Current status and prospects of research on safety situation awareness of high speed railway operation environment

Purpose–The safety of high-speed rail operation environments is an important guarantee for the safe operation of high-speed rail.The operating environment of the high-speed rail is complex,and the main factors affecting the safety of high-speed rail operating environment include meteorological disasters,perimeter intrusion and external environmental hazards.The purpose of the paper is to elaborate on the current research status and team research progress on the perception of safety situation in high-speed rail operation environment and to propose directions for further research in the future.Design/methodology/approach–In terms of the mechanism and spatio-temporal evolution law of the main influencing factors on the safety of high-speed rail operation environments,the research status is elaborated,and the latest research progress and achievements of the team are introduced.This paper elaborates on the research status and introduces the latest research progress and achievements of the team in terms of meteorological,perimeter and external environmental situation perception methods for high-speed rail operation.Findings–Based on the technical route of“situational awareness evaluation warning active control,”a technical system for monitoring the safety of high-speed train operation environments has been formed.Relevant theoretical and technical research and application have been carried out around the impact of meteorological disasters,perimeter intrusion and the external environment on high-speed rail safety.These works strongly support the improvement of China’s railway environmental safety guarantee technology.Originality/value–With the operation of CR450 high-speed trains with a speed of 400 kmper hour and the application of high-speed train autonomous driving technology in the future,new and higher requirements have been put forward for the safety of high-speed rail operation environments.The following five aspects of work are urgently needed:(1)Research the single factor disaster mechanism of wind,rain,snow,lightning,etc.for high-speed railways with a speed of 400 kms per hour,and based on this,study the evolution characteristics of multiple safety factors and the correlation between the high-speed driving safety environment,revealing the coupling disastermechanism ofmultiple influencing factors;(2)Research covers multi-source data fusion methods and associated features such as disaster monitoring data,meteorological information,route characteristics and terrain and landforms,studying the spatio-temporal evolution laws of meteorological disasters,perimeter intrusions and external environmental hazards;(3)In terms of meteorological disaster situation awareness,research high-precision prediction methods for meteorological information time series along high-speed rail lines and study the realization of small-scale real-time dynamic and accurate prediction of meteorological disasters along high-speed rail lines;(4)In terms of perimeter intrusion,research amulti-modal fusion perception method for typical scenarios of high-speed rail operation in all time,all weather and all coverage and combine artificial intelligence technology to achieve comprehensive and accurate perception of perimeter security risks along the high-speed rail line and(5)In terms of external environment,based on the existing general network framework for change detection,we will carry out research on change detection and algorithms in the surrounding environment of highspeed rail.

A Deep Learning‑Based Approach for Terminal Area Flight Flow Operational Safety Situation Awareness

Safety is the cornerstone of the civil aviation industry and the enduring focus of civil aviation.This paper uses air traffic complexity and potential aircraft conflict relationships as entry points to study the operational safety level of terminal area flight flows and proposes a deep learning-based method for safety situation awareness in terminal area aircraft operations.Firstly,a more comprehensive and precise safety situation assessment features are constructed.Secondly,a deep clustering situation recognition model with added safety situation information capture layer is proposed.Finally,a spatiotemporal graph convolutional neural network based on attention mechanism is constructed for predicting safety situations.Experimental results from a real dataset show that:(1)The proposed model surpasses traditional models across all evaluated dimensions;(2)the recognition model ensures that the encoded features capture distinctive safety situation information,thereby enhancing model interpretability and task alignment;(3)the prediction model demonstrates superior integrated modeling capabilities in both spatial and temporal dimensions.Ultimately,this paper elucidates the spatiotemporal evolution characteristics of air traffic safety situation levels,offering valuable insights for air traffic safety management.

System Architecture and Key Technologies of Network Security Situation Awareness System YHSAS

Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHSAS is developed for national backbone network,large network operators,large enterprises and other large-scale network.This paper describes its architecture and key technologies:Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis,Knowledge Representation and Management of Super Large-Scale Network Security,Multi-Level,Multi-Granularity and Multi-Dimensional Network Security Index Construction Method,Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology,and so on.The performance tests show that YHSAS has high real-time performance and accuracy in security situation analysis and trend prediction.The system meets the demands of analysis and prediction for large-scale network security situation.

