Situation Awareness Technology Research Articles

Staring laser situation awareness technology with wide-FOV and high-angle positioning accuracy based on two-dimensional grating and two-channels

Current and future battlefields face the emerging threat of laser weapon development. Existing laser situation awareness technology cannot provide simultaneous operation of wide field-of-view (FOV) and high angle positioning accuracy. This study introduces a system employing a two-dimensional grating and dual-channel approach that offers high precision in situation awareness about laser wavelengths and angle positioning across a wide FOV. The system consists of two main components: an approximate measurement channel and an accurate measurement channel. The former includes a wide-FOV lens and a focal plane array detector (FPA1), acquiring preliminary angle information of the laser. The latter, comprising a two-dimensional grating, a narrow-FOV lens, and a FPA2, captures the incoming laser’s wavelength and precise azimuth and pitch through the analysis of diffraction spot position and distance. Experimental findings confirm that the accuracies of angle and central wavelength measurements exceed 0.05° and 8 nm, respectively, for a FOV of 120°.

Research and Simulation of Partial Discharge Phenomenon Sensing Technology for Substation Inspection Robots

Abstract This paper thoroughly investigates the phenomenon of partial discharge in substations and proposes a set of simulation experimental apparatus to replicate various types of partial discharge defects in switchgear. By modifying the switchgear, incorporating models for different types of partial discharge, and equipping various standard partial discharge sensors, accurate simulations of phenomena such as point discharge, suspended discharge, gap discharge, and particle discharge are achieved. Through this system, the effectiveness of different detection methods, such as ultrasonic, high-frequency, and transient voltage methods, is validated for various defect models. Furthermore, utilizing an indoor inspection robot guide rail, the paper simulates the environment of partial discharge in substations, enabling the validation of the robot’s partial discharge detection performance and typical patterns under different conditions. This work is dedicated to providing situational awareness technology for partial discharge phenomenon detection for substation inspection robots, so that the inspection robot can detect various abnormal partial discharge phenomena in transformers or other substation equipment through micro sensors to promote the stable operation of the substation.

Security Situation Awareness System Based on Artificial Intelligence

There is rapid growth of the security threats faced by enterprises and the security attacks technology is also established at a higher level. Enterprises are facing an escalating threat landscape marked by sophisticated security attacks. To address the structural and technical challenges of information security situational awareness, a method for designing an artificial intelligence-driven system is proposed. In order to solve the system structure and key technical problems of information security situational awareness technology of artificial intelligence, a method of designing information security situational awareness system is proposed, and experiments are carried out through the method. By analyzing the data sources that the system needs to collect, including network traffic mirror data, log data, security intelligence and support data, this paper verifies the feasibility of the system method of information security situational awareness technology of artificial intelligence technology. The proposed core competence platform has the characteristics of low delay and robust real-time capabilities. By presetting the event processing topology, we can quickly build the event processing process, and build the corresponding event processing topology model according to different processing requirements to meet the business requirements. The AI-powered information security situational awareness system substantially enhances security awareness and prediction accuracy.

Day-ahead optimization of integrated electricity and thermal system combining multiple types of demand response strategies and situation awareness technology

Under the dual pressure of energy shortage and environmental pollution, relying only on increasing the installed capacity of units and line transmission capacity cannot cope with the conflict between the growth of power demand and the difficulty of grid expansion in the long run. Demand response conducts users to change their energy consumption habits through system-issued electricity prices or incentives, so that the demand of the load side can be adjusted flexibly, which can further enhance the consumption of wind power and improve system economics. Based on the background of diversified energy use, this paper proposes a day-ahead optimal scheduling strategy for integrated electricity and thermal system considering multiple types of demand response. Firstly, the dispatch framework of integrated electricity and thermal system with the situation awareness technology is constructed to address uncertainties of Renewable Energy Sources, thus helping system mitigate uncertain risks. Secondly, the demand response mechanism of power system and regional thermal inertia of thermal system are modeled, respectively, to uncover the principles of load regulation of different energy systems; Then, a day-ahead optimal scheduling model for the integrated thermal and electricity system is developed, and the consumption evaluation index is integrated to indicate energy utilization efficiency; Finally, a combined electric-heat system model with 39-node grid and 6-node heat network is developed, and the positive effects of considering multiple types of demand response and situation awareness technology on promoting the consumption of renewable energy and improving the energy efficiency of the system are verified through the case study.

