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MSICST: Multiple-Scenario Industrial Control System Testbed for Security Research
Get PDFA security testbed is an important aspect of Industrial Control System (ICS) security research. However, existing testbeds still have many problems in that they cannot fully simulate enterprise networks and ICS attacks. This paper presents a Multiple-Scenario Industrial Control System Testbed (MSICST), a hardware-in-the-loop ICS testbed for security research. The testbed contains four typical process scenarios: thermal power plant, rail transit, smart grid, and intelligent manufacturing. We use a combination of actual physical equipment and software simulations to build the process scenario sand table and use real hardware and software to build the control systems, demilitarized zone, and enterprise zone networks. According to the ICS cyber kill chain, the attacker is modeled, and two typical attack scenarios are constructed in the testbed. Through research into this security solution, whitelist-based host protection and a new Intrusion Detection System (IDS) are proposed and tested
Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach
Get PDFWind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however, blade get damaged due to wind gusts, bad weather conditions, unpredictable aerodynamic forces, lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade. It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine. In this paper, a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades. The models are built based on computing the vibration response of the blade when it is excited using piezoelectric accelerometer. The statistical, histogram and ARMA methods for each algorithm were compared essentially to suggest a better model for the identification and localization of crack on wind turbine blade
Seismic Vulnerability Analysis of Single-Story Reinforced Concrete Industrial Buildings with Seismic Fortification
Get PDFAs there is a lack of earthquake damage data for factory buildings with seismic fortifications in China, seismic vulnerability analysis was performed by numerical simulation in this paper. The earthquake-structure analysis model was developed with considering the influence of uncertainties of the ground motion and structural model parameters. The small-size sampling was conducted based on the Latin hypercube sampling and orthogonal design methods. Using nonlinear analysis, the seismic vulnerability curves and damage probability matrix with various seismic fortification intensities (SFI) were obtained. The seismic capacity of the factory building was then evaluated. The results showed that, with different designs at different SFIs, the factory building could consistently achieve the three seismic fortification objectives. For the studied factory buildings with the SFI of 6, they satisfied the seismic fortification requirements of “no damage in moderate earthquakes, mendable in strong earthquakes”; for those buildings with SFIs of 7 and 8, the requirement of “no collapsing in super strong earthquakes” was generally met; while for those with SFIs of 9, the requirement of “mendable in moderate earthquakes” was almost satisfied. The results showed factory buildings designed with low SFIs are better at achieving the seismic fortification objectives than those designed with high SFIs
Nanostructural Evolution of Sugarcane Rind and Pith Submitted to Hydrothermal Pretreatments
Get PDFLignocellulose conversion into cellulosic ethanol and coproducts starts with a pretreatment step. Most current industrial plants of cellulosic ethanol use thermochemical pretreatments under hydrothermal conditions, with or without addition of acid catalyst. Such pretreatments modify biomass chemistry and morphology, particularly at the nanoscale. In this work, we use X-ray diffraction, dynamic vapor sorption and calorimetric thermoporometry to investigate the biomass nanostructural changes promoted by hydrothermal conditions. We compare and differentiate the rind and pith fractions of sugarcane stalks in order to contribute to the understanding of rind-pith contrasting recalcitrance. Moreover, for both cane fractions our results point consistently to cellulose co-crystallization, lignin aggregation, and opening of nanoscale pores as the main nanostructural phenomena occurring during hydrothermal treatments
A Survey on Machine Learning Algorithms in Little-Labeled Data for Motor Imagery-Based Brain-Computer Interfaces
No full textThe Brain-Computer Interfaces (BCIs) had been proposed and used in therapeutics for decades. However, the need of time-consuming calibration phase and the lack of robustness, which are caused by little-labeled data, are restricting the advance and application of BCI, especially for the BCI based on motor imagery (MI). In this paper, we reviewed the recent development in the machine learning algorithm used in the MI-based BCI, which may provide potential solutions for addressing the issue. We classified these algorithms into two categories, namely, and enhancing the representation and expanding the training set. Specifically, these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands, regularization, and so on. The methods of expanding the training dataset include approaches of transfer learning (session to session transfer, subject to subject transfer) and generating artificial EEG data. The result of these techniques showed the resolution of the challenges to some extent. As a developing research area, the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research
Secure Model of Medical Data Sharing for Complex Scenarios
