Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm
University of Tabriz（大不里士大学）；Urmia University（乌尔米耶大学）；Yazd University（亚兹德大学）；Obuda University（欧布达大学）；Duy Tan University（岘港大学（越南））；Ton Duc Thang University（东德大学）
Saeed Samadianfard, Sajjad Hashemi, Katayoun Kargar, Mojtaba Izadyar, Ali Mostafaeipour, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband
Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. Due to neglecting of requirement and significance of data preprocessing and disregarding the inadequacy of using a single predicting model, many traditional models have poor performance in wind speed prediction. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh, and Deylaman) to increase the accuracy of the subsequent hybrid model. The capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP model without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the standalone MLP model. The hybrid model had acceptable performances with lower amounts of the RMSE, SI and RE parameters and higher values of NSE, WI, and KGE parameters. It was concluded that the WOA optimization algorithm can improve the prediction accuracy of MLP model and may be recommended for accurate wind speed prediction.
Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent hazards in the surrounding environment. Many existing lane change prediction models take as input lateral or angle information and make short-term (< 5 seconds) maneuver predictions. In this study, we propose a longer-term (5~10 seconds) prediction model without any lateral or angle information. Three prediction models are introduced, including a logistic regression model, a multilayer perceptron (MLP) model, and a r…
The new type of Coronavirus disease called COVID-19 continues to spread quite rapidly. Although it shows some specific symptoms, this disease, which can show different symptoms in almost every individual, has caused hundreds of thousands of patients to die. Although healthcare professionals work hard to prevent further loss of life, the rate of disease spread is very high. For this reason, the help of computer aided diagnosis (CAD) and artificial intelligence (AI) algorithms is vital. In this study, a method based on optimization of convolutional neural network (CNN) architecture, which is the…
University of New Brunswick（新不伦瑞克大学）；University of Victoria（维多利亚大学）；National Research Council Canada（加拿大国家研究委员会）
Critically endangered species in Canadian North Atlantic waters are systematically surveyed to estimate species populations which influence governing policies. Due to its impact on policy, population accuracy is important. This paper lays the foundation towards a data-driven glare modelling system, which will allow surveyors to preemptively minimize glare. Surveyors use a detection function to estimate megafauna populations which are not explicitly seen. A goal of the research is to maximize useful imagery collected, to that end we will use our glare model to predict glare and optimize for gla…
Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring
Department of Computer Science, University College London（计算机科学系，伦敦大学学院）；Department of Statistical Science, University College London（统计科学系，伦敦大学学院）；Department of Statistics, Athens University of Economics and Business（统计系，雅典经济与商业大学）
University of Oslo（奥斯陆大学）；Institute for Energy Systems (IFE)（能源系统研究所）
This study focuses on multi-step spatio-temporal wind speed forecasting for the Norwegian continental shelf. The study aims to leverage spatial dependencies through the relative physical location of different measurement stations to improve local wind forecasts. Our multi-step forecasting models produce either 10-minute, 1- or 4-hour forecasts, with 10-minute resolution, meaning that the models produce more informative time series for predicted future trends. A graph neural network (GNN) architecture was used to extract spatial dependencies, with different update functions to learn temporal co…
2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China（人工智能学院，河北工业大学，天津，中国）；State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China（电力设备可靠性与智能化国家重点实验室，河北工业大学，天津，中国）；Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China（河北省大数据计算重点实验室，河北工业大学，天津，中国）
Automated Classification of Plasma Regions at Mars Using Machine Learning
Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA（波士顿大学电气与计算机工程系，波士顿，马萨诸塞州，02215，美国）；Center for Space Physics, Boston University, Boston, MA 02215, USA（波士顿大学空间物理中心，波士顿，马萨诸塞州，02215，美国）；School of Natural Sciences, Institute for Advanced Study, Princeton, NJ 08540, USA（普林斯顿大学高级研究院自然科学学院，普林斯顿，新泽西州，08540，美国）；Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA（波士顿大学数学与统计学系，波士顿，马萨诸塞州，02215，美国）
Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics. Recently, a few deep learning models have surpassed the traditional window based multilayer perceptron. Taking inspiration from the image classification domain we propose a deep convolutional neural network architecture, MUST-CNN, to predict protein properties. This architecture uses a novel multilayer shift-and-stitch (MUST) technique to generate fully dense per-position predictions on protein sequences. Our model is significantly sim…
The study in this paper presents a one-dimensional convolutional neural network (1DCNN) model, designed for the automated detection of obstructive Sleep Apnoea (OSA) captured from single-channel electrocardiogram (ECG) signals. The system provides mechanisms in clinical practice that help diagnose patients suffering with OSA. Using the state-of-the-art in 1DCNNs, a model is constructed using convolutional, max pooling layers and a fully connected Multilayer Perceptron (MLP) consisting of a hidden layer and SoftMax output for classification. The 1DCNN extracts prominent features, which are used…
Enhancing Robustness of Asynchronous EEG-Based Movement Prediction using Classifier Ensembles
Robotics Innovation Center（机器人创新中心）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Institute of Medical Technology Systems（医疗技术系统研究所）；University of Duisburg-Essen（杜伊斯堡- Essen大学）
Wind power is seeing a strong growth around the world. At the same time, shrinking profit margins in the energy markets let wind farm managers explore options for cost reductions in the turbine operation and maintenance. Sensor-based condition monitoring facilitates remote diagnostics of turbine subsystems, enabling faster responses when unforeseen maintenance is required. Condition monitoring with data from the turbines' supervisory control and data acquisition (SCADA) systems was proposed and SCADA-based fault detection and diagnosis approaches introduced based on single-task normal operatio…
End to End Autoencoder MLP Framework for Sepsis Prediction
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；State Key Laboratory of Public Big Data, Guizhou University（贵州大学公共大数据国家重点实验室）
A Machine Learning Framework for Handling Unreliable Absence Label and Class Imbalance for Marine Stinger Beaching Prediction
Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics（过渡人工智能研究组与数学与统计学院）；School of Mathematics and Statistics（数学与统计学院）；Center for Marine Science and Innovation（海洋科学与创新中心）
Effective Series Decomposition and Components Learning for Time Series Generation
School of Engineering and Applied Science, University of Pennsylvania（工程与应用科学学院，宾夕法尼亚大学）；Viterbi School of Engineering, University of Southern California（工程学院，南加州大学）
Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction
College of Computer and Information, Hohai University（河海大学计算机与信息学院）；Universitas Ahmad Dahlan（艾哈迈德·达兰大学）；Faculty of Engineering, Universitas Negeri Malang（马朗国立大学工程学院）
University of Oslo（奥斯陆大学）；Institute for Energy Systems（能源系统研究所）
With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and have found wide success in predicting wake losses and expected power production. This paper proposes a modular framework for attention-based graph neural networks (GNN), where attention can be applied to any desired component of a graph block. The results show that the model significantly outperforms a multilayer perceptron (MLP) and a bidirectional …
Task embeddings in multi-layer perceptrons for multi-task learning and inductive transfer learning in renewable power forecasts have recently been introduced. In many cases, this approach improves the forecast error and reduces the required training data. However, it does not take the seasonal influences in power forecasts within a day into account, i.e., the diurnal cycle. Therefore, we extended this idea to temporal convolutional networks to consider those seasonalities. We propose transforming the embedding space, which contains the latent similarities between tasks, through convolution and…
Seoul National University（首尔大学）；Korea Institute of Industrial Technology（韩国生产技术研究院）
The inverse approach is computationally efficient in aerodynamic design as the desired target performance distribution is prespecified. However, it has some significant limitations that prevent it from achieving full efficiency. First, the iterative procedure should be repeated whenever the specified target distribution changes. Target distribution optimization can be performed to clarify the ambiguity in specifying this distribution, but several additional problems arise in this process such as loss of the representation capacity due to parameterization of the distribution, excessive constrai…
