domain shift issues under cross-subject and few-sample conditions restrict the model generalization performance. To cope with these problems, this work proposes a cognitive state detection framework that integrates Transformer-based multimodal feature interaction and self-training of pseudolabel optimization. First, the multihead attention mechanism is introduced to model the temporal evolution patterns across modalities, dynamically harmonizing cross-modal contributions to extract cognitive state-related shared features. Then, a dual-model cross-validation strategy is designed to filter high-quality pseudolabeled samples from the target domain for subsequent self-training, effectively avoiding the dependency on auxiliary modules in domain adaptation. Finally, Extensive experiments show that the proposed work significantly improves the recognition accuracy, and the designed pseudolabel optimization mechanism can be transferred to related tasks without increasing model complexity.

Automated analysis of aviation safety reports is helpful in effectively preventing future accidents and improving emergency response capabilities. To date, there are no publicly available large-scale aviation text similarity datasets, which hinders the successful application of NLP techniques in the aviation domain. We present an automatically created aviation text similarity dataset consisting of more than 500,000 pairs for fine-tuning pretrained language models. Since technical terms have specialized meanings that differ from everyday language, we propose an efficient semantic encoding algorithm to improve the ability of embeddings to adequately represent aviation terms. We provide new solutions and revised evaluation metrics for the classification and the retrieval of safety reports, confirming the reliability of our dataset and the superiority of our algorithm.Note to Practitioners—Text representation is an essential task in natural language processing(NLP). A crucial step towards the successful application of NLP in safety reports analysis is to ensure that aviation texts are adequately encoded. Aiming at the problem of poor ability of current embeddings to represent technical terms, we automatically create an aviation text similarity dataset and propose a semantic encoding algorithm for aviation terms. It is clear that the proposed method has great potential in representation of technical terms, thus providing assistance for downstream tasks such as text classification, information retrieval and question answering.

This paper focuses on the issue of limited information based emergency response control for a specific category of underwater cyber-physical systems (UCPSs) confronted with multiple emergencies. The focus is on a typical underwater vehicle system (UVS), whose communication with the control center may be compromised by Denial-of-Service (DoS) attacks. The occurrence of dual DoS attacks leads to significant information loss, intensifying the complexity of decision-making and emergency response during critical situations. Moreover, abrupt ocean current disturbances or faults will also seriously affect the performance of UVSs. Drawing upon distinct DoS attack scenarios, this study introduces a novel emergency response decision mechanism. A limited information-based disturbance observer (DO) is then proposed to effectively handle various unknown disturbances, ensuring favorable disturbance estimation performance. Subsequently, as for different attack channels, two innovative emergency response controllers are respectively proposed, considering the constraints of limited information. Furthermore, distinct stability criteria are derived by using Lyapunov stability and stochastic analysis techniques to guarantee the stabilization of UVSs. Finally, a series of numerical results is presented to illstrate the efficiency of the proposed algorithm under various emergency scenarios.Note to Practitioners—This study presents an novel framework for emergency automatic control and decision-making in UVSs suffering from different emergencies. While most of previous research primarily concentrated on emergency situations in the physical layer, this article delves into both the information layer and the physical layer to tackle decision-making and response challenges within information-constrained environments. Specifically, a limited information-based DO is designed to dynamically model disturbances, such as ocean currents and actuator faults. The DO adapts its structure based on valuable information obtained from emergency monitoring, ensuring accurate disturbance estimation upon successful transmission. The proposed strategy holds significant practical applicability in UVSs for handling emergencies, and further experimental validations are planned to be conducted on actual underwater vehicles.