Efficient multi-branch segmentation network for situation awareness in autonomous navigation

Real-time and high-precision situational awareness technology is critical for autonomous navigation of unmanned surface vehicles (USVs). In particular, robust and fast obstacle semantic segmentation methods are essential. However, distinguishing between the sea and the sky is challenging due to the differences between port and maritime environments. In this study, we built a dataset that captured perspectives from USVs and unmanned aerial vehicles in a maritime port environment and analysed the data features. Statistical analysis revealed a high correlation between the distribution of the sea and sky and row positional information. Based on this finding, a three-branch semantic segmentation network with a row position encoding module (RPEM) was proposed to improve the prediction accuracy between the sea and the sky. The proposed RPEM highlights the effect of row coordinates on feature extraction. Compared to the baseline, the three-branch network with RPEM significantly improved the ability to distinguish between the sea and the sky without significantly reducing the computational speed.

Network awareness of security situation information security measurement method based on data mining

Awareness of Network Security Situation (abbreviated as NSS for short) technology is in a period of vigorous development recently. NSS technology means network security situational awareness technology. It refers to the technology of collecting, processing, and analyzing various real-time information in the network to understand and evaluate the current network security status. It can not only find network security threats, but also reflect the NSS in the system security metrics, and provide users with targeted security protection measures. Based on data mining methods, this paper analyzed and models perceived threats and security events with data mining algorithms, and improved information security measurement methods based on association analysis. This paper proposed network security information analysis and NSS based on data mining, and analyzed the experimental results of network awareness of NSS information security measurement. The experimental results showed that when the Timer was 8, the accuracy of the awareness of NSS information security measurement method based on data mining can reach 92.89%. The data mining model had the highest accuracy of 93.14% in situation understanding and evaluation of KDDCup-99 dataset. The results showed that the model can accurately predict the NSS. When Timer was 6, the highest accuracy of the model was 92.71%. In general, the NSS prediction mining model based on KDDCup-99 can better understand, evaluate and predict the situation.

Dynamic Aggregation Method for Load Aggregators Considering Users’ Deviation Electricity

Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China’s power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system.

Application of machine learning algorithm and data evaluation in computer network security situation awareness technology

With the rapid development of information technology and the rapid popularization of the Internet, while people enjoy the convenience and efficiency brought about by new technologies, they are also suffering from the harm caused by cyber attacks. In addition to efficiently thwarting network assaults, a high volume of complicated security event data might unintentionally increase the strain of policy makers. At present, NS threats mainly include network viruses, trojans, DOS (Denial-Of-Service), etc. For the increasingly complex Network Security (NS) problems, the traditional rule-based network monitoring technology is difficult to predict the unknown attack behavior. Environment-based, dynamic and integrated data fusion can integrate data from a macro perspective. In recent years, Machine Learning (ML) technology has developed rapidly, which could easily train, test and predict existing third-party models. It uses ML algorithms to find out the association between data rather than manually sets rules. Support vector machine is a common ML method, which can predict the security of the network well after training and testing. In order to monitor the overall security status of the entire network, NS situation awareness refers to the real-time and accurate reproduction of network attacks using the reconstruction approach. Situation awareness technology is a powerful network monitoring and security technology, but there are many problems in the existing NS technology. For example, the state of the network cannot be accurately detected, and its change rule cannot be understood. In order to effectively predict network attacks, this paper adopted a technology based on ML and data analysis, and constructed a NS situational awareness model. The results showed that the detection efficiency of the model based on ML and data analysis was 7.18% higher than that of the traditional NS state awareness model.

Research on hierarchical network security situation awareness data fusion method in big data environment

ABSTRACT With the increase in the number of network users, data information has become more abundant, and query speed and coverage have greatly improved. In this era, the problem of information leakage is relatively serious, and some malicious software has invaded the network system of user devices, increasing the threat to network security. In order to improve the analysis ability of network security situation awareness, this study designs a hierarchical network security situation awareness data fusion method under the big data environment. On the basis of data fusion technology and network security situation awareness technology, the hierarchical network security situation information is collected. Then, its features are extracted, and the network security situation awareness process is constructed based on RBF neural. By using this process, hierarchical network security situation awareness data can be obtained. The data is first sorted out and analyzed, and then the data is filtered. Finally, the matrix three decomposition data fusion algorithm is selected as the blueprint of the data fusion method. It can complete the hierarchical network security situation awareness data fusion. Experimental results show that the proposed method has high matching accuracy, little influence on the data fusion time due to the increase of data volume and low energy consumption in the fusion process.

