Results 71 to 80 of about 114,884 (315)

Protecting Cyber Physical Systems Using a Learned MAPE-K Model

IEEE Access, 2019
Industry 4.0 leverages on cyber-physical systems (CPSs) that enable different physical sensors, actuators, and controllers to be interconnected via switches and cloud computing servers, forming complex online systems.
Ibrahim Elgendi, Md. Farhad Hossain, A. Jamalipour, K. Munasinghe   +3 more
semanticscholar   +1 more source

Volatility analysis and forecasting of vegetable prices using an ARMA‐GARCH model: An application of the CF filter and seasonal adjustment method to Korean green onions

Agribusiness, EarlyView.
Abstract The vegetable market experiences significant price fluctuations due to the complex interplay of trend, cyclical, seasonal, and irregular factors. This study takes Korean green onions as an example and employs the Christiano–Fitzgerald filter and the CensusX‐13 seasonal adjustment methods to decompose its price into four components: trend ...
Yiyang Qiao, Byeong‐il Ahn
wiley   +1 more source

Self-Adaptive Framework Based on MAPE Loop for Internet of Things †

Italian National Conference on Sensors, 2019
The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other ...
Euijong Lee, Young-Duk Seo, Young-Gab Kim   +2 more
semanticscholar   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

PERAMALAN MENGGUNAKAN METODE DOUBLE EXPONENTIAL SMOOTHING DAN VERIFIKASI HASIL PERAMALAN MENGGUNAKAN GRAFIK PENGENDALI TRACKING SIGNAL (STUDI KASUS: DATA IHK PROVINSI KALIMANTAN TIMUR)

Barekeng, 2020
This study uses IHK data from East Kalimantan Province in January 2016 to February 2019, which has a patterned trend. Data that shows a trend, can use double exponential smoothing forecasting one parameter from Brown and two parameters from Holt.
Humairo Dyah Puji Habsari, Ika Purnamasari, Desi Yuniarti   +2 more
doaj   +1 more source

Bus Travel TIME in the Mixed Traffic Based on Statistica Neural Network [PDF]

, 2010
This paper presents the assessment of a number of factors affecting bus travel time and a relationship model between those factors and bus travel time. Statistica Neural Network (SNN) tool is used to solve this complex phenomenon.
Kamaruddin, I. (Ibrahim), Napiah, M. (Madzlan), Suwardo, S. (Suwardo), Wahyunggoro, O. (Oyas)   +3 more
core  

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties

Advanced Intelligent Discovery, EarlyView.
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler   +5 more
wiley   +1 more source

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Advanced Intelligent Discovery, EarlyView.
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Option Pricing of Twin Assets [PDF]

, 2014
How to price and hedge claims on nontraded assets are becoming increasingly important matters in option pricing theory today. The most common practice to deal with these issues is to use another similar or "closely related" asset or index which is traded,
Araneda, Axel A., Villena, Marcelo J.
core