Video captioning can understand videos, provide decision-makers with user-friendly natural language narration, alleviate the gap between man and machine, and promote human-machine interaction. Therefore, it has good application prospects in emergency response scenarios, such as aerial refueling and assisted driving. However, there are two problems with the current video captioning methods: 1) they are mainly oriented to general domains, and there are few studies on industrial applications; 2) the methods only interact with the semantics of video and text from a single view (tokens or sentences). For the above problems, this paper proposes a multi-view end-to-end video caption (MVVC) method for human-machine fusion. Compared with the previous video captioning methods, 1) the MVVC model is an end-to-end model which directly takes video frames as input without object detection for each frame; 2) we perform cross-modal interaction of video and text from both local and global views. So the model can simultaneously understand video content and generate text at two granularities(tokens to sentences). In order to verify the performance of the new model, we conducted a series of comparative and ablation experiments on MVVC on the two data sets of aerial refueling and automatic driving. The experiments show that our new method has a stronger video understanding ability and can generate more accurate video descriptions. At the same time, it also verified that the video captioning task could promote human-machine fusion and assist decision-making in emergency scenarios.Note to Practitioners—The motivation of this paper is to convert the video into natural language so that the autonomous system can automatically understand the observed scene, describe it to relevant stakeholders, and promote human-machine fusion. However, the traditional method needs to process the video offline and has an insufficient understanding of the video content. Therefore, this paper proposes an end-to-end video capture method based on multi-view semantic alignment, which can understand the video content directly from the original video pixels in real time and improve captioning accuracy. It can meet the application requirements of the industrial field and has practical application value.

Controlling the cyborg rat with the guidance of an unmanned aerial vehicle (UAV) is important yet challenging. UAV can provide a mobile bird’s eye view (BEV) with multiple shooting angles to expand the motion space of the cyborg rat. However, uncertainty is a critical problem arising from the obscure and blurry imaging from UAV and the locomotion willingness of cyborg rats. To solve this problem, we propose the collaborative Rat-UAV navigation (RUN) paradigm. The uncertainty in RUN is formulated using the partially observed Markov decision processes (POMDP) in a perceptual-control framework. First, perceptual uncertainties in rat pose estimation and environmental modeling are reduced. Second, control policies are delivered to maximize rewards and minimize control uncertainties until the cyborg rat reaches the target. To achieve this, RUN can accurately detect tiny rats with fewer parameters, dynamically plan paths with partial observations from the UAV, and stably control the rat using a locomotion willingness transition graph in a large practical arena. Our framework is tested in real-world experiments. For the first time, a cyborg rat successfully completed a navigation task with the guidance of a UAV.Note to Practitioners—This paper studies, for the first time, the problem of navigating a cyborg rat with the guidance of an unmanned aerial vehicle (UAV). Most existing researches on the autonomous navigation of cyborg rats are conducted in fully observed environments with fixed cameras, disregarding uncertainties in complex environmental perception and rat control. In this paper, we propose a collaborative Rat-UAV navigation (RUN) paradigm, which formulates uncertainties using the partially observed Markov decision processes (POMDP). Specifically, RUN enhances the cyborg rat’s interaction with the UAV in the navigation environment. First, it narrows the gap between the rat’s perceived and actual states by utilizing keypoint detection, dynamic environment modeling, and locomotion willingness estimation from the view of the UAV. Secondly, RUN constructs a policy generation module to maximize the reward of controlling the cyborg rat, based on a locomotion willingness transition graph. Experimental results demonstrate that our framework effectively guides the cyborg rat through a large maze, followed by six targets, with the assistance of the UAV. In future researches, we will investigate the real-time prediction of rat locomotion willingness using multimodal neuroethological recordings, towards a bidirectional brain computer interface between rats and UAVs.

Emergency-related knowledge from urban rail transit (URT) safety event case base is crucial for realizing intelligent emergency response in URT. However, safety event case records are non-standard and unstructured, leading to challenges in data mining, information induction, and knowledge organization. To obtain emergency-related knowledge effectively from URT safety event cases, this paper proposes a knowledge acquisition model incorporating label semantics and provides a knowledge acquisition degree evaluation method. In this study, the knowledge acquisition task is formulated as a machine reading comprehension (MRC) task. This formulation can take full advantage of the rich semantic information of labels, which can compensate for the drawbacks of the traditional sequence labeling model. The experiments conducted on the URT safety event case base demonstrate the effectiveness of the proposed methods. The knowledge acquisition model performs well on different labels and achieves a performance boost over baseline models.Note to Practitioners—Urban rail transit (URT) safety events are caused by various factors, such as natural disasters like earthquakes and floods, equipment failures, and terrorist attacks. et al. These events may disrupt the normal operation of URT, pose a great risk to personal safety, and result in property losses. URT safety event cases encompass essential emergency-related experience and knowledge, which serve as valuable references for managing new emergencies and mitigating potential losses. However, the record format of URT safety event cases is non-standard, with the majority stored as fragmented textual data, posing challenges in effectively extracting and utilizing the valuable knowledge they contain. Considering the data characteristics of URT safety event cases, three different methods are developed for knowledge acquisition. Among these methods, a knowledge acquisition model is proposed to extract emergency-related knowledge from URT safety event cases efficiently and accurately.

