Parametric message passing-based relative navigation in joint tactical information distribution system

Relative navigation is a key feature in the joint tactical information distribution system（JTIDS）.A parametric message passing algorithm based on factor graph is proposed to perform relative navigation in JTIDS.First of all,the joint posterior distribution of all the terminals＇ positions is represented by factor graph.Because of the nonlinearity between the positions and time-of-arrival（TOA） measurement,messages cannot be obtained in closed forms by directly using the sum-product algorithm on factor graph.To this end,the Euclidean norm is approximated by Taylor expansion.Then,all the messages on the factor graph can be derived in Gaussian forms,which enables the terminals to transmit means and covariances.Finally,the impact of major error sources on the navigation performance are evaluated by Monte Carlo simulations,e.g.,range measurement noise,priors of position uncertainty and velocity noise.Results show that the proposed algorithm outperforms the extended Kalman filter and cooperative extended Kalman filter in both static and mobile scenarios of the JTIDS.

Information standardization for typical toxic and harmful algae in China's coastal waters——a case study of Karenia mikimotoi

To assist the researches of toxic and harmful algae and provide government workers with judgment basis for decision-making related events,we established a biological information management system for toxic and harmful algae in China’s of fshore to assist relevant research.In this study,Karenia mikimotoi was studied as a typical toxic and harmful algae species,and the basic biological information and biogeographic distribution information of K.mikimotoi were systematically studied and collected.In the part of basic biological information,the name,toxin,and molecular characteristic sequence of K.mikimotoi were sorted out by literature searching and website browsing.Through experimental means,the relevant information of morphological identifi cation,pigment composition,and lipid composition were obtained.In the part of biogeographic distribution information,this study sorted out the information of K.mikimotoi,analyzed the characteristics of its occurrence,and completed the standardized construction of biogeographic distribution information.Through the collation of basic biological information and biogeographic distribution information of K.mikimotoi,the standardization of related information was completed,which provided template and method reference for information collection of other toxic and harmful algae species,which was benefi cial to the database analysis and design.

Optimization in the Migration Problem of Mobile Agents in Distributed Information Retrieval Systems

In this paper, we employ genetic algorithms to solve the migration problem (MP). We propose a new encoding scheme to represent trees, which is composed of two parts: the pre-ordered traversal sequence of tree vertices and the children number sequence of corresponding tree vertices. The proposed encoding scheme has the advantages of simplicity for encoding and decoding, ease for GA operations, and better equilibrium between exploration and exploitation. It is also adaptive in that, with few restrictions on the length of code, it can be freely lengthened or shortened according to the characteristics of the problem space. Furthermore, the encoding scheme is highly applicable to the degree-constrained minimum spanning tree problem because it also contains the degree information of each node. The simulation results demonstrate the higher performance of our algorithm, with fast convergence to the optima or sub-optima on various problem sizes. Comparing with the binary string encoding of vertices, when the problem size is large, our algorithm runs remarkably faster with comparable search capability. Key words distributed information retrieval - mobile agents - migration problem - genetic algorithms CLC number TP 301. 6 Foundation item: Supported by the National Natural Science Foundation of China (90104005), the Natural Science Foundation of Hubei Province and the Hong Kong Polytechnic University under the grant G-YD63Biography: He Yan-xiang (1952-), male, Professor, research direction: distributed and parallel processing, multi-agent systems, data mining and e-business.

Alarm-Based Root Cause Analysis Based on Weighted Fault Propagation Topology for Distributed Information Network

A distributed information network with complex network structure always has a challenge of locating fault root causes.In this paper,we propose a novel root cause analysis(RCA)method by random walk on the weighted fault propagation graph.Different from other RCA methods,it mines effective features information related to root causes from offline alarms.Combined with the information,online alarms and graph relationship of network structure are used to construct a weighted graph.Thus,this approach does not require operational experience and can be widely applied in different distributed networks.The proposed method can be used in multiple fault location cases.The experiment results show the proposed approach achieves much better performance with 6%higher precision at least for root fault location,compared with three baseline methods.Besides,we explain how the optimal parameter’s value in the random walk algorithm influences RCA results.

Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

In the realm of Multi-Label Text Classification(MLTC),the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches.Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content,often overlooking intrinsic textual cues such as label statistical features.In contrast,these endogenous insights naturally align with the classification task.In our paper,to complement this focus on intrinsic knowledge,we introduce a novel Gate-Attention mechanism.This mechanism adeptly integrates statistical features from the text itself into the semantic fabric,enhancing the model’s capacity to understand and represent the data.Additionally,to address the intricate task of mining label correlations,we propose a Dual-end enhancement mechanism.This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation.We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets.These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism.Our final model unequivocally outperforms the baseline model,attesting to its robustness.These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models.

