• Title/Summary/Keyword: Artificial neural Networks (ANN)

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A New Approach to Short-term Price Forecast Strategy with an Artificial Neural Network Approach: Application to the Nord Pool

  • Kim, Mun-Kyeom
    • Journal of Electrical Engineering and Technology
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    • v.10 no.4
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    • pp.1480-1491
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    • 2015
  • In new deregulated electricity market, short-term price forecasting is key information for all market players. A better forecast of market-clearing price (MCP) helps market participants to strategically set up their bidding strategies for energy markets in the short-term. This paper presents a new prediction strategy to improve the need for more accurate short-term price forecasting tool at spot market using an artificial neural networks (ANNs). To build the forecasting ANN model, a three-layered feedforward neural network trained by the improved Levenberg-marquardt (LM) algorithm is used to forecast the locational marginal prices (LMPs). To accurately predict LMPs, actual power generation and load are considered as the input sets, and then the difference is used to predict price differences in the spot market. The proposed ANN model generalizes the relationship between the LMP in each area and the unconstrained MCP during the same period of time. The LMP calculation is iterated so that the capacity between the areas is maximized and the mechanism itself helps to relieve grid congestion. The addition of flow between the areas gives the LMPs a new equilibrium point, which is balanced when taking the transfer capacity into account, LMP forecasting is then possible. The proposed forecasting strategy is tested on the spot market of the Nord Pool. The validity, the efficiency, and effectiveness of the proposed approach are shown by comparing with time-series models

An investigation on the mortars containing blended cement subjected to elevated temperatures using Artificial Neural Network (ANN) models

  • Ramezanianpour, A.A.;Kamel, M.E.;Kazemian, A.;Ghiasvand, E.;Shokrani, H.;Bakhshi, N.
    • Computers and Concrete
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    • v.10 no.6
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    • pp.649-662
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    • 2012
  • This paper presents the results of an investigation on the compressive strength and weight loss of mortars containing three types of fillers as cement replacements; Limestone Filler (LF), Silica Fume (SF) and Trass (TR), subjected to elevated temperatures including $400^{\circ}C$, $600^{\circ}C$, $800^{\circ}C$ and $1000^{\circ}C$. Results indicate that addition of TR to blended cements, compared to SF addition, leads to higher compressive strength and lower weight loss at elevated temperatures. In order to model the influence of the different parameters on the compressive strength and the weight loss of specimens, artificial neural networks (ANNs) were adopted. Different diagrams were plotted based on the predictions of the most accurate networks to study the effects of temperature, different fillers and cement content on the target properties. In addition to the impressive RMSE and $R^2$ values of the best networks, the data used as the input for the prediction plots were chosen within the range of the data introduced to the networks in the training phase. Therefore, the prediction plots could be considered reliable to perform the parametric study.

A Study on the Multi-Level Artificial Neural Networks Using Genetic Algorithm for Preliminary Structural Design (예비 구조설계를 위한 유전알고리즘을 이용한 다단계 인공신경망에 관한 연구)

  • Choi, Byoung Han
    • Journal of Korean Society of Steel Construction
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    • v.16 no.4 s.71
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    • pp.443-452
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    • 2004
  • Recently, the Artificial Neural Network(ANN) which can organize complex non-linear problems by effectively applying the parallel computational model that is similar to the human brain, was adopted in the wide department of technology and resulted in many successful applications. In this study, a more appropriate formal method is suggested for the preliminary structural design stage controlled merely by the designer's experience and intuition. To do so, this study proposes a multi-level ANN according to the general progressive structural design procedure, using Back-Propagation Algorithm (BP) and Genetic Algorithm (GA) for the ANN learning. The preliminary structural design of cable-stayed bridges was applied to illustrate the applicability of the study formulated as stated above, and the results of two different learning methods were compared.

Numerical evaluation of gamma radiation monitoring

  • Rezaei, Mohsen;Ashoor, Mansour;Sarkhosh, Leila
    • Nuclear Engineering and Technology
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    • v.51 no.3
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    • pp.807-817
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    • 2019
  • Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

Intelligent & Predictive Security Deployment in IOT Environments

  • Abdul ghani, ansari;Irfana, Memon;Fayyaz, Ahmed;Majid Hussain, Memon;Kelash, Kanwar;fareed, Jokhio
    • International Journal of Computer Science & Network Security
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    • v.22 no.12
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    • pp.185-196
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    • 2022
  • The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

Development of Flood Runoff Forecasting System by using Artificial Neural Networks - Development & Application of GUI_FFS - (인공신경망 이론을 이용한 홍수유출 예측 시스템 개발 - GUI_FFS 개발 및 적용 -)