Emergencies often lead to multi-modal and unbalanced distribution of urban rail transit passenger flows. Aiming at this problem, a emergency control method of multi-modal passenger flow is proposed under the premise of comprehensively considering the operation cost and passenger travel efficiency of urban rail transit(URTS).The main work includes: 1) From the perspective of train scheduling, designing and developing a contingency strategy for multi-modal passenger flow by combining the train operation plan based on full-length and short-turn routing, station passenger flow restrictions, and dynamic train departure intervals; 2) From the perspective of train operation and passenger travel synergy, the emergency control model of passenger flow is established with the objective of minimizing the total train operation time and average passenger waiting time; 3) A multi-agent deep reinforcement learning(MDRL) method with a new action selection, reward and double loop mechanism-ARDQMIX is proposed to realize emergency autonomous perception and control of passenger flow in urban rail transit. The simulation results show that the emergency control method of multi-modal proposed in this study has a good utility for improving passenger travel efficiency and reducing operation cost.Note to Practitioners—Urban rail transit system(URTS) is punctual, fast, safe and large capacity, and has gradually become the preferred means of transportation for passengers. However, due to the rapid growth of travel demand, the URTS in the morning and evening peak hours traffic problems are particularly serious, will produce a multi-modal passenger travel demand, such as can not be quickly alleviated, the traffic problem will be caught in a vicious circle, leading to the urban rail transit system service capacity decline, and even lead to the potential safety problems. Aiming at this problem, a emergency control method of multi-modal passenger flow is proposed under the premise of comprehensively considering the operation cost and passenger travel efficiency of URTS.

Urban Rail Transit (URT) system is often disturbed by various types of security incidents during operation. Therefore, effective utilization of passenger in-transit information to dynamically assess the URT system’s abilities to maintain and resume normal operation under disturbances (operational resilience) is the key and prerequisite for URT safety management and emergency response. Based on typical operational disruption scenario, this paper study the multimodal data-driven URT operational resilience and its evaluation methods. The main contributions of this research includes: capturing key information about passengers in the travel process based on historical data, and quantifying the system’s service capabilities; proposing the concept of operational resilience oriented towards URT emergency management based on service level; employing the resilience curve to analyze the evolutionary patterns of URT service levels during safety events, and emphasizing the relativity and dynamism of operational resilience; constructing a two-stage “disruption-recovery” URT operational resilience evaluation model and taking Hangzhou URT as an example to verify the effectiveness of the model through experiments. The results provide a good reference for URT safety status assessment and emergency response.Note to Practitioners—With the expansion of urban rail transit scale, the passenger flow it undertakes is also increasing. Frequent security incidents can seriously disrupt the operation of urban rail transit, causing significant economic losses and even threatening the safety of passengers. If the ability of urban rail transit to resist safety incidents and restore normal operation can be understood in advance, it will help operation managers make reasonable judgments and emergency decisions. Therefore, this paper proposed an evaluation method for operational resilience with the service level of urban rail transit as the key.

In future battlefields, missiles are expected to become highly precise and efficient strike weapons, with missile intelligence emerging as a critical development trend. To address the problem of optimizing 3-D missile interception guidance laws, this article introduces the deep Q-network (DQN) algorithm on the foundation of proportional navigation guidance (PNG) and proposes an adaptive proportional guidance algorithm based on deep reinforcement learning (DRL). The proposed algorithm uses air combat situational information as the state space and incorporates parameters such as the missile-target relative distance and line-of-sight (LOS) angle into the reward function design. The optimal proportional navigation coefficient$K^{*}$for low-overload maneuvering targets is determined through network search, and the longitudinal and lateral control commands of the missile are decoupled by designing the proportional coefficient increment$\Delta K$, constructing a discretized action space. Simulation results show that, compared to the PNG with a constant$K^{*}$, the proposed method significantly improves the hit probability of high-overload maneuvering targets while maintaining the hit rate for low-overload maneuvering targets. As an exploration of future intelligent combat scenarios, this guidance law design method holds both theoretical significance and practical application value.

