Kinematic Absolute Positioning with Quad-Constellation GNSS

The absolute positioning technique is based on a point positioning mode with a single Global Navigation Satellite System (GNSS) receiver, which has been widely used in many fields such as vehicle navigation and kinematic surveying. For a long period, this positioning technique mainly relies on a single GPS system. With the revitalization of Global Navigation Satellite System (GLONASS) constellation and two newly emerging constellations of BeiDou Navigation Satellite System (BDS) and Galileo, it is now feasible to carry out the absolute positioning with quad-constellation of GPS, GLONASS, BDS, and Galileo. A combination of multi-constellation observations can offer improved reliability, availability, and accuracy for position solutions. In this chapter, combined GPS/GLONASS/BDS/Galileo point positioning models for both traditional single point positioning (SPP) and precise point positioning (PPP) are presented, including their functional and stochastic components. The traditional SPP technique has a positioning accuracy at a meter level, whereas the PPP technique can reach an accuracy of a centimeter level. However, the later relies on the availability of precise ephemeris and needs a long convergence time. Experiments were carried out to assess the kinematic positioning performance in the two different modes. The positioning results are compared among different constellation combinations to demonstrate the advantages of quad-constellation GNSS

Experimental study of a rotary valve multi-bed rapid cycle pressure swing adsorption process based medical oxygen concentrator

Rapid cycle pressure swing adsorption (RCPSA) characterized by short cycle time permits adsorbents to be used more frequently and increases process productivity. A rotary valve multi-bed RCPSA process is proposed to improve the performance of miniature oxygen concentrator. The RCPSA air separation system is highly integrated by four adsorbent beds and a rotary valve. The effects of process parameters on the performance of the RCPSA system were conducted by experiments. Results showed that the system could produce 1 L min−1 of ~ 92% O2 with ~ 30% of oxygen recovery and ~ 78 kg·TPD−1 of bed size factor (BSF) from compressed air at low adsorption and desorption pressure ratio (~ 250:101 kPa). The minimum BSF with ~ 5 s optimal cycle time is a sharp decline with increasing of pressure ratio. However, a reversed trend between BSF and unit power at different pressure ratios is prominently emerged

Image Mining for Generating Ontology Databases of Geographical Entities

Abstract: This paper extracts the basic geographic information from remote sensing images at first, and then studies the resolution granularity of the remote sensing images which can be applied to distinguish the features of corresponding objects by adopting global-covered remote sensing images with multi-frequency spectra and multi-resolution. Thus necessary feature information for the geographical ontology database, such as texture characteristic information can be mined and through our data mining strategy from remote sensing images based on the formal concept analysis theory, data mining methods for texture features are achieved. The emphases of this paper are the mining method for texture characteristic for generating ontology database of the geographical entity. By mining the texture characteristics, we can find the partial structure that frequently appears in the remote sensing image data, and find the restriction relationship between the central pixel and its neighborhood pixels in partial regions of images. This process is constituted by the following four steps: sampling areas partition normalized processing, characteristic data mining, building Hasse graph and generating rules. Through the computation about remote sensing image data mining, we put the uncertainty problem about characteristics form data mining up to a height of information theory and study it, and find the consolidate mathematics expression between information quantity and uncertainty about the characteristics in order to resolve the quantitative evaluation problem between information quantity and uncertainty of remote sensing image. This paper introduces the concepts-driven data mining framework to uncertainty process, so as to guide the idiographic algorithm and process during the image mining procedure. According to the characteristic of remote sensing images, combining with all kinds of GIS data, we can describe the essential characteristics that build ontology database of the geographical entity

Info2vec: an aggregative representation method in multi-layer and heterogeneous networks

Mapping nodes in multi-layer and heterogeneous networks to low-dimensional vectors has wide applications in community detection, node classification and link prediction, etc. In this paper, a generalized graph representation learning framework is proposed for information aggregation in various multi-layer and heterogeneous networks. Specifically, an aggregation network is firstly obtained by graph transformation, generating potential information links based on the network structure on different layers. A comprehensive measurement of the similarity between different nodes in the aggregation network is then carried out by aggregating the information of nodes’ identities of structure, nearness and attributes etc. Based on the comprehensive similarity values the nodes have, a context graph can be generated using a simple edge percolation method, which provides a basis facilitating some important downstream work such as classification, clustering and prediction etc. We demonstrate the effectiveness of the new framework in identifying subnetworks in a cyberspace network, where it significantly outperforms all the existing baselines.Ministry of Education (MOE)G.Y. and Y.K. were supported by NSSFC 2019-SKJJ-C-005. G.X. was supported by the Ministry of Education (MOE), Singapore, under contract RG19/20

