Physical Sciences and Mathematics Commons^™

Forecasting Locational Marginal Prices In Electricity Markets By Using Artificial Neural Networks, Kim Jay R. Rosano, Allan C. Nerves

Journal of Economics, Management and Agricultural Development

Electricity price forecasting is an important tool used by market players in decision-making and strategizing their participation in the electricity market. In most studies, market-clearing price is forecasted as it gives an aggregated overview of system price. However, locational marginal price (LMP) gives better outlook of the price particular to the customer location in the electrical power grid. This study utilizes Artificial Neural Networks to forecast weekday LMP of generator and load nodes. Various inputs such as historical prices and demand, and temporal indices were used. Using data for selected nodes of the Philippine Wholesale Electricity Spot Market, forecast Mean …

(R1494) Approximate Solutions Of The Telegraph Equation, Ilija Jegdić

Applications and Applied Mathematics: An International Journal (AAM)

In this paper the initial boundary value problems for the linear telegraph equation in one and two space dimensions are considered. To find approximate solutions, a recently proposed optimization-free approach that utilizes artificial neural networks with one hidden layer is used, in which the connecting weights from the input layer to the hidden layer are chosen randomly and the weights from the hidden layer to the output layer are found by solving a system of linear equations. One of the advantages of this method, in comparison to the usual discretization methods for the two-dimensional linear telegraph equation, is that this …

Factors Influencing Intent To Take A Covid-19 Test In The United States, Sheila Rutto

Theses and Dissertations

In 2020, COVID-19 became the first pandemic in the world’s history that brought the entire world to an abrupt and unexpected halt. Since the first reported case of the disease to date, the novel coronavirus has been able to wreak havoc in literary every corner of the globe and left an ever-growing number of unprecedented fatalities. The normal way of life has been disrupted, and the level of uncertainty about the end of this pandemic continues to manifest to many. Due to the urgency to bring this pandemic under control, medical officers have been able to recommend actions that people …

Jmasm 55: Matlab Algorithms And Source Codes Of 'Cbnet' Function For Univariate Time Series Modeling With Neural Networks (Matlab), Cagatay Bal, Serdar Demir

Journal of Modern Applied Statistical Methods

Artificial Neural Networks (ANN) can be designed as a nonparametric tool for time series modeling. MATLAB serves as a powerful environment for ANN modeling. Although Neural Network Time Series Tool (ntstool) is useful for modeling time series, more detailed functions could be more useful in order to get more detailed and comprehensive analysis results. For these purposes, cbnet function with properties such as input lag generator, step-ahead forecaster, trial-error based network selection strategy, alternative network selection with various performance measure and global repetition feature to obtain more alternative network has been developed, and MATLAB algorithms and source codes has been …

Flying Free: A Research Overview Of Deep Learning In Drone Navigation Autonomy, Thomas Lee, Susan Mckeever, Jane Courtney

Articles

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the …

Machine Learning Representations For Optimization Of Process Systems, Logan Watts

Chemical Engineering Undergraduate Honors Theses

The optimal operation of chemical processes provides the foundation for optimization problems to determine the most effective way to operate or design a given process. Chemical processes can be represented as nonlinear systems of equations with decision variables, resulting in a problem that can be solved through nonlinear solvers. The downfalls of nonlinear solvers create the need for improved methods of finding globally optimal solutions to the design or operation of a chemical process. The project will seek to evaluate the use of artificial neural networks to approximate nonlinear systems of equations for the purpose of optimizing chemical processes. The …

Numerical Studies Of Superconductivity And Charge-Density-Waves: Progress On The 2d Holstein Model And A Superconductor-Metal Bilayer, Philip M. Dee

Doctoral Dissertations

The problem of superconductivity has been central in many areas of condensed matter physics for over 100 years. Despite this long history, there is still no theory capable of describing both conventional and unconventional superconductors. Recent experimental observations such as the dilute superconductivity in SrTiO₃ and near room-temperature superconductivity in hydride compounds under extreme pressure have renewed interest in electron-phonon systems. Adding to this is evidence that electron-phonon coupling may play a supporting role in unconventional systems like the cuprates and monolayer FeSe on SrTiO₃.

One way to make sense of these observations is to construct simple …

Implications Of The Quantum Dna Model For Information Sciences, F. Matthew Mihelic

Faculty Publications

The DNA molecule can be modeled as a quantum logic processor, and this model has been supported by pilot research that experimentally demonstrated non-local communication between cells in separated cell cultures. This modeling and pilot research have important implications for information sciences, providing a potential architecture for quantum computing that operates at room temperature and is scalable to millions of qubits, and including the potential for an entanglement communication system based upon the quantum DNA architecture. Such a system could be used to provide non-local quantum key distribution that could not be blocked by any shielding or water depth, would …

Effects Of Covid-19 On Electric Energy Consumption In Turkey And Ann-Basedshort-Term Forecasting, Harun Özbay, Adem Dalcali

Turkish Journal of Electrical Engineering and Computer Sciences

: Due to the coronavirus, millions of people worldwide carry out their work, education, shopping, culture, and entertainment activities from their homes now using the advantages of today's technology. Apart from this, patient care and follow-up are carried out with the help of electronic equipment especially in the institutions where health services are provided. It is important to provide a reliable electricity supply for humanity so that people can perform all these services. In this study, the outlook of energy in Turkey was examined. The current energy consumption and investments were examined. Then, the precautions by the government in the …

Improving Stock Trading Decisions Based On Pattern Recognition Using Machine Learning Technology, Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu, Bingbing Jiang

Information Technology & Decision Sciences Faculty Publications

PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions. Four popular machine learning methods and 11 different features types are applied to all possible combinations of daily patterns to start the pattern recognition schedule. Different time windows from one to ten days are used to detect the prediction effect at different periods. An investment strategy is constructed according to the identified candlestick patterns and suitable time window. We deploy PRML for the forecast of all Chinese market stocks from Jan 1, 2000 until Oct 30, 2020. Among them, the data from …

Distributed Denial Of Service Attack Detection In Cloud Computing Using Hybridextreme Learning Machine, Gopal Singh Kushwah, Virender Ranga

Turkish Journal of Electrical Engineering and Computer Sciences

One of the major security challenges in cloud computing is distributed denial of service (DDoS) attacks. In these attacks, multiple nodes are used to attack the cloud by sending huge traffic. This results in the unavailability of cloud services to legitimate users. In this research paper, a hybrid machine learning-based technique has been proposed to detect these attacks. The proposed technique is implemented by combining the extreme learning machine (ELM) model and the blackhole optimization algorithm. Various experiments have been performed with the help of four benchmark datasets namely, NSL KDD, ISCX IDS 2012, CICIDS2017, and CICDDoS2019, to evaluate the …