Design of Nanosecond-Level Transient Electric Field Sensor and Its Application in HVDC Converter Station

In this paper, a nanosecond-level transient electric field (E-field) sensor based on a monopole electrically small rod antenna is proposed. The working principle and design process of the sensor are analyzed in detail while a finite element simulation is carried out to verify the response characteristics of the sensor both in time and frequency domain, whose upper cut-off frequency can reach 680 MHz. For the purpose of assessing the actual detection capability of the sensor in HVDC converter stations, the sensor designed in this paper was used to detect the transient radiation E-field in the valve hall during the artificial short-circuit test in the Zhoushan 1200 kV five-terminal flexible DC transmission project. The measurement results of the transient E-field indicates that the sensor is able to respond instantaneously to the radiated E-field generated by the operation of the key power electronic equipment in the valve hall. It demonstrates that the designed sensor can be effectively applied in the scenario of the measurement of transient electromagnetic environment and non-invasive situational awareness technology for high-voltage devices in HVDC systems.

Network Situation Assessment Method Based on Improved BP Neural Network

Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results.

Deep learning-based security situational awareness and detection technology for power networks in the context of big data

Abstract With the comprehensive promotion of “big data + energy”, new power network security threats are also more prominent, and the traditional security system mainly based on “protection” will face great challenges. Firstly, this paper proposes four kinds of network security situational awareness detection techniques based on distributed data analysis by combining the characteristics of big data in power networks. Secondly, the CRIT-LSTM power network security situational awareness model is constructed by improving its loss evaluation process using the cross entropy (CE) function and improving the LSTM unit structure using linear unit (ReLU). Finally, the performance of the three models is compared and analyzed under two aspects of neural network training and testing and various metrics to verify the models’ effectiveness. The results show that the improved CRIT-LSTM model based on deep learning, combining LSTM and ReLU algorithms, has an RMSE of 0.717 for the training set and 0.806 for the test set. 7.32% accuracy and 10.51% improvement in recall compared to the LSTM-only model. The power network security situational awareness model based on the CRIT-LSTM model proposed in this paper integrates various security system functions to maximize the defense against attacks and reduce unnecessary security risk losses.

Situation Element Extraction Based on Fuzzy Rough Set and Combination Classifier.

Generalized network security situation awareness technology is divided into three processes: situation element extraction, situation understanding, and situation prediction. Situation element extraction is the most critical step in the whole process, and its extraction quality will directly affect the accuracy of situation understanding and prediction. In view of the shortcomings of current situation element extraction methods, this study makes an in-depth study on the network security situation element extraction algorithm and proposes a situation element extraction model based on the fuzzy rough set and combined classifier, which is used to improve the accuracy of situation elements acquisition, so as to provide a better data basis for situation understanding and prediction. In this study, the theory of fuzzy rough set is used to reduce the attributes of data without reducing the ability of data classification, which reduces the complexity of data; using the combination classifier theory and particle swarm optimization algorithm, a framework of situation element extraction is built, which can extract situation elements more accurately. The experimental results show that the network security situation element extraction framework proposed in this study can effectively shorten the extraction time of situation elements and improve the accuracy of situation element acquisition under the premise of ensuring the ability of data classification, thus proving the effectiveness and feasibility of the situation element extraction framework proposed in this study.

DUG Insight: A software package for big-data analysis and visualisation, and its demonstration for passive radar space situational awareness using radio telescopes

As the demand for software to support the processing and analysis of massive radio astronomy datasets increases in the era of the SKA, we demonstrate the interactive workflow building, data mining, processing, and visualisation capabilities of DUG Insight. We test the performance and flexibility of DUG Insight by processing almost 68,000 full sky radio images produced from the Engineering Development Array (EDA2) over the course of a three day period. The goal of the processing was to passively detect and identify known Resident Space Objects (RSOs: satellites and debris in orbit) and investigate how radio interferometry could be used to passively monitor aircraft traffic. These signals are observable due to both terrestrial FM radio signals reflected back to Earth and out-of-band transmission from RSOs. This surveillance of the low Earth orbit and airspace environment is useful as a contribution to space situational awareness and aircraft tracking technology. From the observations, we made 40 detections of 19 unique RSOs within a range of 1,500 km from the EDA2. This is a significant improvement on a previously published study of the same dataset and showcases the flexible features of DUG Insight that allow the processing of complex datasets at scale. Future enhancements of our DUG Insight workflow will aim to realise real-time acquisition, detect unknown RSOs, and continue to process data from SKA-relevant facilities.

