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  • Open Access

    ARTICLE

    A DDoS Attack Information Fusion Method Based on CNN for Multi-Element Data

    Jieren Cheng1, 2, Canting Cai1, *, Xiangyan Tang1, Victor S. Sheng3, Wei Guo1, Mengyang Li1

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 131-150, 2020, DOI:10.32604/cmc.2020.06175

    Abstract Traditional distributed denial of service (DDoS) detection methods need a lot of computing resource, and many of them which are based on single element have high missing rate and false alarm rate. In order to solve the problems, this paper proposes a DDoS attack information fusion method based on CNN for multi-element data. Firstly, according to the distribution, concentration and high traffic abruptness of DDoS attacks, this paper defines six features which are respectively obtained from the elements of source IP address, destination IP address, source port, destination port, packet size and the number of IP packets. Then, we propose… More >

  • Open Access

    ARTICLE

    Production Capacity Evaluation of Horizontal Shale Gas Wells in Fuling District

    Jingyi Wang1, Jing Sun1,*, Dehua Liu1, Xiang Zhu1

    FDMP-Fluid Dynamics & Materials Processing, Vol.15, No.5, pp. 613-625, 2019, DOI:10.32604/fdmp.2019.08782

    Abstract One of the important indicators of shale gas reservoir excavation is capacity evaluation, which directly affects whether large-scale shale gas reservoirs can be excavated. Capacity evaluation is the basis of system analysis and dynamic prediction. Therefore, it is particularly important to conduct capacity evaluation studies on shale gas horizontal wells. In order to accurately evaluate the horizontal well productivity of shale gas staged fracturing, this paper uses a new method to evaluate the productivity of Fuling shale gas. The new method is aimed at the dynamic difference of horizontal wells and effectively analyzes the massive data, which are factors affecting… More >

  • Open Access

    ARTICLE

    Inventory of fruit species and ethnobotanical aspects in Sultepec, Mexico State, Mexico

    Rubí-Arriaga M, A González-Huerta, I Martínez-De La Cruz, O Franco-Mora, JF Ramírez-Dávila, JA López-Sandoval, GV Hernández-Flores

    Phyton-International Journal of Experimental Botany, Vol.83, pp. 203-211, 2014, DOI:10.32604/phyton.2014.83.203

    Abstract Sultepec, State of Mexico, located on the central part of Mexico, belongs to the Physiographic province “Sierra Madre del Sur” and to the Subprovince “Depresión del Balsas”. Although it is known for its floristic richness, it lacks an inventory of vascular plants, including the fruit species. The aim of this work was to elaborate a database including family, scientific name, local name, biological form, origin, use, management, production and service of the fruit species. Plants were collected continuously from June 2010 to June 2011. Subsequently, they were determined in the herbarium “Eizi Matuda” (CODAGEM) from the Facultad de Ciencias Agrícolas… More >

  • Open Access

    ARTICLE

    Sensor Fault Detection in Large Sensor Networks using PCA with a Multi-level Search Algorithm

    A. Rama Mohan Rao1, S. Krishna Kumar1, K. Lakshmi1

    Structural Durability & Health Monitoring, Vol.8, No.3, pp. 271-294, 2012, DOI:10.32604/sdhm.2012.008.271

    Abstract Current advancements in structural health monitoring, sensor and sensor network technologies have encouraged using large number of sensor networks in monitoring spatially large civil structures like bridges. Large amount of spatial information obtained from these sensor networks will enhance the reliability in truly assessing the state of the health of the structure. However, if sensors go faulty during operation, the feature extraction techniques embedded into SHM scheme may lead to an erroneous conclusion and often end up with false alarms. Hence it is highly desirable to robustly detect the faulty sensors, isolate and correct the data, if the data at… More >

  • Open Access

    ARTICLE

    A New Method for Maintenance Management Employing Principal Component Analysis

    Fausto Pedro García Márquez1

    Structural Durability & Health Monitoring, Vol.6, No.2, pp. 89-100, 2010, DOI:10.3970/sdhm.2010.006.089

    Abstract This paper presents a simple graphic method for detecting and classifying faults in point mechanisms based on the study of some statistical parameters of the force and current signals of the point machine. Principal Components Analysis (PCA) employed in order to reduce the number of these parameters. PCA is utilised in this paper for modifying the parameter dataset, and reducing the coordinate system by linear transformation. It is then possible to plot the new coordinate system in 2 or 3 dimensions, where the faults can be detected and identified. In this work most of the faults could be detected, but… More >

  • Open Access

    ABSTRACT

    Unsupervised Support Vector Machine Based Principal Component Analysis for Structural Health Monitoring

    Chang Kook Oh1, Hoon Sohn1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.8, No.3, pp. 91-100, 2008, DOI:10.3970/icces.2008.008.091

    Abstract Structural Health Monitoring (SHM) is concerned with identifying damage based on measurements obtained from structures being monitored. For the civil structures exposed to time-varying environmental and operational conditions, it is inevitable that environmental and operational variability produces an adverse effect on the dynamic behaviors of the structures. Since the signals are measured under the influence of these varying conditions, normalizing the data to distinguish the effects of damage from those caused by the environmental and operational variations is important in order to achieve successful structural health monitoring goals. In this paper, kernel principal component analysis (kernel PCA) using unsupervised support… More >

  • Open Access

    ARTICLE

    Oxypropylation of Brazilian Pine-Fruit Shell Evaluated by Principal Component Analysis

    Stephany C. de Rezende1,2, João A. Pinto1,3, Isabel P. Fernandes1,3, Fernanda V. Leimann1,2* and Maria-Filomena Barreiro1,3*

    Journal of Renewable Materials, Vol.6, No.7, pp. 715-723, 2018, DOI:10.32604/JRM.2018.00028

    Abstract Pine-fruit shell (PFS) is a lignocellulosic residue derived from the fruit of Araucaria angustifolia, a coniferous tree native of South America, part of a whole vegetation of the Atlantic Forest, found in the South and Southwest of Brazil. In this work PFS will be characterized and used in the production of PFS-based polyols through oxypropylation. Three series were chosen (PFS/propylene oxide (PO) (w/v, g/mL) of 30/70, 20/80 and 10/90) with four catalyst levels (5%, 10%, 15% and 20%, (w/w, PFS based)). Oxypropylation occurred at moderate conditions of temperature, pressure and time giving rise to liquid polyols with a homopolymer content… More >

  • Open Access

    ARTICLE

    Fingerprint Liveness Detection from Different Fingerprint Materials Using Convolutional Neural Network and Principal Component Analysis

    Chengsheng Yuan1,2,3, Xinting Li3, Q. M. Jonathan Wu3, Jin Li4,5, Xingming Sun1,2

    CMC-Computers, Materials & Continua, Vol.53, No.4, pp. 357-372, 2017, DOI:10.3970/cmc.2017.053.357

    Abstract Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints, which are made of common fingerprint materials, such as silicon, latex, etc. Thus, to protect our privacy, many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint. Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features, but these methods normally destroy or lose spatial information between pixels. Different from existing methods, convolutional neural network (CNN) can generate high-level semantic representations by learning and concatenating low-level edge and shape features from a large amount of labeled… More >

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