Dynamic Defense against Stealth Malware Propagation in Cyber-Physical Systems: A Game-Theoretical Framework

Stealth malware is a representative tool of advanced persistent threat (APT) attacks, which poses an increased threat to cyber-physical systems (CPS) today. Due to the use of stealthy and evasive techniques, stealth malwares usually render conventional heavy-weight countermeasures inapplicable. Light-weight countermeasures, on the other hand, can help retard the spread of stealth malwares, but the ensuing side effects might violate the primary safety requirement of CPS. Hence, defenders need to find a balance between the gain and loss of deploying light-weight countermeasures, which normally is a challenging task. To address this challenge, we model the persistent anti-malware process as a shortest-path tree interdiction (SPTI) Stackelberg game with both static version (SSPTI) and multi-stage dynamic version (DSPTI), and safety requirements of CPS are introduced as constraints in the defender’s decision model. The attacker aims to stealthily penetrate the CPS at the lowest cost (e.g., time, effort) by selecting optimal network links to spread, while the defender aims to retard the malware epidemic as much as possible. Both games are modeled as bi-level integer programs and proved to be NP-hard. We then develop a Benders decomposition algorithm to achieve the Stackelberg equilibrium of SSPTI, and design a Model Predictive Control strategy to solve DSPTI approximately by sequentially solving an 1+δ approximation of SSPTI. Extensive experiments have been conducted by comparing proposed algorithms and strategies with existing ones on both static and dynamic performance metrics. The evaluation results demonstrate the efficiency of proposed algorithms and strategies on both simulated and real-case-based CPS networks. Furthermore, the proposed dynamic defense framework shows its advantage of achieving a balance between fail-secure ability and fail-safe ability while retarding the stealth malware propagation in CPS

Mass and Heat Transfer of Pressure Swing Adsorption Oxygen Production Process with Small Adsorbent Particles

Rapid-cycle pressure swing adsorption (PSA) with small adsorbents particles is intended to improve mass transfer rate and productivity. However, the mass transfer mechanisms are changed with reduction of particle size during rapid-cycle adsorption process. A heat and mass transfer model of rapid-cycle PSA air separation process employing small LiLSX zeolite particles is developed and experimentally validated to numerically analyze the effects of mass transfer resistances on the characteristics of cyclic adsorption process. Multicomponent Langmuir model and linear driving force model are employed for characterizing the adsorption equilibrium and kinetic. The results of numerical analysis demonstrate that the dominant mass transfer resistance of small adsorbents particles is a combination of film resistance, axial dispersion effect and macropore diffusion resistance. The oxygen purity, recovery and productivity of the product are overestimated by ~2–4% when the effect of axial dispersion on mass transfer is ignored. As particle size decreases, the front of nitrogen-adsorbed concentration and gas temperature become sharp, which effectively improves the performance. However, the adverse effect of axial dispersion on the mass transfer becomes significant at very small particles conditions. It is nearly identical shapes of nitrogen concentration and gas temperature profiles after adsorption and desorption steps. The profiles are pushed forward near the production end with an increase in bed porosities. The optimal oxygen recovery and productivity are achieved with a particle diameter of 0.45 mm and bed porosity of 0.39 during the PSA process

Parallel Learning of Dynamics in Complex Systems

Dynamics always exist in complex systems. Graphs (complex networks) are a mathematical form for describing a complex system abstractly. Dynamics can be learned efficiently from the structure and dynamics state of a graph. Learning the dynamics in graphs plays an important role in predicting and controlling complex systems. Most of the methods for learning dynamics in graphs run slowly in large graphs. The complexity of the large graph’s structure and its nonlinear dynamics aggravate this problem. To overcome these difficulties, we propose a general framework with two novel methods in this paper, the Dynamics-METIS (D-METIS) and the Partitioned Graph Neural Dynamics Learner (PGNDL). The general framework combines D-METIS and PGNDL to perform tasks for large graphs. D-METIS is a new algorithm that can partition a large graph into multiple subgraphs. D-METIS innovatively considers the dynamic changes in the graph. PGNDL is a new parallel model that consists of ordinary differential equation systems and graph neural networks (GNNs). It can quickly learn the dynamics of subgraphs in parallel. In this framework, D-METIS provides PGNDL with partitioned subgraphs, and PGNDL can solve the tasks of interpolation and extrapolation prediction. We exhibit the universality and superiority of our framework on four kinds of graphs with three kinds of dynamics through an experiment