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31,112 full-text articles. Page 7 of 895.

Fingerprinting Jpegs With Optimised Huffman Tables, Sean McKeown, Gordon Russell, Petra Leimich 2018 Edinburgh Napier University

Fingerprinting Jpegs With Optimised Huffman Tables, Sean Mckeown, Gordon Russell, Petra Leimich

Journal of Digital Forensics, Security and Law

A common task in digital forensics investigations is to identify known contraband images. This is typically achieved by calculating a cryptographic digest, using hashing algorithms such as SHA256, for each image on a given medium, and comparing individual digests with a database of known contraband. However, the large capacities of modern storage media and time pressures placed on forensics examiners necessitates the development of more efficient processing methods. This work describes a technique for fingerprinting JPEGs with optimised Huffman tables which requires only the image header to be present on the media. Such fingerprints are shown to be robust across ...


A New Framework For Securing, Extracting And Analyzing Big Forensic Data, Hitesh Sachdev, hayden wimmer, Lei Chen, Carl Rebman 2018 Georgia Southern University

A New Framework For Securing, Extracting And Analyzing Big Forensic Data, Hitesh Sachdev, Hayden Wimmer, Lei Chen, Carl Rebman

Journal of Digital Forensics, Security and Law

Finding new methods to investigate criminal activities, behaviors, and responsibilities has always been a challenge for forensic research. Advances in big data, technology, and increased capabilities of smartphones has contributed to the demand for modern techniques of examination. Smartphones are ubiquitous, transformative, and have become a goldmine for forensics research. Given the right tools and research methods investigating agencies can help crack almost any illegal activity using smartphones. This paper focuses on conducting forensic analysis in exposing a terrorist or criminal network and introduces a new Big Forensic Data Framework model where different technologies of Hadoop and EnCase software are ...


A Bit Like Cash: Understanding Cash-For-Bitcoin Transactions Through Individual Vendors, Stephanie J. Robberson, Mark R. McCoy 2018 University of Central Oklahoma

A Bit Like Cash: Understanding Cash-For-Bitcoin Transactions Through Individual Vendors, Stephanie J. Robberson, Mark R. Mccoy

Journal of Digital Forensics, Security and Law

As technology improves and economies become more globalized, the concept of currency has evolved. Bitcoin, a cryptographic digital currency, has been embraced as a secure and convenient type of money. Due to its security and privacy for the user, Bitcoin is a good tool for conducting criminal trades. The Financial Crimes Enforcement Network (FinCEN) has regulations in place to make identification information of Bitcoin purchasers accessible to law enforcement, but enforcing these rules with cash-for-Bitcoin traders is difficult. This study surveyed cash-for-Bitcoin vendors in Oklahoma, Texas, Arkansas, Missouri, Kansas, Colorado, and New Mexico to determine personal demographic information, knowledge of ...


Applied Cognitive Computing And Artificial Intelligence: How Machines Learn To “Read” The Law, Vern R. Walker 2018 Maurice A. Deane School of Law at Hofstra University

Applied Cognitive Computing And Artificial Intelligence: How Machines Learn To “Read” The Law, Vern R. Walker

Legal Tech Boot Camp

No abstract provided.


Introducing The Fractional Differentiation For Clinical Data-Justified Prostate Cancer Modelling Under Iad Therapy, Ozlem Ozturk Mizrak 2018 Illinois State University

Introducing The Fractional Differentiation For Clinical Data-Justified Prostate Cancer Modelling Under Iad Therapy, Ozlem Ozturk Mizrak

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


Issues In Reproducible Simulation Research, Ben G. Fitzpatrick 2018 Loyola Marymount University

Issues In Reproducible Simulation Research, Ben G. Fitzpatrick

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


Mathematical Modeling And Simulation With Deep Learning Methods Of Cancer Growth For Patient-Specific Therapy, Vishal Kobla, Joshua P. Smith, Pranav Unni, Padmanabhan Seshaiyer 2018 Academies of Loudoun

Mathematical Modeling And Simulation With Deep Learning Methods Of Cancer Growth For Patient-Specific Therapy, Vishal Kobla, Joshua P. Smith, Pranav Unni, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


The Subject Librarian Newsletter, Engineering And Computer Science, Spring 2017, Buenaventura "Ven" Basco 2018 University of Central Florida

The Subject Librarian Newsletter, Engineering And Computer Science, Spring 2017, Buenaventura "Ven" Basco

Buenaventura "Ven" Basco

No abstract provided.


The Subject Librarian Newsletter, Engineering And Computer Science, Spring 2016, Ven Basco 2018 University of Central Florida

The Subject Librarian Newsletter, Engineering And Computer Science, Spring 2016, Ven Basco

Buenaventura "Ven" Basco

No abstract provided.


The Subject Librarian Newsletter, Engineering And Computer Science, Fall 2016, Buenaventura "Ven" Basco 2018 University of Central Florida

The Subject Librarian Newsletter, Engineering And Computer Science, Fall 2016, Buenaventura "Ven" Basco

Buenaventura "Ven" Basco

No abstract provided.


