Personnel Localization Method in Transformer Substation Based on Factor Graph

A SINS/GNSS location method based on factor diagram is proposed to meet the requirement of accurate location of substation construction personnel. In this paper, the inertial autonomous positioning, carrier motion information acquisition and satellite positioning technologies are integrated. The factor graph method is adopted to abstract the measurement information received by inertial navigation and satellite into factor nodes, and the state information into variable nodes, so as to construct the SINS/GNSS construction personnel positioning fusion factor graph model. The Gauss-Newton iterative method is used to implement the recursive updating of variable nodes, and the optimal estimate of the location information of the construction personnel is calculated, which realized the high precision location of the construction personnel. The factor graph method is verified by pedestrian navigation data. The results show that the factor graph method can continuously and stably output high-precision positioning results, and realize non-equidistant fusion of SINS and GNSS. The positioning accuracy is better than Kalman filter algorithm, and the horizontal positioning accuracy is less than 1 m. Therefore, the factor graph method proposed can provide accurate location information for substation construction personnel.

强乘积图的最小强半径

A strong product graph is denoted by G_(1)■G_(2),where G_(1) and G_(2) are called its factor graphs.This paper gives the range of the minimum strong radius of the strong product graph.And using the relationship between the cartesian product graph G_(1)■G_(2) and the strong product graph G_(1)■G_(2),another different upper bound of the minimum strong radius of the strong product graph is given.

Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization

Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.

Resilient tightly coupled INS/UWB integration method for indoor UAV navigation under challenging scenarios

Based on the high positioning accuracy,low cost and low-power consumption,the ultra-wide-band(UWB)is an ideal solution for indoor unmanned aerial vehicle(UAV)localization and navigation.However,the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture.A resilient tightly-coupled inertial navigation system(INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper.A factor graph optimization(FGO)method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios.To deal with the impact of UWB non-line-of-sight(NLOS)signals and noise uncertainty,the conventional neural net-works(CNNs)are introduced into the stochastic modelling to improve the resilience and reliability of the integration.Based on the status that the UWB features are limited,a‘two-phase'CNNs structure was designed and implemented:one for signal classification and the other one for measurement noise prediction.The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario.Compared to classical FGO method,the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios.The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.

A New Encryption-Then-Compression Scheme on Gray Images Using the Markov Random Field

Compressing encrypted images remains a challenge.As illustrated in our previous work on compression of encrypted binary images,it is preferable to exploit statistical characteristics at the receiver.Through this line,we characterize statistical correlations between adjacent bitplanes of a gray image with the Markov random field(MRF),represent it with a factor graph,and integrate the constructed MRF factor graph in that for binary image reconstruction,which gives rise to a joint factor graph for gray images reconstruction(JFGIR).By exploiting the JFGIR at the receiver to facilitate the reconstruction of the original bitplanes and deriving theoretically the sum-product algorithm(SPA)adapted to the JFGIR,a novel MRF-based encryption-then-compression(ETC)scheme is thus proposed.After preferable universal parameters of the MRF between adjacent bitplanes are sought via a numerical manner,extensive experimental simulations are then carried out to show that the proposed scheme successfully compresses the first 3 and 4 most significant bitplanes(MSBs)for most test gray images and the others with a large portion of smooth area,respectively.Thus,the proposed scheme achieves significant improvement against the state-of-the-art leveraging the 2-D Markov source model at the receiver and is comparable or somewhat inferior to that using the resolution-progressive strategy in recovery.

A Multi-Vehicle Cooperative Localization Method Based on Belief Propagation in Satellite Denied Environment

The global navigation satellite system(GNSS)is currently being used extensively in the navigation system of vehicles.However,the GNSS signal will be faded or blocked in complex road environments,which will lead to a decrease in positioning accuracy.Owing to the higher-precision synchronization provided in the sixth generation(6G)network,the errors of ranging-based positioning technologies can be effectively reduced.At the same time,the use of terahertz in 6G allows excellent resolution of range and angle,which offers unique opportunities for multi-vehicle cooperative localization in a GNSS denied environment.This paper introduces a multi-vehicle cooperative localization method.In the proposed method,the location estimations of vehicles are derived by utilizing inertial measurement and then corrected by exchanging the beliefs with adjacent vehicles and roadside units.The multi-vehicle cooperative localization problem is represented using a factor graph.An iterative algorithm based on belief propagation is applied to perform the inference over the factor graph.The results demonstrate that our proposed method can offer a considerable capability enhancement on localization accuracy.