The highly variable nature of cellular networks challenges end-to-end network transmissions in achieving low-latency and high-throughput performance. In high-speed rail (HSR) networks, the intermittent connectivity and capacity dynamics imposed by high client mobility further add complexity and difficulty in providing seamless service. While congestion control algorithms (CCAs) play an essential role in ensuring optimal network performance, prior works on congestion control have predominantly concentrated on enhancing network performance within stationary or low-mobility mobile networks without considering frequent disconnections and highly dynamic network capacities imposed by HSR networks, resulting in severe RTT inflation and slow loss recovery. In this paper, we argue that a dedicated transport layer protocol is necessary for high-mobility scenarios. We propose an end-to-end low-latency congestion control algorithm HiMo for HSR networks that reacts to abrupt bandwidth changes quickly, handles frequent handovers, and is immediately deployable. Our trace-driven emulation on real-world datasets demonstrates that HiMo can reduce 51.3% 95th-percentile latency with comparable throughput on high-speed rail networks, compared to state-of-the-art CCAs.

Time granularity is a key parameter necessary for short-time passenger flow prediction of urban rail transit (URT); however, no universal method is available for its setting. This study investigates the time granularity setting principle for short-term passenger flow prediction in URT. First, a method to measure the autocorrelation of passenger flow time series is constructed, focusing on the comparison of time granularities. Second, based on the functional relationship between the first-order autocorrelation coefficients of the passenger flow time series under different time granularities, the time granularity setup principle is obtained for different passenger flow characteristics. Finally, the reasonableness and universality of the time granularity setting principle are verified by analyzing the passenger flow characteristics and autocorrelation magnitude of the actual inbound and origin-destination (OD) passenger flow data under different stations and dates at different time granularities.

The similarity measurement method is the key part of the normal cloud model as well as its applications. Too much attention has been paid on geometric and numerical features, leading to the weak interpretability and unreasonable results in the similarity measurement method of the normal cloud models. A bidirectional and weighted similarity measurement (BWSM) method is proposed by the number distribution and membership of cloud droplets on a normal cloud model. First, the cloud droplets are analyzed, and their characteristics of number distribution and membership that belong to different cloud models are obtained. Second, the similarity measurement strategy of the normal cloud models is analyzed and used to propose a BWSM method, which is compared with two commonly used similarity measurement methods of normal cloud models. The influence of expectation, entropy, and hyper entropy on the similarity measurement method of the normal cloud models is analyzed. Finally, the method is applied on the operational status perception and computing of urban rail transit. Results show the method has high rationality, validity, and applicability for perceiving and computing the operational status of urban rail transit. The method improves the interpretability of the similarity measurement of the normal cloud models and the credibility of the research based on the similarity measurement of the normal cloud models.

The emergency plan of the urban rail transit (URT) system is a guiding document for dealing with emergencies and formulating emergency plans. However, the emergency plan text described in natural language has some problems, such as poor visibility and enforceability. Effective help is difficult to provide for the rapid implementation of emergency response. Therefore, obtaining key emergency task information from the emergency response plan and visualizing the emergency disposal workflow are the main challenges. In this article, we propose a method of extracting emergency elements (EELs) from emergency plans and constructing a business process model. First, a nested entity extraction model incorporating adversarial training is proposed to extract EELs from the complex sentences in the emergency plan text. Second, the EELs are combined into emergency task units, and then the relations between emergency task units are identified to form the emergency task sequence flow, which is stored in matrix form. Finally, the emergency disposal workflow model is generated based on the emergency task sequence flow and the BPMN modeling method. Taking the actual emergency plan text as an example, the process from the extraction of EELs to the construction of the disposal workflow model is demonstrated. Experimental results prove that this method has advantages in comprehensively extracting EELs, visualizing the emergency disposal workflow, and improving the enforceability of emergency plans.

Given the precalibrated image pairs, stereo matching aims to infer the scene depth information in real-time, which has important research value in the fields of high-precision 3-D reconstruction of the Earth’s surface, automatic driving and unmanned aerial vehicle (UAV) navigation. The cost volume-based stereo matching method adopts a coarse-to-fine manner to construct cascaded cost volume, and applies 3-D convolution to capture the correspondence of feature matching to infer the disparity map, which achieves comparable performance. However, the existing method has difficulty dealing with jitter regions with disparity change, and direct disparity regression easily leads to overfitting of cost volume regularization. To alleviate the above two problems, this work proposes an end-to-end disparity estimation network based on Transformer. Its specific improvements are as follows. 1) The cross-view feature interaction module based on Transformer is introduced to realize the feature interaction of global context information. 2) A parallax attention mechanism is designed to impose global geometric constraints on the epipolar line to improve the reliability of feature matching. 3) Focal loss is applied for the training of the disparity classification model to emphasize one-hot supervision in ambiguous regions. Comprehensive experiments on public datasets Sceneflow, KITTI2015, ETH3D, and aerial WHU datasets validate that the proposed work can effectively enhance the performance of disparity estimation.

