An Early-Warning System for Shipping Market Crisis Using Climate Index

This study was aimed at assessing the spatial and temporal distribution of surface water quality variables of the Xin’anjiang River (Huangshan). For this purpose, 960 water samples were collected monthly along the Xin’anjiang River from 2008 to 2017. Twenty-four water quality indicators, according to the environmental quality standards for surface water (GB 3838-2002), were detected to evaluate the water quality of the Xin’anjiang River over the past 10 years. Principal component analysis (PCA) was used to comprehensively evaluate the water quality across eight monitoring stations and analyze the sources of water pollution. The results showed that all samples could be analyzed by three main components, which accounted for 87.24% of the total variance. PCA technology identified important water quality parameters and revealed that nutrient pollution and organic pollution are major latent factors which influence the water quality of Xin’anjiang River. It also showed that agricultural activities, erosion, domestic, and industrial discharges are fundamental causes of water pollution in the study area. It is of great significance for water quality safety management and pollution control of the Xin’anjiang River. Meanwhile, the inverse distance weighted (IDW) method was used to interpolate the PCA comprehensive score. Based on this, the temporal and spatial structure and changing characteristics of water quality in the Xin’anjiang River were analyzed. We found that the overall water quality of Xin’anjiang River (Huangshan) was stable from 2008 to 2017, but the pollution of the Pukou sampling point was of great concern. The results of IDW helped us to identify key areas requiring control in the Xin’anjiang River, which pointed the way for further delicacy management of the river. This study proved that the combination of PCA and IDW interpolation is an effective tool for determining surface water quality. It was of great significance for the control of water pollution in Xin’anjiang River and the reduction of eutrophication pressure in Thousand Island Lake.

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“…(3) Calculate the correlation coefficient matrix, R, with standardized data and determine the correlation between indicators (Equation (2)) [30].…”

Section: Principal Component Analysismentioning

confidence: 99%

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Yang

Zhao

Wang

et al. 2020

IJERPH

206

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“…e proposal and application of this technology have also greatly changed the way equipment is maintained. Up to now, only some simple machines have applied this technology, but specific to large equipment, such as rolling mills, the fast and accurate position determination technology of multimodal couplings is still immature [4,5].…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Position Determination of Multimodal Couplings of Steel Rolling Machinery and Equipment Combined with Multimedia Optimized Particle Filters

Tan

2021

Advances in Multimedia

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During the overhaul and maintenance of the transmission system of the modern rolling mill, a very common problem is the positioning of the motor and the reducer or speed increaser. The operation of the equipment and the service life of the equipment will be significantly affected by the accuracy of the multimodal coupling position determination. Therefore, precise positioning and rapid positioning must be achieved. This article intends to analyze how this type of equipment achieves rapid and accurate positioning and what factors will affect the location determination process.

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“…Through step three, the standardized correlation coefficient matrix (

) defines the correlation between the indicators in Equation ( 10 ) [ 63 ].

…”

Section: Proposed Methodologymentioning

confidence: 99%

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework

Siami,

Barszcz,

Zimroz

2024

Sensors

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In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes.

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An Early-Warning System for Shipping Market Crisis Using Climate Index

Cited by 15 publications

References 12 publications

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Fast and Accurate Position Determination of Multimodal Couplings of Steel Rolling Machinery and Equipment Combined with Multimedia Optimized Particle Filters

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework

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