Factor graph based navigation and positioning for control system design:A review

Navigation and positioning is an important and challenging problem in many control engineering applications.It provides feedback information to design controllers for systems.In this paper,a bibliographical review on factor graph based navigation and positioning is presented.More specifically,the sensor modeling,the factor graph optimization methods,and the topology factor based cooperative localization are reviewed.The navigation and positioning methods via factor graph are considered and classified.Focuses in the current research of factor graph based navigation and positioning are also discussed with emphasis on its practical application.The limitations of the existing methods,some solutions for future techniques,and recommendations are finally given.

Modified attitude factor graph fusion method for unmanned helicopter under atmospheric disturbance

The Unmanned Aerial Helicopter(UAH)has attracted increasing attention in the military and civil areas with the unique flight performance.The significant impact on the attitude measurement performance of UAHs by the strong airflow disturbance is an essential factor threatening flight safety.To improve the attitude measurement performance of UAHs under atmospheric disturbance,an attitude fusion method over the factor graph is applied and provides the plug-and-play capability.Based on the relationship between position,velocity and attitude,a new attitude correction algorithm for the Modified Attitude Factor Graph Fusion(MAFGF)navigation method is designed and constructed through the fused position and velocity information.Finally,results of simulation and experiment are given to show the effectiveness of the proposed method.

Low-complexity factor-graph-based MAP detector for filter bank multicarrier systems

FBMC(Filter Bank Multicarrier)modulation is considered one of the waveform candidates in fifth generation wireless communication technology because of its several improved features compared to conventional orthogonal frequency division multiplexing schemes.A soft-input-soft-output factor-graph-based maximum-a-posterior detector is applied to FBMC systems.The detector achieves better performance than simple linear equalizers such as minimum mean square error and zero forcing in coded systems while exhibiting only a linear growth in complexity with the number of simultaneous interfering symbols.Furthermore,the proposed detector can be easily extended to cases where FBMC modulation is combined with multiple-input-multiple-output processing.The complexity of the detector is analyzed and the simulation results demonstrated its superior performance.

Inference Attacks on Genomic Data Based on Probabilistic Graphical Models

The rapid progress and plummeting costs of human-genome sequencing enable the availability of large amount of personal biomedical information,leading to one of the most important concerns—genomic data privacy.Since personal biomedical data are highly correlated with relatives,with the increasing availability of genomes and personal traits online(i.e.,leakage unwittingly,or after their releasing intentionally to genetic service platforms),kin-genomic data privacy is threatened.We propose new inference attacks to predict unknown Single Nucleotide Polymorphisms(SNPs)and human traits of individuals in a familial genomic dataset based on probabilistic graphical models and belief propagation.With this method,the adversary can predict the unobserved genomes or traits of targeted individuals in a family genomic dataset where some individuals’genomes and traits are observed,relying on SNP-trait association from Genome-Wide Association Study(GWAS),Mendel’s Laws,and statistical relations between SNPs.Existing genome inferences have relatively high computational complexity with the input of tens of millions of SNPs and human traits.Then,we propose an approach to publish genomic data with differential privacy guarantee.After finding an approximate distribution of the input genomic dataset relying on Bayesian networks,a noisy distribution is obtained after injecting noise into the approximate distribution.Finally,synthetic genomic dataset is sampled and it is proved that any query on synthetic dataset satisfies differential privacy guarantee.

Enhancing recommender systems by incorporating social information

Although recommendation techniques have achieved distinct developments over the decades,the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality.Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings.How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge.In this paper,based on a factor graph model,we formalize the problem in a semi-supervised probabilistic model,which can incorporate different user information,user relationships,and user-item ratings for learning to predict the unknown ratings.We evaluate the method in two different genres of datasets,Douban and Last.fm.Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms.Furthermore,a distributed learning algorithm is developed to scale up the approach to real large datasets.

Iterative multiuser detection for high spectral efficiency reverse-link of multibeam satellite via expectation propagation

Multibeam satellite communications employing full frequency reuse have the potential to increase spectral efficiency.However,they suffer from severe inter-beam interference.An expectation propagation based message passing algorithm is proposed for decoding multi-user transmissions in the reverse link of multi-beam satellite communications with full frequency reuse.Compared with an iterative MMSE(Minimum Mean Square Error)interference cancellation algorithm,the proposed algorithm reduces the cubic complexity to square complexity in the number of interfering beams.Numerical results show that the proposed algorithm outperforms the iterative MMSE algorithm slightly in terms of bit error rate when the energy per bit to noise power spectral density ratio is low.The performance of both algorithms is the same for other cases.