The Subject Librarian Newsletter, Engineering And Computer Science, Fall 2015, Ven Basco 2018 University of Central Florida

The Subject Librarian Newsletter, Engineering And Computer Science, Fall 2015, Ven Basco

Buenaventura "Ven" Basco

No abstract provided.


Two Neural Network Based Decentralized Controller Designs For Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, David A. Cartes 2018 Missouri University of Science and Technology

Two Neural Network Based Decentralized Controller Designs For Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, David A. Cartes

Donald C. Wunsch

This paper presents two neural network (NN) based decentralized controller designs for large scale power systems' generators, one is for the excitation control and the other is for the steam valve control. Though the control signals are calculated using local signals only, the transient and overall system stabilities can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system and the interconnection terms, thus the requirements for exact system parameters are released. Simulation studies with a three machine power system demonstrate the effectiveness of the proposed controller designs.


Preface, Gennady Fridman, Jeremy Levesley, Ivan Tyukin, Donald C. Wunsch 2018 Missouri University of Science and Technology

Preface, Gennady Fridman, Jeremy Levesley, Ivan Tyukin, Donald C. Wunsch

Donald C. Wunsch

In August 2014 a conference on “Model reduction across disciplines” was held in Leicester, UK. As a scientific field, model reduction is an important part of mathematical modelling and data analysis with very wide areas of applications. The main scientific goal of the conference was to facilitate interdisciplinary discussion of model reduction and coarse-graining methodologies in order to reveal their general mathematical nature. This time, however, the conference had an additional personal and more profound mission – it was dedicated to the 60th birthday of Professor Alexander Gorban (albeit with some delay) whose fantastic achievements in applying model reduction techniques to ...


Neural Network Stabilizing Control Of Single Machine Power System With Control Limits, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow 2018 Missouri University of Science and Technology

Neural Network Stabilizing Control Of Single Machine Power System With Control Limits, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow

Donald C. Wunsch

Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. This paper proposes a stable neural network (NN) controller for the stabilization of a single machine infinite bus power system. In the power system control literature, simplified analytical models are used to represent the power system and the controller designs are not based on rigorous stability analysis. This work overcomes the two major problems by using an accurate analytical model for controller development and presents the closed-loop stability analysis. The NN is used to approximate the ...


Neural Network Based Decentralized Controls Of Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes 2018 Missouri University of Science and Technology

Neural Network Based Decentralized Controls Of Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes

Donald C. Wunsch

This paper presents a suite of neural network (NN) based decentralized controller designs for large scale power systems' generators, one is for the excitation control and the other is for the steam valve control. Though the control inputs are calculated using local signals, the transient and overall system stability can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system dynamics and the inter-connection terms, thus the requirements for exact system parameters are relaxed. Simulation studies with a three-machine power system demonstrate the effectiveness of the proposed controller designs.


Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani 2018 Missouri University of Science and Technology

Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani

Donald C. Wunsch

This paper presents a neural network (NN) based decentralized excitation controller design for large scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem controllers can be guaranteed. NNs are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded (UUB). Simulation results with a 3-machine power system demonstrate ...


Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch 2018 Missouri University of Science and Technology

Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch

Donald C. Wunsch

This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data. to forecast wind, a new method for wind time series data forecasting is developed based on the extreme learning machine (ELM). the clustering results improve the accuracy of the proposed method of wind ...


Feedback Linearization Based Power System Stabilizer Design With Control Limits, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Jagannathan Sarangapani 2018 Missouri University of Science and Technology

Feedback Linearization Based Power System Stabilizer Design With Control Limits, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Jagannathan Sarangapani

Donald C. Wunsch

In power system controls, simplified analytical models are used to represent the dynamics of power system and controller designs are not rigorous with no stability analysis. One reason is because the power systems are complex nonlinear systems which pose difficulty for analysis. This paper presents a feedback linearization based power system stabilizer design for a single machine infinite bus power system. Since practical operating conditions require the magnitude of control signal to be within certain limits, the stability of the control system under control limits is also analyzed. Simulation results under different kinds of operating conditions show that the controller ...


Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow 2018 Missouri University of Science and Technology

Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow

Donald C. Wunsch

A novel decentralized neural network (DNN) controller is proposed for a class of large-scale nonlinear systems with unknown interconnections. The objective is to design a DNN for a class of large-scale systems which do not satisfy the matching condition requirement. The NNs are used to approximate the unknown subsystem dynamics and the interconnections. The DNN is designed using the back stepping methodology with only local signals for feedback. All of the signals in the closed loop (system states and weights estimation errors) are guaranteed to be uniformly ultimately bounded and eventually converge to a compact set.


Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer, Wenxin Liu, Ganesh K. Venayagamoorthy, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes 2018 Missouri University of Science and Technology

Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer, Wenxin Liu, Ganesh K. Venayagamoorthy, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes

Donald C. Wunsch

Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of ...


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