scholarly journals CONCEPTUAL FRAMEWORK FOR HEAVY-DUTY VEHICLE PLATOONING IN PHYSICAL INTERNET SYSTEMS

2021 ◽  
Vol 8 (4) ◽  
pp. 407-414
Author(s):  
Eszter Puskás ◽  
Gábor Bohács
Keyword(s):  
Network Architecture ◽  
Interconnected System ◽  
Logistics Network ◽  
Logistics Systems ◽  
Technology Innovations ◽  
Vehicle Platooning ◽  
Heavy Duty Vehicle ◽  
Physical Internet ◽  
Vehicle Technology ◽  
Different Levels

One of today's most significant challenges is sustainability, which is closely linked to environmentally friendly solutions and resource efficiency. As a solution to these goals, the concept of the Physical Internet emerged, defining the logistics network of the future as a global, open, and interconnected system. Concerning the conditions of vehicles based on Physical Internet-based systems, we cannot ignore the latest vehicle technology innovations that appear more and more intensively in parallel. The framework proposes planning at the strategic, tactical, and operational levels. Different levels of coordination implement different approaches to platoon coordination in line with the network architecture of PI-based logistics systems. We recommend the highest level of offline design in fixed π-hubs. The tactical level involves designing π-hubs online. We propose the implementation of speed-based solutions at the operational planning level.

A Densely Connected Network Based on U-Net for Medical Image Segmentation

2021 ◽  
Vol 17 (3) ◽  
pp. 1-14
Author(s):  
Zhenzhen Yang ◽  
Pengfei Xu ◽  
Yongpeng Yang ◽  
Bing-Kun Bao
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Loss Function ◽  
Network Architecture ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Cross Entropy ◽  
Loss Functions ◽  
Feature Maps ◽  
Different Levels

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

scholarly journals Ambiguity in language networks

2015 ◽  
Vol 32 (1) ◽  
Author(s):  
Ricard V. Solé ◽  
Luís F. Seoane
Keyword(s):  
Language Processing ◽  
Network Architecture ◽  
Essential Role ◽  
Scaling Laws ◽  
Network Growth ◽  
Computational Approaches ◽  
Universal Scaling ◽  
Different Levels ◽  
Relevant Component ◽  
Language Network

AbstractHuman language defines the most complex outcomes of evolution. The emergence of such an elaborated form of communication allowed humans to create extremely structured societies and manage symbols at different levels including, among others, semantics. All linguistic levels have to deal with an astronomic combinatorial potential that stems from the recursive nature of languages. This recursiveness is indeed a key defining trait. However, not all words are equally combined nor frequent. In breaking the symmetry between less and more often used and between less and more meaning-bearing units, universal scaling laws arise. Such laws, common to all human languages, appear on different stages from word inventories to networks of interacting words. Among these seemingly universal traits exhibited by language networks, ambiguity appears to be a specially relevant component. Ambiguity is avoided in most computational approaches to language processing, and yet it seems to be a crucial element of language architecture. Here we review the evidence both from language network architecture and from theoretical reasonings based on a least effort argument. Ambiguity is shown to play an essential role in providing a source of language efficiency, and is likely to be an inevitable byproduct of network growth.

Disturbance observer approach for fuel-efficient heavy-duty vehicle platooning

2019 ◽  
Vol 58 (5) ◽  
pp. 748-767 ◽  
Author(s):  
Gyujin Na ◽  
Gyunghoon Park ◽  
Valerio Turri ◽  
Karl H. Johansson ◽  
Hyungbo Shim ◽  
...  
Keyword(s):  
Disturbance Observer ◽  
Heavy Duty ◽  
Vehicle Platooning ◽  
Heavy Duty Vehicle

MCA-Unet: Multi-class Attention-aware U-net for Seismic Phase Picking

2021 ◽  
Author(s):  
Wei Li ◽  
Georg Rümpker ◽  
Horst Stöcker ◽  
Megha Chakraborty ◽  
Darius Fener ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Attention Mechanism ◽  
Seismic Waveforms ◽  
Decoder Architecture ◽  
Seismic Event Detection ◽  
Feature Information ◽  
Multi Scales ◽  
Segmentation Problem ◽  
Different Levels

<p>This study presents a deep learning based algorithm for seismic event detection and simultaneous phase picking in seismic waveforms. U-net structure-based solutions which consists of a contracting path (encoder) to capture feature information and a symmetric expanding path (decoder) that enables precise localization, have proven to be effective in phase picking. The network architecture of these U-net models mainly comprise of 1D CNN, Bi- & Uni-directional LSTM, transformers and self-attentive layers. Althought, these networks have proven to be a good solution, they may not fully harness the information extracted from multi-scales.</p><p> In this study, we propose a simple yet powerful deep learning architecture by combining multi-class with attention mechanism, named MCA-Unet, for phase picking.  Specially, we treat the phase picking as an image segmentation problem, and incorporate the attention mechanism into the U-net structure to efficiently deal with the features extracted at different levels with the goal to improve the performance on the seismic phase picking. Our neural network is based on an encoder-decoder architecture composed of 1D convolutions, pooling layers, deconvolutions and multi-attention layers. This architecture is applied and tested to a field seismic dataset (e.g. Wenchuan Earthquake Aftershocks Classification Dataset) to check its performance.</p>

Transforming Dubai Logistics Corridor into a Global Logistics Hub

2017 ◽  
Vol 14 (2) ◽  
pp. 115-136 ◽  
Author(s):  
Balan Sundarakani
Keyword(s):  
Operations Management ◽  
Indian Subcontinent ◽  
Location Strategy ◽  
Hub Location ◽  
East European ◽  
Logistics Network ◽  
Expansion Strategy ◽  
Logistics Systems ◽  
Global Logistics ◽  
Country Analysis

The case discusses the various factors that bolstered positioning Dubai as the ideal location for a logistics hub, located at the crossroads of international trade and commerce between the Eastern and Western worlds. Dubai is also regarded as the gateway to the world’s most progressive markets which include Greater Africa, the Indian subcontinent, and the East European countries. The case illustrates some of the critical challenges faced by the city in particular and the country as a whole, in the past decade, thereby evaluating the issues and risks that can hinder its strategic logistics developmental roadmap. The case can be taken up for subjects such as logistics and supply chain management, operations management, global logistics systems, warehouse management and strategic management, and to enrich concepts related but not limited to facility location strategy, logistics network expansion strategy, country analysis, distribution hub location strategy, etc.

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