Research on Network Security Situation Awareness Technology Based on Big Data

Research on Network Security Situation Awareness Technology Based on Big Data

Research on Situational Awareness Technology of Industrial Control Network Based on Big Data

With the rapid advancement of national strategies such as "Internet+" and "Made in China 2025", industrial control systems have been widely used in various industries such as energy, municipalities, transportation, water conservancy and aerospace, etc. The security of industrial control networks is always affecting the lifeline of the national economy. Therefore, the security of industrial control networks has not been given enough attention, resulting in frequent industrial control network security incidents, which makes us realize the importance of creating an industrial control network situational awareness system that integrates assessment and prediction. In this paper, we study the situational awareness technology of industrial control network based on big data, and integrate situational extraction as the premise, situational assessment as the core, and situational prediction as the goal to comprehensively sense the situational awareness of industrial control network system. Firstly, we adopt two ways of industrial control network data collection in the situational extraction, one is the use of traffic mirroring bypass access to sensor-aware terminals, without affecting the original production services on the premise of network traffic data collection. The second is the use of WireShark tools to achieve industrial control network traffic packet collection and statistics of traffic packets per second, analysis of the current network state, build Hadoop big data platform to achieve offline data pre-processing and feature extraction, and build Flink and TensorFlow model for graph neural network model training and complete prediction. Secondly, we use a combination of hierarchical analysis and correlation analysis to evaluate the situation, and use the evaluation graph to show the current network security state. Thirdly, the situation prediction is done by training the graph neural network model offline. We use the Flink real-time computation engine to read the data of the industrial control network into the graph neural network model in real time, which is used to enhance the feature representation of each node and to predict the probability of anomaly occurrence of the industrial control network in the future period. Finally, the graph neural network model of this paper is compared with traditional neural networks and machine learning models, and the accuracy and false alarm rate indexes are used to demonstrate the high accuracy and robustness of this model.

Research on public health crisis early warning system based on context awareness.

BACKGROUND: With the continuous expansion of urban scale and the increasing concentration of population, public health crisis has become an important part of urban residents’ health management. The outbreak of the COVID-19 pandemic in Wuhan in 2020 has sounded the alarm.OBJECTIVE: With the government at all levels to carry out the construction of urban Internet of things and information internet, the Internet backbone network has been built, deployed a large number of sensors, and collected a large number of urban situation data.METHODS: In this paper, situational awareness technology is introduced into public health emergency services.RESULTS: By constructing ontology, situational data and residents’ health data are integrated. Through key technologies such as situational data collection, data fusion and data mining, real-time perception of environmental conditions of public health emergency scene is realized, and situational data fusion and situational information reasoning model are constructed.CONCLUSIONS: The model is applied to the public health crisis emergency simulation system to verify the effectiveness of the model.

Research on Network Security Situational Awareness Technology under Data Fusion

While the popularization and application of the Internet brings convenience to people’s lives, it also triggers a series of network security issues. However, traditional network security methods can no longer meet the requirements of network security, and network security situational awareness technology integrates security elements from all levels, can fully grasp the network security status as a whole, and give warnings to network security trends to improve the network safety technology level.

Analysis and Application of intelligent Power Control System Cyber Security Situation Awareness Based on Wavelet Neural Network

In recent years, international industrial control network security incidents have occurred frequently. As a core component of the industrial control field, intelligent power control systems are increasingly threatened by external network attacks. Based on the current research status of power industrial control network security, closely combining the development of active monitoring and defense technology in the public network field and the problems encountered by network security operators in actual work, this paper uses data mining methods to study the power control system network security situation awareness technology. Combing operational data collection and integrated processing, situation index screening and extraction, we use wavelet neural network analysis method to train the sampled data set, and finally calculate the true value of the network security status through deep intelligent learning. Finally, we conclude that the artificial intelligence algorithm based on wavelet neural network can be used for power control system network security situation awareness. In actual work, it can predict the situation value for a period of time in the future and assist network security personnel in judgment and decision-making.

Optimal Dispatching Technology of Distributed Power Generation Based on Situation Awareness

The comprehensive utilization of distributed power sources can effectively reduce the consumption of fossil energy and reduce carbon emissions. Based on the situational awareness technology, this paper proposes a distributed power optimization dispatching technology, with the least carbon emission as the optimization goal and meeting the grid load demand as the constraint condition. First, in the situation awareness stage, collect information such as the operation status of the power grid; second, use the data collected by situation awareness to simulate the operation of the power grid in the situation understanding stage to achieve in-depth perception of the power grid; finally, consider the grid operating load in the situation guidance stage Demand adjustment and optimization strategy to realize flexible dispatch and control of the power grid in a favorable direction. This paper takes the IEEE 30-bus system as an example for simulation analysis, and the simulation results verify the effectiveness of the proposed method.