A situation awareness assessment method based on fuzzy cognitive maps

The status of an operator’s situation awareness is one of the critical factors that influence the quality of the missions.Thus the measurement method of the situation awareness status is an important topic to research.So far,there are lots of methods designed for the measurement of situation awareness status,but there is no model that can measure it accurately in real-time,so this work is conducted to deal with such a gap.Firstly,collect the relevant physiological data of operators while they are performing a specific mission,simultaneously,measure their status of situation awareness by using the situation awareness global assessment technique(SAGAT),which is known for accuracy but cannot be used in real-time.And then,after the preprocessing of the raw data,use the physiological data as features,the SAGAT’s results as a label to train a fuzzy cognitive map(FCM),which is an explainable and powerful intelligent model.Also,a hybrid learning algorithm of particle swarm optimization(PSO)and gradient descent is proposed for the FCM training.The final results show that the learned FCM can assess the status of situation awareness accurately in real-time,and the proposed hybrid learning algorithm has better efficiency and accuracy.

A network security situation awareness method based on layered attack graph

The real-time of network security situation awareness(NSSA)is always affected by the state explosion problem.To solve this problem,a new NSSA method based on layered attack graph(LAG)is proposed.Firstly,network is divided into several logical subnets by community discovery algorithm.The logical subnets and connections between them constitute the logical network.Then,based on the original and logical networks,the selection of attack path is optimized according to the monotonic principle of attack behavior.The proposed method can sharply reduce the attack path scale and hence tackle the state explosion problem in NSSA.The experiments results show that the generation of attack paths by this method consumes 0.029 s while the counterparts by other methods are more than 56 s.Meanwhile,this method can give the same security strategy with other methods.

Situation Awareness Data Fusion Method Based on Library Events

Microelectronic technology and communication technology are developed in deep manner;the computing mode has been transferred from traditional computer-centered to human centered pervasive.So,the concept of Internet of things(IoT)is gradually put forward,which allows people to access information about their surroundings on demand through different terminals.The library is the major public space for human to read and learn.How to provide a more comfortable library environment to better meet people’s learning requirements is a place where the Internet of things plays its role.The purpose of this paper is to solve the difference between the data fusion of library environment and the data fusion of other environments by the method of data fusion oriented to library.This paper presents a general technical framework of situational awareness for smart library system which includes a data fusion middleware.It can process data and inform the upper module of the changed library environment after deploying the smart library system in a library,including data collection and processing,how to judge whether events are triggered,how the system reacts,and the acquisition and update of user preferences.This paper presents a situational awareness recommendation method based on an effective data fusion model and algorithm for library after conducting experimental in service of library,which give more accurate of book recommendation than traditional method and good learning service environment of library for readers.

A Situational Awareness Method for Initial Insulation Fault of Distribution Network Based on Multi-Feature Index Comprehensive Evaluation

Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend of the initial insulation fault is unknown,which brings difficulties to the distribution inspection.In order to solve the above problems,a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed.Firstly,the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network,and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system.Secondly,considering all kinds of fault factors of the distribution network and the influence of the power supply region,the evaluation method of the initial insulation fault situation of the distribution network is proposed,and the development situation of the distribution network insulation fault is classified according to the evaluation method.Then,principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data,and the support vector machine(SVM)was trained.The optimal parameter combination of the SVM model was found by the grid search method,and a multi-class SVM model based on 1-v-1 method was constructed.Finally,the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples.The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%,and the perception accuracy of 4 situation levels is above 96%.In addition,the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage,which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation.The correctness and effectiveness of this method are verified.

