China
Africa
Explore chapters and articles related to this topic
Air Cargo
Published in John G. Wensveen, Air Transportation, 2018
Dimensional weight is computed by finding the cubic measurement of a shipment (length × width × height) and charging the rate for 1 pound for each 194 cubic inches. There are exceptions. For example, cut flowers and nursery stock being transported to domestic cities take a charge of 1 pound for each 250 cubic inches. In this way, if a cargo compartment were filled with, say, 20 pounds of Styrofoam cups, the charge would be based on a weight that represented a minimum density in relation to the space occupied.
Air Cargo
Published in John G. Wensveen, Air Transportation, 2016
Dimensional weight is computed by finding the cubic measurement of a shipment (length × width × height) and charging the rate for 1 pound for each 194 cubic inches. There are exceptions. For example, cut flowers and nursery stock being transported to domestic cities take a charge of 1 pound for each 250 cubic inches. In this way, if a cargo compartment were filled with, say, 20 pounds of Styrofoam cups, the charge would be based on a weight that represented a minimum density in relation to the space occupied.
The m-polar fuzzy ELECTRE-I integrated AHP approach for selection of non-traditional machining processes
Published in Cogent Engineering, 2023
Sensitivity analysis is a procedure used to look into how changing input can affect output for the MCDM process. There are other types of sensitivity analysis, but the two most common types are single-dimensional weight sensitivity analysis (SDWSA) and high-dimensional weight sensitivity analysis (HDWSA). While HDWSA uses the performance score to determine the most important criteria weight, SDWSA uses a weight additive constraint. To determine the range of criteria weight stability, SDWSA is now being used. Due to the lack of a performance score for the mFS ELECTRE-I method, HDWSA cannot be performed. The local and global stability range for the weight of the criteria is examined in SDWSA.
An atlas-free newborn brain image segmentation and classification scheme based on SOM-DCNN with sparse auto encoder
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2020
Tushar H. Jaware, K. B. Khanchandani, Durgeshwari Kalal
Start: The n-dimensional weight vectors of the computing units are selected at random. An initial radius, learning constant and a neighbourhood function are selected.
Identification and modelling of race driving styles
Published in Vehicle System Dynamics, 2022
Stefan Löckel, André Kretschi, Peter van Vliet, Jan Peters
As the previous, time-depending definition of the driving line distributions with ProMPs causes local distortions due to varying sector times in the complex simulation environment, we choose a modified, more robust representation as shown in Algorithm 2. We iterate over all demonstrations for a particular track and initially interpolate the time-series data for the x and y positions in the inertial frame.3 Subsequently, we perform a spatial mapping to find the closest point on the track centre-line for each position. This facilitates the definition of the vehicle position trajectories depending on the distance of the track centre-line s. In order to maintain the information on the vehicle speed, we additionally consider the time as a function of this distance s. These distance-based trajectories are now used to build the target matrix for each demonstration lap i. This information is subsequently projected into a lower-dimensional weight space utilising ridge regression with regularisation factor and the basis function matrix . For we use 200 Gaussian radial basis functions with centres c distributed equidistantly around the track and an appropriate predefined spread σ. Each column of corresponds to the distances s which are present in , and each row contains the evaluation of the respective radial basis function for the given distances.