|
|
|
|
|
Fan Lin, Dengjie Chen, Cheng Liu and Jincheng He
This study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit?s electrical characteristics. By using dielectric properties, the method accurately...
ver más
|
|
|
|
|
|
|
|
|
|
|
Liqiu Chen, Chongshi Gu, Sen Zheng and Yanbo Wang
Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to elim...
ver más
|
|
|
|
|
|
|
|
|
|
|
Tianli Wang, Yanji Ma and Siqi Luo
As one of the major new agricultural business entities, agricultural leading enterprises (ALEs) are responsible for ensuring national food security, leading agricultural and rural modernization, and increasing farmers? employment prospects and incomes. F...
ver más
|
|
|
|
|
|
|
|
|
|
|
Francisco Melo Pereira and Rute C. Sofia
This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shengwei Jia, Nianyu Zou, Songhai Xu and Min Cheng
In this paper, an illumination measurement system is proposed and experimentally demonstrated. The system consists of two parts, including the illumination acquisition module mounted on the UAV and the real-time display interface of the cloud platform wi...
ver más
|
|
|
|
|
|
|
|
|
|
|
N.N. Goglev,S.A. Migalin,E.V. Kasatkina
Pág. 111 - 119
The use of artificial intelligence technologies and big data analysis in risk management makes it possible to reduce the burden on experts and reduce the influence of the human factor in risk assessment. These technologies are well studied and actively u...
ver más
|
|
|
|
|
|
|
|
|
|
|
Diogo Ribeiro, Luís Miguel Matos, Guilherme Moreira, André Pilastri and Paulo Cortez
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening pr...
ver más
|
|
|
|
|
|
|
|
|
|
|
Abiodun M. Ikotun and Absalom E. Ezugwu
Automatic clustering problems require clustering algorithms to automatically estimate the number of clusters in a dataset. However, the classical K-means requires the specification of the required number of clusters a priori. To address this problem, met...
ver más
|
|
|
|
|
|
|
|
|
|
|
Omar Alghushairy, Raed Alsini, Terence Soule and Xiaogang Ma
Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning. Outlier detection is impo...
ver más
|
|
|
|
|
|
|
|
|
|
|
Everton Jose Santana, Ricardo Petri Silva, Bruno Bogaz Zarpelão and Sylvio Barbon Junior
With data collected by Internet of Things sensors, deep learning (DL) models can forecast the generation capacity of photovoltaic (PV) power plants. This functionality is especially relevant for PV power operators and users as PV plants exhibit irregular...
ver más
|
|
|
|
|
|