Situation Awareness of Active Distribution Network:Roadmap,Technologies,and Bottlenecks

With the rapid development of local generation and demand response,the active distribution network(ADN),which aggregates and manages miscellaneous distributed resources,has moved from theory to practice.Secure and optimal operations now require an advanced situation awareness(SA)system so that operators are aware of operation states and potential risks.Current solutions in distribution supervisory control and data acquisition(DSCADA)as well as the distribution automation system(DAS)generally are not able to meet the technology requirements of SA.In this paper,the authors’participation in the project of developing an SA system as the basic component of a practical active distribution management system(ADMS)deployed in Beijing,China,is presented.This paper reviews the ADN’s development roadmap by illustrating the changes that are made in elements,topology,structure,and control scheme.Taking into consideration these hardware changes,a systematic framework is proposed for the main components and the functional hierarchy of an SA system for the ADN.The SA system’s implementation bottlenecks are also presented,including,but not limited to issues in big data platform,distribution forecasting,and security evaluation.Potential technology solutions are also provided.

Concept and Research Framework for Coordinated Situation Awareness and Active Defense of Cyber-physical Power Systems Against Cyber-attacks

Due to the tight coupling between the cyber and physical sides of a cyber-physical power system(CPPS),the safe and reliable operation of CPPSs is being increasingly impacted by cyber security.This situation poses a challenge to traditional security defense systems,which considers the threat from only one side,i.e.,cyber or physical.To cope with cyberattacks,this paper reaches beyond the traditional one-side security defense systems and proposes the concept of cyber-physical coordinated situation awareness and active defense to improve the ability of CPPSs.An example of a regional frequency control system is used to show the validness and potential of this concept.Then,the research framework is presented for studying and implementing this concept.Finally,key technologies for cyber-physical coordinated situation awareness and active defense against cyber-attacks are introduced.

Review on strategies of space-based optical space situational awareness

Space-based optical(SBO)space surveillance has attracted widespread interest in the last two decades due to its considerable value in space situation awareness(SSA).SBO observation strategy,which is related to the performance of space surveillance,is the top-level design in SSA missions reviewed.The recognized real programs about SBO SAA proposed by the institutions in the U.S.,Canada,Europe,etc.,are summarized firstly,from which an insight of the development trend of SBO SAA can be obtained.According to the aim of the SBO SSA,the missions can be divided into general surveillance and space object tracking.Thus,there are two major categories for SBO SSA strategies.Existing general surveillance strategies for observing low earth orbit(LEO)objects and beyond-LEO objects are summarized and compared in terms of coverage rate,revisit time,visibility period,and image processing.Then,the SBO space object tracking strategies,which has experienced from tracking an object with a single satellite to tracking an object with multiple satellites cooperatively,are also summarized.Finally,this paper looks into the development trend in the future and points out several problems that challenges the SBO SSA.

Ship behavior prediction via trajectory extraction-based clustering for maritime situation awareness

This study presents a method in which historical AIS data are used to predict the future trajectory of a se-lected vessel.This is facilitated via a system intelligence-based approach that can be subsequently utilized to provide enhanced situation awareness to navigators and future autonomous ships,aiding proactive col-lision avoidance.By evaluating the historical ship behavior in a given geographical region,the method applies machine learning techniques to extrapolate commonalities in relevant trajectory segments.These commonalities represent historical behavior modes that correspond to the possible future behavior of the selected vessel.Subsequently,the selected vessel is classified to a behavior mode,and a trajectory with respect to this mode is predicted.This is achieved via an initial clustering technique and subsequent tra-jectory extraction.The extracted trajectories are then compressed using the Karhunen-Loéve transform,and clustered using a Gaussian Mixture Model.The approach in this study differs from others in that tra-jectories are not clustered for an entire region,but rather for relevant trajectory segments.As such,the extracted trajectories provide a much better basis for clustering relevant historical ship behavior modes.A selected vessel is then classified to one of these modes using its observed behavior.Trajectory predic-tions are facilitated using an enhanced subset of data that likely correspond to the future behavior of the selected vessel.The method yields promising results,with high classification accuracy and low prediction error.However,vessels with abnormal behavior degrade the results in some situations,and have also been discussed in this study.

