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Electrical Data Matrix Decomposition in Smart Grid
Get PDFAs the development of smart grid and energy internet, this leads to a significant increase in the amount of data transmitted in real time. Due to the mismatch with communication networks that were not designed to carry high-speed and real time data, data losses and data quality degradation may happen constantly. For this problem, according to the strong spatial and temporal correlation of electricity data which is generated by human’s actions and feelings, we build a low-rank electricity data matrix where the row is time and the column is user. Inspired by matrix decomposition, we divide the low-rank electricity data matrix into the multiply of two small matrices and use the known data to approximate the low-rank electricity data matrix and recover the missed electrical data. Based on the real electricity data, we analyze the low-rankness of the electricity data matrix and perform the Matrix Decomposition-based method on the real data. The experimental results verify the efficiency and efficiency of the proposed scheme
Research on Privacy Preserving Data Mining
No full textIn recent years, with the explosive development in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. A number of methods and techniques have been developed for privacy preserving data mining. This paper provided a wide survey of different privacy preserving data mining algorithms and analyzed the representative techniques for privacy preservation. The existing problems and directions for future research are also discussed
A Novel SINS/IUSBL Integration Navigation Strategy for Underwater Vehicles
No full textThis paper presents a novel SINS/IUSBL integration navigation strategy for underwater vehicles. Based on the principle of inverted USBL (IUSBL), a SINS/IUSBL integration navigation system is established, where the USBL device and the SINS are both rigidly mounted onboard the underwater vehicle, and fully developed in-house, the integration navigation system will be able to provide the absolute position of the underwater vehicle with a transponder deployed at a known position beforehand. Furthermore, the state error equation and the measurement equation of SINS/IUSBL integration navigation system are derived, the difference between the position calculated by SINS and the absolute position obtained by IUSBL positioning technology is used as the measurement information. The observability of the integration system is analyzed based on the singular value decomposition (SVD) method. Finally, a mathematical simulation is performed to demonstrate the effectiveness of the proposed SINS/IUSBL integration approach, and the observable degrees of the state variables are also analyzed
Underground Disease Detection Based on Cloud Computing and Attention Region Neural Network
No full textDetecting the underground disease is very crucial for the roadbed health monitoring and maintenance of transport facilities, since it is very closely related to the structural health and reliability with the rapid development of road traffic. Ground penetrating radar (GPR) is widely used to detect road and underground diseases. However, it is still a challenging task due to data access anywhere, transmission security and data processing on cloud. Cloud computing can provide scalable and powerful technologies for large-scale storage, processing and dissemination of GPR data. Combined with cloud computing and radar detection technology, it is possible to locate the underground disease quickly and accurately. This paper deploys the framework of a ground disease detection system based on cloud computing and proposes an attention region convolution neural network for object detection in the GPR images. Experimental results of the precision and recall metrics show that the proposed approach is more efficient than traditional objection detection method in ground disease detection of cloud based system
RAIM Algorithm Based on Fuzzy Clustering Analysis
No full textWith the development of various navigation systems (such as GLONASS, Galileo, BDS), there is a sharp increase in the number of visible satellites. Accordingly, the probability of multiply gross measurements will increase. However, the conventional RAIM methods are difficult to meet the demands of the navigation system. In order to solve the problem of checking and identify multiple gross errors of receiver autonomous integrity monitoring (RAIM), this paper designed full matrix of single point positioning by QR decomposition, and proposed a new RAIM algorithm based on fuzzy clustering analysis with fuzzy c-means (FCM). And on the condition of single or two gross errors, the performance of hard or fuzzy clustering analysis were compared. As the results of the experiments, the fuzzy clustering method based on FCM principle could detect multiple gross error effectively, also achieved the quality control of single point positioning and ensured better reliability results
Tannin-Based Biofoams-A Review
No full textThis review details the development of tannin-based biofoams for fire resistance and acoustic insulation and details the different varieties of these foams that have been developed, from tannin-furanic self-blowing foams to tannin-furanic polyurethanes and finally non-isocyanate tannin-based-carbohydrates polyurethanes (NIPU)
Stabilization and Evaluation of Modified Nanofiber Flour Wood on the Properties of Cement-Baszd Mortar
