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What is Pooling

Chapter 3
Building a Sustainable Transportation Infrastructure for Long-Term Economic Growth
Chassis lessors negotiate an agreement where all lessor-owned international chassis in the pool are available at terminals that are doing business with it.
Published in Chapter:
Chassis: The Unsung Linchpin in the Global Supply Chain
Bethany Stich (University of New Orleans, USA), James Amdal (University of New Orleans, USA), and Peter Webb (University of New Orleans, USA)
Copyright: © 2019 |Pages: 27
DOI: 10.4018/978-1-5225-7396-8.ch003
Abstract
International trade requires efficient delivery between exporter and importer, usually by ocean vessels. Roughly 90% of cargo is transported in shipping containers, delivered for export by truck or rail, then received at the importer to be similarly discharged. This chapter discusses containerized trade, focusing on international chassis, the wheeled shipping container bed. The authors discuss the invention of containerization and give the historical context for US chassis provision. The chapter outlines the chassis logistical difficulties of US truckers. An overview of attempts to solve this by chassis pooling is provided. The chapter then addresses differences between chassis in the US and the rest of the world. Key chassis regulations are covered, followed by a discussion of the relation of chassis to sustainability. Antitrust issues concerning the main chassis providers and the three recently created ocean carrier alliances are covered. The authors conclude with three recommendations for improving US chassis access.
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More Results
Chapter 1
Deep Learning Based Sentiment Analysis for Phishing SMS Detection
It is used to decrease the resolution of the feature map while preserving the features that are required for classification.
Published in Chapter: Deep Learning Based Sentiment Analysis for Phishing SMS Detection; From: New Opportunities for Sentiment Analysis and Information Processing
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Chapter 6
Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence: Subjective and Objective Assessment
A pooling layer is another building block of a CNN. Its function is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network. Pooling layer operates on each feature map independently. The most common approach used in pooling is max pooling.
Published in Chapter: Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence: Subjective and Objective Assessment; From: Fuzzy Expert Systems and Applications in Agricultural Diagnosis
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Chapter 3
Understanding Convolutional Neural Network With TensorFlow: CNN
Pooling merely refers to an image's down-sampling, in pooling layers to minimize the dimensionality of feature maps. Consequently, it decreases both the number of parameters that must be learned and the amount of computation done by the network. The pooling layer summarizes the characteristics contained in an area of the convolution layer-generated feature map.
Published in Chapter: Understanding Convolutional Neural Network With TensorFlow: CNN; From: Machine Learning Algorithms Using Scikit and TensorFlow Environments
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