scholarly journals Assessing the Robustness of a Factory Amid the COVID-19 Pandemic: A Fuzzy Collaborative Intelligence Approach

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 481
Author(s):  
Toly Chen ◽  
Yu-Cheng Wang ◽  
Min-Chi Chiu
Keyword(s):  
Average Method ◽  
Weighted Average ◽  
Ideal Solution ◽  
Weighted Average Method ◽  
Fuzzy Weighted Average ◽  
Order Preference ◽  
Collaborative Intelligence ◽  
The Impact ◽  
Intelligence Approach ◽  
Assessment Result

The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic.

scholarly journals Evaporation from weighing precipitation gauges: impacts on automated gauge measurements and quality assurance methods

2015 ◽  
Vol 8 (6) ◽  
pp. 2291-2300 ◽  
Author(s):  
R. D. Leeper ◽  
J. Kochendorfer
Keyword(s):  
Average Method ◽  
Weighted Average ◽  
The Other ◽  
Reference Network ◽  
Weighted Average Method ◽  
The World ◽  
Average Loss ◽  
Precipitation Gauge ◽  
The Impact

Abstract. Evaporation from a precipitation gauge can cause errors in the amount of measured precipitation. For automated weighing-bucket gauges, the World Meteorological Organization (WMO) suggests the use of evaporative suppressants and frequent observations to limit these biases. However, the use of evaporation suppressants is not always feasible due to environmental hazards and the added cost of maintenance, transport, and disposal of the gauge additive. In addition, research has suggested that evaporation prior to precipitation may affect precipitation measurements from auto-recording gauges operating at sub-hourly frequencies. For further evaluation, a field campaign was conducted to monitor evaporation and its impacts on the quality of precipitation measurements from gauges used at U.S. Climate Reference Network (USCRN) stations. Two Geonor gauges were collocated, with one gauge using an evaporative suppressant (referred to as Geonor-NonEvap) and the other with no suppressant (referred to as Geonor-Evap) to evaluate evaporative losses and evaporation biases on precipitation measurements. From June to August, evaporative losses from the Geonor-Evap gauge exceeded accumulated precipitation, with an average loss of 0.12 mm h−1. The impact of evaporation on precipitation measurements was sensitive to the choice of calculation method. In general, the pairwise method that utilized a longer time series to smooth out sensor noise was more sensitive to gauge evaporation (−4.6% bias with respect to control) than the weighted-average method that calculated depth change over a smaller window (

An Approach for Solving Fuzzy MADM Problems

1997 ◽  
Vol 05 (04) ◽  
pp. 459-480 ◽  
Author(s):  
Chie-Bein Chen ◽  
Chiu-Chi Wei
Keyword(s):  
Weighted Average ◽  
Practical Method ◽  
Multiple Attribute Decision Making ◽  
Ideal Solution ◽  
Topsis Method ◽  
Fuzzy Weighted Average ◽  
Multiple Attribute ◽  
Order Preference ◽  
Fuzzy Madm ◽  
Simple Additive Weighting

An approach using defuzzifying methods is proposed for the fuzzy multiple attribute decision-making (MADM) problems. The computing effectiveness of the proposed defuzzifying methods combined with the simple additive weighting (SAW) method and the technique for order preference by similarity to ideal solution (TOPSIS) method are evaluated based on a comparison to the improved fuzzy weighted average (IFWA) followed by a ranking method. Both SAW and TOPSIS methods are two of classic MADM methods. The purpose of this application is to make the method easier to program and data easier to manipulate. This results in a more practical method for fuzzy decisions. A numerical example and experiment are discussed to demonstrate the implementation of the methods in different input conditions.

Front vehicle detection based on improved fusion method for lidar and visual image

2022 ◽  
pp. 095440702110685
Author(s):  
Aijuan Li ◽  
Zhenghong Chen ◽  
Donghong Ning ◽  
Xin Huang ◽  
Gang Liu
Keyword(s):  
Average Method ◽  
Weighted Average ◽  
Visual Image ◽  
Vehicle Detection ◽  
Detection Accuracy ◽  
The Real ◽  
Weighted Average Method ◽  
Weighted Fusion ◽  
Vehicle Test ◽  
Real Vehicle

In order to ensure the detection accuracy, an improved adaptive weighted (IAW) method is proposed in this paper to fuse the data of images and lidar sensors for the vehicle object’s detection. Firstly, the IAW method is proposed in this paper and the first simulation is conducted. The unification of two sensors’ time and space should be completed at first. The traditional adaptive weighted average method (AWA) will amplify the noise in the fusion process, so the data filtered with Kalman Filter (KF) algorithm instead of with the AWA method. The proposed IAW method is compared with the AWA method and the Distributed Weighted fusion KF algorithm in the data fusion simulation to verify the superiority of the proposed algorithm. Secondly, the second simulation is conducted to verify the robustness and accuracy of the IAW algorithm. In the two experimental scenarios of sparse and dense vehicles, the vehicle detection based on image and lidar is completed, respectively. The detection data is correlated and merged through the IAW method, and the results show that the IAW method can correctly associate and fuse the data of the two sensors. Finally, the real vehicle test of object vehicle detection in different environments is carried out. The IAW method, the KF algorithm, and the Distributed Weighted fusion KF algorithm are used to complete the target vehicle detection in the real vehicle, respectively. The advantages of the two sensors can give full play, and the misdetection of the target objects can be reduced with proposed method. It has great potential in the application of object acquisition.

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