An experimental analysis of situation awareness for cockpit display interface evaluation based on flight simulation

Aircraft cockpit display interface （CDI） is one of the most important human-machine interfaces for information perceiving. During the process of aircraft design, situation awareness （SA） is frequently considered to improve the design, as the CDI must provide enough SA for the pilot to maintain the flight safety. In order to study the SA in the pilot-aircraft system, a cockpit flight simulation environment is built up, which includes a virtual instrument panel, a flight visual display and the corresponding control system. Based on the simulation environment, a human-in-the-loop experiment is designed to measure the SA by the situation awareness global assessment technique （SAGAT）. Through the experiment, the SA degrees and heart rate （HR） data of the subjects are obtained, and the SA levels under different CDI designs are analyzed. The results show that analyzing the SA can serve as an objective way to evaluate the design of CDI, which could be proved from the consistent HR data. With this method, evaluations of the CDI design are performed in the experimental flight simulation environment, and optimizations could be guided through the analysis.

Situation Awareness and Sensitivity Analysis for Absorption of Grid-connected Renewable Energy Power Generation Integrating Robust Optimization and Radial Basis Function Neural Network

The significance of situation awareness(SA) in power systems has increased to enhance the utilization of gridconnected renewable energy power generation(REPG). This paper proposes a real-time calculation architecture based on the integration of robust optimization(RO) and artificial intelligence. First, the time-series simulation of the REPG consumption capacity is carried out under the current grid operating conditions. RO is employed in this simulation, given the randomness of the REPG output and the grid load. Then, the radial basis function neural network(RBFNN) is trained with the results under different parameters using the artificial fish swarm algorithm(AFSA), enabling the neural network(NN) to be the replacement for the time-series simulation model. The trained NN can quickly perceive the REPG absorption situation within the predefined grid structure and period. Moreover, the Sobol' method is adopted to conduct the global sensitivity analysis for different parameters based on the input-output samples obtained by the trained NN. Finally, the simulation experiments based on the modified IEEE 14-bus system prove the real-time performance and accuracy of the proposed SA architecture.

Hastily formed knowledge networks and distributed situation awareness for collaborative robotics

In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support.This is particularly important in applications pertaining to emergency rescue and crisis management.During operational missions,data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans.We describe this as the creation of Hastily Formed Knowledge Networks(HFKNs).The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans.The information collected ranges from low-level sensor data to high-level semantic knowledge,the latter represented in part as RDF Graphs.The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents.This is done through the distributed synchronization of RDF Graphs shared between agents.High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members.The system is empirically validated and complexity results of the proposed algorithms are provided.Additionally,a field robotics case study is described,where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.

Electric Vehicle Charging Situation Awareness for Ultra-Short-Term Load Forecast of Charging Stations

Electric vehicles(EVs)are expected to be key nodes connecting transportation-electricity-communication networks.Advanced automotive electronics technologies enhance EVs’perception,computing,and communication capacity,which in turn can boost the operational efficiency of intelligent transportation systems(ITSs).EVs couple the ITS to the power system,providing a promising solution to charging congestion and transformer overload via navigation and forecasting approaches.This study proposes a privacy-preserving EV charging situation awareness framework and method to forecast the ultra-short-term load of charging stations.The proposed method only relies on public information from commercial service providers.In the case study,data are powered by the Baidu LBS cloud and EV-SGCC platform,and the experiment is conducted within an area of Pudong New District in Shanghai.Based on the results,the charging load of charging stations can be adequately forecasted more than 1 min ahead with low communication and computing power requirements.This research provides the basis for further studies on operation optimization and electricity market transaction of charging stations.

