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Swarm intention identification via dynamic distribution probability image

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Chinese Journal of Aeronautics 2024年第10期37卷 380-392页

作者： Yinhan WANG Jiang WANG Shaoming HE Fei WANG Qi WANG School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China institute of computing technology Chinese Academy of SciencesBeijing 100190China

In the realm of decision-making for defense and security applications,it is paramount to swiftly and accurately identify the intentions of incoming *** identification methods predominantly focus on single-target applications and overlook the perturbations introduced by measurement *** this study,we propose a novel concept:the Dynamic Distribution Probability(DDP)image,which is constructed using the estimated state and its covariance *** grayscale pixel value within the image signifies the probability of the presence of the agent within the *** proposed identification scheme integrates the use of Extended Kalman Filter(EKF),Convolutional Neural Network(CNN),Back Propagation(BP)network,and Gated Recurrent Unit(GRU)***,the DDP image is processed through a CNN to distill the formation characteristics,and the estimated swarm state from EKF is inputted into a BP network to deduce the kinematic *** outputs from both networks are summed and subsequently channeled into a GRU network to capture temporal *** numerical simulations and flight experiments are presented to demonstrate the robust anti-noise capability of the proposed scheme compared with conventional methods,as well as its superior training efficiency.

关键词： Intention Identification Kalman filters Convolutional neural networks Swarm intelligence

CCM-FL:Covert communication mechanisms for federated learning in crowd sensing IoT

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Digital Communications and Networks 2024年第3期10卷 597-608页

作者： Hongruo Zhang Yifei Zou Haofei Yin Dongxiao Yu Xiuzhen Cheng institute of Intelligent computing School of Computer Science and TechnologyShandong UniversityQingdao266237China

The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the private information of users in federated learning has become an important research *** with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning *** this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things *** from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal ***,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning *** analysis and nu-merical simulations are presented to show the performance of our covert communication *** hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

关键词： Covert communications Federated learning Crowd sensing SINR model

Privacy Preserving Algorithm for Spectrum Sensing in Cognitive Vehicle Networks

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Chinese Journal of Electronics 2024年第1期33卷 30-42页

作者： Hongning LI Tonghui HU Jiexiong CHEN Xiuqiang WU Qingqi PEI Guangzhou institute of technology Xidian University Guangzhou CEPREI Shaanxi Key Laboratory of Blockchain and Secure computing

The scarcity of spectrum resources fails to meet the increasing throughput demands of vehicular networks. There is an urgent need to maximize the utilization of spectrum bands in mobile networks. To ascertain the availability of spectrum bands, users should engage in wireless channel sensing and collaboration. However, spectrum sensing data always involves users’ privacy, such as their location. This paper first introduces sensing trajectory inference attack in cognitive vehicular networks and then proposes a data confusion-based privacy-preserving algorithm and a cryptonym array-based privacy-preserving aggregation scheme for spectrum sensing in cognitive vehicular networks. Unlike existing methods, the proposed schemes transmit confused data during the aggregation process. This deliberate obfuscation makes it almost impossible to infer users’ location from the transmitted data. The analysis demonstrates the resilience of the proposed schemes against sensing trajectory inference attack.

关键词： Privacy Data privacy Wireless sensor networks Authentication Data aggregation Throughput Inference algorithms

Extracting Integrated Features of Electronic Medical Records Big Data for Mortality and Phenotype Prediction

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Chinese Journal of Electronics 2024年第3期33卷 776-792页

作者： Fei LI Yiqiang CHEN Yang GU Yaowei WANG institute of computing technology University of Chinese Academy of Sciences Pengcheng Laboratory

The key to synthesizing the features of electronic medical records(EMR) big data and using them for specific medical purposes,such as mortality and phenotype prediction,is to integrate the individual medical event and the overall multivariate time series feature extraction automatically,as well as to alleviate data imbalance *** paper provides a general feature extraction method to reduce manual intervention and automatically process large-scale *** processing uses two variational auto-encoders(VAEs) to automatically extract individual and global *** avoids the well-known posterior collapse problem of Transformer VAE through a uniquely designed "proportional and stabilizing" mechanism and forms a unique means to alleviate the data imbalance *** conducted experiments using ICU-STAY patients' data from the MIMIC-Ⅲ database and compared them with the mainstream EMR time series processing *** results show that the method extracts visible and comprehensive features,alleviates data imbalance problems and improves the accuracy in specific predicting tasks.