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows.

Emergency events such as fire, flood and COVID-19 occurred in urban rail transit (URT) usually triggered chain effect and evolved into huge disaster. This kind of chain with complexity and uncertainty evolution brought great challenges to the safety management of the system. Thus, the coupling effects of emergencies and then its relationship with the chain evolution is necessary to analyze emphatically. A Graph Evaluation and Review Technique Simulation (GERTS) evolution network is firstly constructed to describe the coupling effect and chain evolution of emergencies. Then, considering the internal and external influencing factors of the emergency chain, a dynamic evolution model of the emergency chain based on Coupled Map Lattice (CML) is proposed. This paper takes fire chain of URT as an example to simulate the evolution process of emergency chain, and analyze the impact of different coupling effects and various influencing factors on the evolution of emergency chain. The results of numerical simulation show that the AND-coupling can significantly inhibit the evolution of emergency events, while the OR-coupling and CO-coupling can expand the impact scope of emergency events. In addition, the evolution speed of emergency events can be controlled by increasing the coupling action time and improving the URT repair ability. When an emergency event occurs, the analysis of coupling effect and the accompanied chain evolution will help managers to make scientific judgment on the development trend of the emergency events and make targeted emergency defense measures.

This work provides a graph network solution for pilot brain fatigue state inference based on electroencephalography (EEG) fatigue indicators. Two graph methods are built as follows. The first one uses a single EEG signal sample as a node, and fatigue detection as a node classification task in a graph network. The developed graph network is then utilized to extract the correlation among different samples to achieve multisample joint decision making. The second method uses a single EEG signal sample as a graph structure, and EEG fatigue prediction as a graph classification task. Electrode position correlation is used to construct a graph. The feature fusion of adjacent electrodes is obtained through the connection relationship among nodes in a graph structure to improve network learning accuracy. In addition, a Bayesian optimization method is proposed to model the randomness of attention weights, and a Bayesian graph attention network is built. This work constructs a based-graph deep learning structures to achieve a pilot fatigue detection model with high accuracy, good generalization, and strong adaptability. Experimental results demonstrate the effectiveness of the proposed model.

Multiagent cooperative systems can be used to conceptualize many real-world problems. Reinforcement learning is a particularly effective tool. The issue of bias in$Q$-function value estimation in single-agent reinforcement learning has garnered a lot of interest and substantial study. Indeed, this challenge endures in multiagent reinforcement learning, primarily owing to the inclusion of maximization operations. The crux of the matter lies in the inability to seamlessly extrapolate single-agent reinforcement learning algorithms to their multiagent counterparts. In this article, we introduce a more encompassing and straightforward principle: the notion of appropriate value correction. We suggest replacing the maximization operation with a monotonically nondecreasing function to obtain more accurate value estimates. We theoretically demonstrate that this operation effectively reduces the potential overestimation bias in the QMIX algorithm. Ultimately, our methodology, dubbed the SMIX algorithm—a fusion of the QMIX algorithm empowered by the Softmax operator, attains state-of-the-art outcomes across diverse multiagent cooperative tasks. This success extends to challenging domains such as StarCraft II, marking it as one of the most formidable games to date.

In this paper, a graph neural network anomaly detection framework is proposed to improve the safety of linear induction motors, a key component of high-speed maglev trains. In our framework, each sensor sequence is treated as a separate feature. The similarity and correlation between multi-dimensional features are learned as prior knowledge for graph structure learning. The spatial-temporal graph attention network incorporates prior knowledge to learn complex correlations between nodes. Furthermore, the framework optimises a joint model for anomaly detection, avoiding the trap of falling into either local or global optimisation and thus achieving the most stable detection. Experimental results of our method on four real-world datasets show that it is more accurate than other state-of-the-art methods in detecting anomalies and capturing inter-sensor correlations. Further analysis of graph attention weights and visualization subgraphs show that our framework is well interpretable and allowing users to locate the root cause of anomalies.