No full textIn order to secure the massive heterogeneous medical data for the complex scenarios and improve the information sharing efficiency in healthcare system, a distributed medical data ledger model (DMDL) is proposed in this paper. This DMDL model has adopted the blockchain technology including the function decoupling, the distributed consensus, smart contract as well as multi-channel communication structure of consortium blockchain. The DMDL model not only has high adaptability, but also meets the requirements of the medical treatment processes which generally involve multi-entities, highly private information and secure transaction. The steps for processing the medical data are also introduced. Additionally, the methods for the definition and application of the DMDL model are presented for three specific medical scenarios, i.e., the management of the heterogeneous data, copyright protection for medical data and the secure utilization of sensitive data. The advantage of the proposed DMDL model is demonstrated by comparing with the models which are being currently adopted in healthcare system
An Augmented IB Method & Analysis for Elliptic BVP on Irregular Domains
No full textThe immersed boundary method is well-known, popular, and has had vast areas of applications due to its simplicity and robustness even though it is only first order accurate near the interface. In this paper, an immersed boundary-augmented method has been developed for linear elliptic boundary value problems on arbitrary domains (exterior or interior) with a Dirichlet boundary condition. The new method inherits the simplicity, robustness, and first order convergence of the IB method but also provides asymptotic first order convergence of partial derivatives. Numerical examples are provided to confirm the analysis
A Novel Image Categorization Strategy Based on Salp Swarm Algorithm to Enhance Efficiency of MRI Images
No full textThe main target of this paper is presentation of an efficient method for MRI images classification so that it can be used to diagnose patients and non-patients. Image classification is one of the prominent subset topics of machine learning and data mining that the most important image technique is the auto-categorization of images. MRI images with high resolution and appropriate accuracy allow physicians to decide on the diagnosis of various diseases and treat them. The auto categorization of MRI images toward diagnosing brain diseases has been being used to accurately diagnose hospitals, clinics, physicians and medical research centers. In this paper, an effective method is proposed for categorizing MRI images, which emphasizes the classification stage. In this method, images have been firstly collected and tagged, and then the discrete wavelet transform method has been implemented to extract the relevant properties. All the ready features in a matrix will be subsequently held, and PCA method has been applied to reduce the features dimension. Furthermore, a new model using support vector machine classifier with radial basis function kernel i.e. SVM+RBF has been performed. The SVM Algorithm must bimanually initialized, while, these values have been automatically entered into the SVM classifier by Salp Swarm Algorithm (SSA): Due to high performance of SSA in fast and accurate solution of nonlinear problem as compared to other optimization algorithms, it has been applied to optimally solve the designed problem. Finally, after applying the optimal parameters and SVM classification training, the test data has been utilized and evaluated. The results have transparently suggested the effectiveness of the proposed method in the Accuracy criteria with 0.9833, the Sensitivity with 1, Specificity with 0.9818 and Error with 0.0167 in best iteration as compared to the conventional SVM method
3D Web Reconstruction of a Fibrous Filter Using Sequential Multi-Focus Images
No full textA fibrous filtering material is a kind of fiber assembly whose structure exhibits a three-dimensional (3D) network with dense microscopic open channels. The geometrical/morphological attributes, such as orientations, curvatures and compactness, of fibers in the network is the key to the filtration performance of the material. However, most of the previous studies were based on materials’ 2D micro-images, which were unable to accurately measure these important 3D features of a filter’s structure. In this paper, we present an imaging method to reconstruct the 3D structure of a fibrous filter from its optical microscopic images. Firstly, a series of images of the fiber assembly were captured at different depth layers as the stage moved vertically. Then a fusion image was established by extracting fiber edges from each layered image. Thirdly, the 3D coordinates of the fiber edges were determined using the sharpness/clarity of each edge pixel in the layered images. Finally, the 3D structure the fiber system was reconstructed through distance transformation based on the locations of fiber edge
LNA Design for Future S Band Satellite Navigation and 4G LTE Applications
No full textA good design of LNA for S band satellite navigation receivers and 4G LTE wireless communication system has been implemented in this paper. Due to increased congestion in the present L band, the S Band frequency from 2483.5-2500 MHz has been allocated for the future satellite navigation systems. For this purpose ATF-34143 amplifier (pHEMT) having high electron mobility and fast switching response has been chosen due to its very low Noise Figure (NF). The amplifier has been designed having bandwidth of 0.8 GHz from 1.8-2.6 GHz. Because of the large bandwidth, the amplifier could serve many wireless communication applications including 4G LTE mobile communication at 2.1 GHz. The design was implemented using the micro strip technology offering extremely low noise figure of 0.312 dB and 0.377 dB for 2.1 GHz and 2.49 GHz respectively. The gain of the amplifier was low and found to be 10.281 dB and 9.175 dB. For the purpose of increasing the gain of an amplifier, the proposed LNA design was then optimized by using Wilkinson Power Divider (WPD). The Balanced LNA design using WPD offered very low noise figure of 0.422 dB and 0.532 dB respectively and the gain was considerably increased and was found to be 20.087 dB and 17.832 dB respectively against 2.1 GHz and 2.49 GHz. Simulations and measurements were taken in Agilent Advanced Design System (ADS) software. The suggested LNA can be used for a variety of wireless communications applications including the future S band satellite navigation systems