关键词： Databases Time series analysis MIMICs Manuals Big Data Feature extraction Transformers

A Pair of Pseudo-differential Operators Involving Index Whittaker Transform in L_(2)^(a) (R_(+);m_(a)(x)dx)

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Acta Mathematica Sinica,English Series 2024年第6期40卷 1420-1430页

作者： Jeetendrasingh MAAN Akhilesh PRASAD Department of Mathematics and computing Indian Institute of Technology(Indian School of Mines)Dhanbad-826004India

Pseudo-differential operators(PDO)Q(x,L_(a,x))and Q(x,L_(a,x))involving the index Whittaker transform are *** for these operators in Hilbert space L_(2)^(a)(R+;m_(a)(x)dx)are obtained.A symbol classΩis *** product an... 详细信息

关键词： Pseudo-differential operator index Whittaker transform confluent hypergeometric functions Hilbert space

A Parallel Discrete Event Simulation Engine for the Low-Earth-Orbit Satellite Constellation Networks

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China Communications 2024年第8期21卷 264-275页

作者： Su Hailong Liu Yaoqi Zhou Yiqing Shi Jinglin Li Hongguang Qian Manli institute of computing technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Lab of Processors Institute of Computing TechnologyCASBeijing 100190China Zhongke Nanjing Mobile Communication&computing Innovation institute Nanjing 211135China Beijing Sylincom technology Co. Beijing 100190China

Low-Earth-Orbit satellite constellation networks(LEO-SCN)can provide low-cost,largescale,flexible coverage wireless communication *** dynamics and large topological sizes characterize *** development and application testing of LEO-SCN are challenging to carry out in a natural *** platforms are a more effective means of technology *** available simulators have a single function and limited simulation *** needs to be a simulator for full-featured *** this paper,we apply the parallel discrete-event simulation technique to the simulation of LEO-SCN to support large-scale complex system simulation at the packet *** solve the problem that single-process programs cannot cope with complex simulations containing numerous entities,we propose a parallel mechanism and algorithms LP-NM and LP-YAWNS for *** the experiment,we use ns-3 to verify the acceleration ratio and efficiency of the above *** results show that our proposed mechanism can provide parallel simulation engine support for the LEO-SCN.

关键词： constellation low earth orbit satellite ns-3 null-message parallel discrete-event simulation

Path signature-based XAI-enabled network time series classification

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Science China(Information Sciences) 2024年第7期67卷 91-106页

作者： Le SUN Yueyuan WANG Yongjun REN Feng XIA Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment technology(CICAEET) Nanjing University of Information Science and Technology School of computing Technologies Royal Melbourne Institute of Technology

Classifying network time series(NTS) is crucial for automating network administration and ensuring cyberspace security. It enables the detection of anomalies, the identification of network attacks,and the monitoring of performance issues, thereby providing valuable support for network protection and optimization. However, modern communication networks pose challenges for NTS classification *** include handling large-scale and complex NTS data, extracting features from intricate datasets, and addressing explainability requirements. These challenges are particularly pronounced for complex 5G networks. Notably, explainability has become crucial for the widespread deployment of network automation for 5G networks and beyond. To tackle these challenges, we propose a path-signature-based NTS classification model called recurrent signature(Recur Sig). This innovative model is designed to overcome the time-consuming feature selection problem by utilizing deep-learning(DL) techniques. Additionally, it provides a solution for addressing the black-box issue associated with DL models in network automation systems(NAS) by incorporating an explainable classification approach. Extensive experimentation on various public datasets demonstrates that Recur Sig outperforms existing models in accuracy and explainability. The results indicate its potential for application in cyberspace security and automated network management, offering an explainable solution for network protection and optimization.

关键词： network time series classification explainable artificial intelligence path signature automated network management recurrent neural network

On-chip quantum NOON state sensing for temperature and humidity

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Chinese Physics B 2024年第10期33卷 15-20页

作者： Weihong Luo Chao Wu Yuxing Du Chang Zhao Miaomiao Yu Pingyu Zhu Kaikai Zhang Ping Xu institute for Quantum Information&State Key Laboratory of High Performance computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China

A maximal photon number entangled state,namely NOON state,can be adopted for sensing with a quantum *** this work,we designed silicon quantum photonic chips containing two types of Mach-Zehnder interferometerswherein the two-photon NOON state,sensing element for temperature or humidity,is *** with classicallight or single photon case,two-photon NOON state sensing shows a solid enhancement in the sensing resolution *** the first demonstration of on-chip quantum photonic sensing,it reveals the advantages of photonic chips forhigh integration density,small-size,stability for multiple-parameter sensing serviceability.A higher sensing precision isexpected to beat the standard quantum limit with a higher photon number NOON state.

关键词： quantum sensing NOON state photonic chip

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

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Chinese Journal of Mechanical Engineering 2024年第2期37卷 330-339页

作者： Rui Xiong Zian Zhao Cheng Chen Xinggang Li Weixiang Shen School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVIC 3122Australia

Battery remaining charging time(RCT)prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles(EVs).Also,it is of great significance to improve EV users’***,the RCT for a lithiumion battery pack in EVs changes with temperature and other battery *** study proposes an electrothermal model-based method to accurately predict battery ***,a characteristic battery cell is adopted to represent the battery pack,thus an equivalent circuit model(ECM)of the characteristic battery cell is established to describe the electrical behaviors of a battery ***,an equivalent thermal model(ETM)of the battery pack is developed by considering the influence of ambient temperature,thermal management,and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal ***,the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from-20℃to 45℃.The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

关键词： Electric vehicles Lithium-ion batteries Remaining charging time Electrothermal model