A hierarchical algorithm for cyberspace situational awareness based on analytic hierarchy process

The existing network security management systems are unable either to provide users with useful security situation and risk assessment, or to aid administrators to make right and timely decisions based on the current state of network. These disadvantages always put the whole network security management at high risk. This paper establishes a simulation environment, captures the alerts as the experimental data and adopts statistical analysis to seek the vulnerabilities of the services provided by the hosts in the network. According to the factors of the network, the paper introduces the two concepts： Situational Meta and Situational Weight to depict the total security situation. A novel hierarchical algorithm based on analytic hierarchy process （AHP） is proposed to analyze the hierarchy of network and confirm the weighting coefficients. The algorithm can be utilized for modeling security situation, and determining its mathematical expression. Coupled with the statistical results, this paper simulates the security situational trends. Finally, the analysis of the simulation results proves the algorithm efficient and applicable, and provides us with an academic foundation for the implementation in the security situation

Deep learning for real-time social media text classification for situation awareness-using Hurricanes Sandy,Harvey,and Irma as case studies

Social media platforms have been contributing to disaster management during the past several years.Text mining solutions using traditional machine learning techniques have been developed to categorize the messages into different themes,such as caution and advice,to better understand the meaning and leverage useful information from the social media text content.However,these methods are mostly event specific and difficult to generalize for cross-event classifications.In other words,traditional classification models trained by historic datasets are not capable of categorizing social media messages from a future event.This research examines the capability of a convolutional neural network(CNN)model in cross-event Twitter topic classification based on three geo-tagged twitter datasets collected during Hurricanes Sandy,Harvey,and Irma.The performance of the CNN model is compared to two traditional machine learning methods:support vector machine(SVM)and logistic regression(LR).Experiment results showed that CNN models achieved a consistently better accuracy for both single event and crossevent evaluation scenarios whereas SVM and LR models had lower accuracy compared to their own single event accuracy results.This indicated that the CNN model has the capability of pre-training Twitter data from past events to classify for an upcoming event for situational awareness.

Data Mining and Spatial Analysis of Social Media Text Based on the BERT-CNN Model to Achieve Situational Awareness: a Case Study of COVID-19

In response to the COVID-19,social media big data has played an important role in epidemic warning,tracking the source of infection,and public opinion monitoring,providing strong technical support for China’s epidemic prevention and control work.The paper used Sina Weibo posts related to COVID-19 hashtags as the data source,and built a BERT-CNN deep learning model to perform fine-grained and high-precision topic classificationon massive social media posts.Taking Shenzhen as a region of interest,we mined the“epidemic data bulletin”and“daily life impact”posts during the epidemic for spatial analysis.The results show that the confirmed communities and designated hospitals in Shenzhen as a whole present the characteristics of“sparse east and dense west”,and there is a strong positive spatial correlation between the number of confirmed cases and social media response.Specifically,Nanshan District,Futian District and Luohu District have more confirmed cases due to large population movements and dense transportation networks,and social media has responded more violently,and people’s lives have been greatly affected.However,Yantian District,Pingshan District and Dapeng New District showed opposite characteristics.The case study results further show that using deep learning methods to mine text information in social media is scientifically feasible for improving situational awareness and decision support during the COVID-19.

A scalable model for network situational awareness based on Endsley＇ s situation model

The paper introduces the Endsley＇ s situation model into network security to describe the network security situation, and improves Endsley＇s data processing to suit network alerts. The proposed model contains the information of incident frequency, incident time and incident space. The HoneyNet dataset is selected to evaluate the proposed model in the evaluation. The paper proposes three definitions to depict and predigest the whole situation extraction in detail, and a fusion component to reduce the influence of alert redundancy on the total security situation. The less complex extraction makes the situation analysis more efficient, and the fine-grained model makes the analysis have a better expansibility. Finally, the situational variation curves are simulated, and the evaluation results prove the situation model applicable and efficient.

Jamming Monitoring Technology of the Space Situational Awareness Facilities:A Comprehensive Survey

Complicated electromagnetic environments of the space situational awareness facilities(i.e.,satellite navigation systems,radar)would significantly impact normal operations.Effective monitoring and the corresponding diagnosis of the jamming signals are essential to normal opera-tions and the innovations in anti-jamming equipment.This paper demonstrates a comprehensive survey on jamming monitoring algorithms and applications.The methods in dealing with jamming signals are summarized primarily.Subsequently,the jamming detection,identification,and direc-tion finding techniques are addressed separately.Based on the established studies,we also provide some potential trends of the demonstrated jamming monitoring issues.