Integrating Smart Building Energy Data into Smart Grid Applications in the Intelligent Secondary Substations

Automation has arrived in the low voltage grid domain. In the next few years, the secondary substation—at the barriers of medium and low voltage grids—will thus be upgraded to enable novel functions. In this paper, we present various smart grid applications running on such intelligent secondary substations(iSSN) including their interaction with each other. We integrate energy consumption and production data, as well as forecasts, sensed from the smart buildings’ energy management systems(BEMSs) into the operation of the low voltage grid. A suitable framework for those modular applications includes features to initiate their installation, update, removal, the remote operator site, and not requiring staff on-site for such typical reappearing maintenance tasks.

Fast achievement of quantum state transfer and distributed quantum entanglement by dressed states

We propose schemes to realize quantum state transfer and prepare quantum entanglement in coupled cavity and cavity-fiber-cavity systems,respectively,by using the dressed state method.We first give the expression of pulses shape by using dressed states and then find a group of Gaussian pulses that are easy to realize in experiment to replace the ideal pulses by curve fitting.We also study the influence of some parameters fluctuation,atomic spontaneous emission,and photon leakage on fidelity.The results show that our schemes have good robustness.Because the atoms are trapped in different cavities,it is easy to perform different operations on different atoms.The proposed schemes have the potential applications in dressed states for distributed quantum information processing tasks.

A Complex Algorithm for Solving a Kind of Stochastic Programming

Considering that the probability distribution of random variables in stochastic programming usually has incomplete information due to a perfect sample data in many real applications, this paper discusses a class of two-stage stochastic programming problems modeling with maximum minimum expectation compensation criterion (MaxEMin) under the probability distribution having linear partial information (LPI). In view of the nondifferentiability of this kind of stochastic programming modeling, an improved complex algorithm is designed and analyzed. This algorithm can effectively solve the nondifferentiable stochastic programming problem under LPI through the variable polyhedron iteration. The calculation and discussion of numerical examples show the effectiveness of the proposed algorithm.

Huber's Minimax Approach in Distribution Classes with Bounded Variances and Subranges with Applications to Robust Detection of Signals

A brief survey of former and recent results on Hubers minimax approach inrobust statistics is given. The least informative distributions minimizing Fisher information forlocation over several distribution classes with upper-bounded variances and subranges are writtendown. These least informative distributions are qualitatively different from classical Huberssolution and have the following common structure: (i) with relatively small variances they areshort-tailed, in particular normal; (ii) with relatively large variances they are heavy-tailed, inparticular the Laplace; (iii) they are compromise with relatively moderate variances. These resultsallow to raise the efficiency of minimax robust procedures retaining high stability as compared toclassical Hubers procedure for contaminated normal populations. In application to signal detectionproblems, the proposed minimax detection rule has proved to be robust and close to Hubers forheavy-tailed distributions and more efficient than Hubers for short-tailed ones both in asymptoticsand on finite samples.

Mission-driven autonomous perception and fusion based on UAV swarm

Distributed autonomous situational awareness is one of the most important foundation for Unmanned Aerial Vehicle(UAV)swarm to implement various missions.Considering the application environment being usually characterized by strong confrontation,high dynamics,and deep uncertainty,the distributed situational awareness system based on UAV swarm needs to be driven by the mission requirements,while each node in the network can autonomously avoid collisions and perform detection mission through limited resource sharing as well as complementarity of respective advantages.By efficiently solving the problems of self-avoidance,autonomous flocking and splitting,joint estimation and control,etc.,perception data from multi-platform multi-source should be extracted and fused reasonably,to generate refined,tailored target information and provide reliable support for decision-making.

Study of Human Action Recognition Based on Improved Spatio-temporal Features

Most of the exist action recognition methods mainly utilize spatio-temporal descriptors of single interest point while ignoring their potential integral information, such as spatial distribution information. By combining local spatio-temporal feature and global positional distribution information(PDI) of interest points, a novel motion descriptor is proposed in this paper. The proposed method detects interest points by using an improved interest point detection method. Then, 3-dimensional scale-invariant feature transform(3D SIFT) descriptors are extracted for every interest point. In order to obtain a compact description and efficient computation, the principal component analysis(PCA) method is utilized twice on the 3D SIFT descriptors of single frame and multiple frames. Simultaneously, the PDI of the interest points are computed and combined with the above features. The combined features are quantified and selected and finally tested by using the support vector machine(SVM) recognition algorithm on the public KTH dataset. The testing results have showed that the recognition rate has been significantly improved and the proposed features can more accurately describe human motion with high adaptability to scenarios.

A Shrinkage Estimator for Combination of Bioassays

A shrinkage estimator and a maximum likelihood estimator are proposed in this paper for combination of bioassays. The shrinkage estimator is obtained in closed form which incorporates prior information just on the common log relative potency after the homogeneity test for combination of bioassays is accepted. It is a practical improvement over other estimators which require iterative procedure to obtain the estimator for the relative potency. A real data is also used to show the superiorities for the newly-proposed procedures.