No full textThe influence of nanofiberwood on the properties of the fresh condition of cement based mortars is not known in detail, despite recent advances in nanocellulose technology and it is related composite materials. Nanofiber wood from industrial waste, produced by high-pressure homogenization, was used as cement partial replacement for cement paste at a content ranging from 0% to 2% by weight of cement. The effect of the nanofiber wood content on the porosity, the compressive strength and the degree of hydration of the cement was investigated. Results have shown an improvement in the compressive strength by more than 50% with 1% of added fiber wood. The chemical modification of nanofibers wood by grafting of chains alkyls in their surfaces can reduce the quantity of water absorbed by the sample. The addition of an anionic additive (SDBS) in the water of mixing improves of more the surface of samples by minimizing the size of pores by emulsion effect, from where the absorption of water reduces. The degree of hydration of the cement has increased with the cellulose content containing nanofibrils. The analyze revealed that the presence of nanofiber wood favored the hydration of the cement by producing more calcium silicate gel and portlandite, probably the main reason for this improvement in compressive strength
Mixed-Ligand Ni(II), Co(II) and Fe(II) Complexes as Catalysts for Esterification of Biomass-Derived Levulinic Acid with Polyol and in Situ Reduction via Hydrogenation with NaBH4
No full textSynthesizing polyol-based ester from biomass feedstocks for the preparation of biolubricant overcomes the dependence on petroleum oil usage. Albeit biomass-derived bio-oil is an alternative for the production of polyol ester, upgrading is essential prior to use as biolubricant. Levulinic acid (LA), obtained from bio-oil was applied for the catalytic esterification with two polyols, e.g., trimethylolpropane (TMP) and pentaerythritol (PE), in the presence of mixed-ligand Ni(II), Co(II), and Fe(II) complexes as catalyst. New mixed-ligand coordination complexes with empirical formula; [Ni(Phe)(Bpy)Cl].H2O, [Co(Phe)(Bpy)Cl].H2O, and [Fe(Phe)(Tyr)Cl].H2O were synthesized by the reaction of ligands [L-phenylalanine (Phe), 4,4'-bipyridine (Bpy), and L-tyrosine (Tyr)] with metal chloride salts and characterized by elemental analysis, magnetic susceptibility, FTIR, TGA/DTA, powder-XRD, and SEM techniques. This study aims to investigate the catalytic activities of the complexes via esterification reaction of levulinic acid with trimethylolpropane and pentaerythritol. In addition, these catalysts were further employed for the in situ hydrogenation of levulinate esters via NaBH4 at room temperature upon refluxing. Indeed, the iron(II) complex was more potential, exhibiting its efficiency as a homogeneous catalyst for esterification-hydrogenation reaction for synthesizing ester-based oils
Dynamic Response of Floating Body Subjected to Underwater Explosion Bubble and Generated Waves with 2D Numerical Model
Get PDFThe low frequency load of an underwater explosion bubble and the generated waves can cause significant rigid motion of a ship that threaten its stability. In order to study the fluid-structure interaction qualitatively, a two-dimensional underwater explosion bubble dynamics model, based on the potential flow theory, is established with a double-vortex model for the doubly connected bubble dynamics simulation, and the bubble shows similar dynamics to that in 3-dimensional domain. A fully nonlinear fluid-structure interaction model is established considering the rigid motion of the floating body using the mode-decomposition method. Convergence test of the model is implemented by simulating the free rolling motion of a floating body in still water. Through the simulation of the interaction of the underwater explosion bubble, the generated waves and the floating body based on the presented model, the influences of the buoyancy parameter and the distance parameter are discussed. It is found that the impact loads on floating body caused by underwater explosion bubble near the free surface can be divided into 3 components: bubble pulsation, jet impact, and slamming load of the generated waves, and the intensity of each component changes nonlinearly with the buoyance parameter. The bubble pulsation load decays with the increase in the horizontal distance. However, the impact load from the generated waves is not monotonous to distance. It increases with the distance within a particular distance threshold, but decays thereafter
A Hierarchy Distributed-Agents Model for Network Risk Evaluation Based on Deep Learning
Get PDFDeep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic, which is especially relevant in Network Intrusion Detection. In this paper, as enlightened by the theory of Deep Learning Neural Networks, Hierarchy Distributed-Agents Model for Network Risk Evaluation, a newly developed model, is proposed. The architecture taken on by the distributed-agents model are given, as well as the approach of analyzing network intrusion detection using Deep Learning, the mechanism of sharing hyper-parameters to improve the efficiency of learning is presented, and the hierarchical evaluative framework for Network Risk Evaluation of the proposed model is built. Furthermore, to examine the proposed model, a series of experiments were conducted in terms of NSL-KDD datasets. The proposed model was able to differentiate between normal and abnormal network activities with an accuracy of 97.60% on NSL-KDD datasets. As the results acquired from the experiment indicate, the model developed in this paper is characterized by high-speed and high-accuracy processing which shall offer a preferable solution with regard to the Risk Evaluation in Network