  • Park, Sung-Chun;Oh, Chang-Ryol;Kim, Dong-Ryeol;Jin, Young-Hoon
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.2B
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    • pp.145-152
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    • 2006
  • In the present study, a nonlinear model of rainfall-runoff process using Artficial Neural networks(ANNs) which have no consideration on the physical parameter for the basin was developed at Naju station which is the main stream of Yeongsan-river, and Sunam station which is the main stream of Hwangryong-river. The result from the model of ANN_NJ_9 at the Naju station revealed the best result of the rainfall-runoff process, while the model of ANN_SA_9 for the Sunam station. Also, GUI_FFS developed in the research showed the $R^2$ of more than 0.98 between the observed and predicted values using the rainfall and runoff in the respective stations. Therefore, the GUI_FFS might be expected that it can play a role for the high reliability to operate and manage the water resources and the design of river plan more efficiently in the future.

ANN-based Adaptive Distance Measurement Using Beacon (비콘을 사용한 ANN기반 적응형 거리 측정)

  • Noh, Jiwoo;Kim, Taeyeong;Kim, Suntae;Lee, Jeong-Hyu;Yoo, Hee-Kyung;Kang, Yungu
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.18 no.5
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    • pp.147-153
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    • 2018
  • Beacon enables one to measure distance indoors based on low-power Bluetooth low energy (BLE) technology, while GPS (Global Positioning System) only can be used outdoors. In measuring indoor distance using Beacon, RSSI (Received Signal Strength Indication) is considered as the one of the key factors, however, it is influenced by various environmental factors so that it causes the huge gap between the estimated distance and the real. In order to handle this issue, we propose the adaptive ANN (Artificial Neural Network) based approach to measuring the exact distance using Beacon. First, we has carried out the preprocessing of the RSSI signals by applying the extended Kalman filter and the signal stabilization filter into decreasing the noise. Then, we suggest the multi-layered ANNs, each of which layer is learned by specific training data sets. The results showed an average error of 0.67m, a precision of 0.78.

Prediction of UCS and STS of Kaolin clay stabilized with supplementary cementitious material using ANN and MLR

  • Kumar, Arvind;Rupali, S.
    • Advances in Computational Design
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    • v.5 no.2
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    • pp.195-207
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    • 2020
  • The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.

Training Method of Artificial Neural Networks for Implementation of Automatic Composition Systems (자동작곡시스템 구현을 위한 인공신경망의 학습방법)

  • Cho, Jae-Min;Ryu, Eun Mi;Oh, Jin-Woo;Jung, Sung Hoon
    • KIPS Transactions on Software and Data Engineering
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    • v.3 no.8
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    • pp.315-320
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    • 2014
  • Composition is a creative activity of a composer in order to express his or her emotion into melody based on their experience. However, it is very hard to implement an automatic composition program whose composition process is the same as the composer. On the basis that the creative activity is possible from the imitation we propose a method to implement an automatic composition system using the learning capability of ANN(Artificial Neural Networks). First, we devise a method to convert a melody into time series that ANN can train and then another method to learn the repeated melody with melody bar for correct training of ANN. After training of the time series to ANN, we feed a new time series into the ANN, then the ANN produces a full new time series which is converted a new melody. But post processing is necessary because the produced melody does not fit to the tempo and harmony of music theory. In this paper, we applied a tempo post processing using tempo post processing program, but the harmony post processing is done by human because it is difficult to implement. We will realize the harmony post processing program as a further work.

Application of artificial neural networks for dynamic analysis of building frames

  • Joshi, Shardul G.;Londhe, Shreenivas N.;Kwatra, Naveen
    • Computers and Concrete
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    • v.13 no.6
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    • pp.765-780
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    • 2014
  • Many building codes use the empirical equation to determine fundamental period of vibration where in effect of length, width and the stiffness of the building is not explicitly accounted for. In the present study, ANN models are developed in three categories, varying the number of input parameters in each category. Input parameters are chosen to represent mass, stiffness and geometry of the buildings indirectly. Total numbers of 206 buildings are analyzed out of which, data set of 142 buildings is used to develop these models. It is demonstrated through developed ANN models that geometry of the building and the sizes of the columns are significant parameters in the dynamic analysis of building frames. The testing dataset of these three models is used to obtain the empirical relationship between the height of the building and fundamental period of vibration and compared with the similar equations proposed by other researchers. Experiments are conducted on Mild Steel frames using uniaxial shake table. It is seen that the values obtained through the ANN models are close to the experimental values. The validity of ANN technique is verified by experimental values.