Implementation of artificial neural networks based AI concepts to the smart grid

Dimitrijević, Marko; Andrejevic-Stosovic, Miona; Milojkovic, Jelena; Litovski, Vančo

doi:10.2298/fuee1403411d

Cited by 10 publications

(9 citation statements)

References 30 publications

(32 reference statements)

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“…Exploiting the basic topology shown in Figure 1, in [26, 27], better forecasting solutions were suggested for the problem of short prediction base period. This architecture is referred to as feed forward accommodated for prediction (FFAP) and depicted in Figure 2.…”

Section: Ffap Neural Networkmentioning

confidence: 99%

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems

Milic

Milojkovic²,

Marković

et al. 2019

Computational Intelligence and Neuroscience

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Accurate prediction of the short time series with highly irregular behavior is a challenging task found in many areas of modern science. Such data fluctuations are not systematic and hardly predictable. In recent years, artificial neural networks have widely been exploited for those purposes. Although it is possible to model nonlinear behavior of short time series by using ANNs, very often they are not able to handle all events equally well. Therefore, alternative approaches have to be applied. In this study, a new, concurrent, performance-based methodology that combines best ANN topologies in order to decrease the forecasting errors and increase the forecasting certainty is proposed. The proposed approach is verified on three different data sets: the Serbian Gross National Income time series, the municipal traffic flow for a particular observation point, and the daily electric load consumption time series. It is shown that the method can significantly increase the forecasting accuracy of the individual networks, regardless of their topologies, which makes the methodology more applicable. For quantitative comparison of the accuracy of the proposed methodology with that of similar methodologies, a series of additional forecasting experiments that include a state-of-the-art ARIMA modelling and a combination of ANN and linear regression forecasting have been conducted.

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Section: Ffap Neural Networkmentioning

confidence: 99%

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems

Milic

Milojkovic²,

Marković

et al. 2019

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…There is almost unlimited number of possible applications of ICT subsystems within the smart grid. SGs with these infrastructures make it more possible to develop reliable systems through Artificial Intelligence (AI) [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Wind turbine tower detection using feature descriptors and deep learning

Abedini

Bahaghighat²,

Shoyan

2020

FACTA U EE

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Wind Turbine Towers (WTTs) are the main structures of wind farms. They are costly devices that must be thoroughly inspected according to maintenance plans. Today, existence of machine vision techniques along with unmanned aerial vehicles (UAVs) enable fast, easy, and intelligent visual inspection of the structures. Our work is aimed towards developing a visionbased system to perform Nondestructive tests (NDTs) for wind turbines using UAVs. In order to navigate the flying machine toward the wind turbine tower and reliably land on it, the exact position of the wind turbine and its tower must be detected. We employ several strong computer vision approaches such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Brute-Force, Fast Library for Approximate Nearest Neighbors (FLANN) to detect the WTT. Then, in order to increase the reliability of the system, we apply the ResNet, MobileNet, ShuffleNet, EffNet, and SqueezeNet pre-trained classifiers in order to verify whether a detected object is indeed a turbine tower or not.

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“…As a result, this paper aims to devise a new structure that will increase the algebraic degree when compared to the traditional Modulo Addition and increase the difficulty of using the conditional properties. At the same time, the size of the structure is user-defined and flexible, providing the users a scalable security against Algebraic Attack specifically when security is considered as a key requirement during the early stages of systems development [21] [22][23] [24].…”

Section: Introductionmentioning

confidence: 99%

A novel architecture with scalable security having expandable computational complexity for stream ciphers

Siddavaatam

Sedaghat

2017

FACTA U EE

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Stream cipher designs are difficult to implement since they are prone to weaknesses based on usage, with properties being similar to one-time pad besides keystream is subjected to very strict requirements. Contemporary stream cipher designs are highly vulnerable to algebraic cryptanalysis based on linear algebra, in which the inputs and outputs are formulated as multivariate polynomial equations. Solving a nonlinear system of multivariate equations will reduce the complexity, which in turn yields the targeted secret information. Recently, Addition Modulo has been suggested over logic XOR as a mixing operator to guard against such attacks. However, it has been observed that the complexity of Modulo Addition can be drastically decreased with the appropriate formulation of polynomial equations and probabilistic conditions. A new design for Addition Modulo is proposed. The framework for the new design is characterized by user-defined expandable security for stronger encryption and does not impose changes in existing layout for any stream cipher such as SNOW 2.0, SOSEMANUK, CryptMT, Grain Family, etc. The structure of the proposed design is highly scalable, which boosts the algebraic degree and thwarts the probabilistic conditions by maintaining the original hardware complexity without changing the integrity of the Addition Modulo.

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Implementation of artificial neural networks based AI concepts to the smart grid

Cited by 10 publications

References 30 publications

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems

Wind turbine tower detection using feature descriptors and deep learning

A novel architecture with scalable security having expandable computational complexity for stream ciphers

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