This paper addresses a travel time reliable signal control problem. Travel time distributional estimates are obtained from a stochastic microscopic traffic simulator. The estimates are embedded within a simulation-based optimization algorithm. Analytical approximations of the simulated metrics are combined with the simulated data in order to enhance the computational efficiency of the algorithm. The signal control problems are formulated based on the expectation and the standard deviation of travel time metrics. The proposed approach goes beyond the traditional use of first-order simulated information, it addresses a problem that embeds higher-order distributional information. It is used to solve a large-scale signal control problem. The approach addresses these challenging simulation-based optimization problems in a computationally efficient manner. Its performance is compared to that of a traditional simulation-based optimization approach. The proposed method systematically outperforms the traditional approach. Such an approach can be used to informthe design and operations of transportation systems by, for instance, addressing reliable and/or robust formulations of traditional transportation problems.

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With the development of terrestrial networks and satellite constellations, vessel movement information can be effectively collected based on Automatic Identification System (AIS) receivers. Vessel motion pattern classification using AIS plays an important role in maritime monitoring and management. However, classifying vast amounts of vessel motion information is prohibitive workload. The aim of this study is to develop effective methods that can aid in automatic vessel motion pattern classification in inland waterways. First, the Least-squares Cubic Spline Curves Approximation (LCSCA) technique is used to represent the vessel motion trajectory. Then, a traditional classification model based on Lp-norm (0 < p < 1) sparse representation is improved to classify vessel motion patterns. And a Matching Pursuit - Fletcher Reeves (MPFR) method is developed to find the sparse solutions of the proposed model. To validate the performance of the proposed model, two AIS datasets from the Yangtze River are collected and applied in our experiment. According to the results, we can know that the proposed model can effectively classify vessel motion pattern in inland waterways. And the effectiveness of the proposed model is superior to those of other representative classification methods.@en

The 11 papers in this special section focus on sensing and perception systems for intelligent manufacturing. This new industrial revolution calls for solutions to embedded intelligence, networked environment, interaction, autonomous, and high performance computing. The manufacturing industry is at the center of this wave of innovation with sensing and perception in focus. The papers included in this section examine the methodologies developed are all based on processing data generated by complex sensing devices acting on certain kind of physical fields, such as vision and temperature field, in order to render 2-D or 3-D information of fields. This kind of field sensing and perception development is the key to facilitate the intelligent operation of manufacturing systems in practices.@en

Advanced engineering and functional ceramics are sensitive to damage cracks, which delay the wide applications of these materials in various fields. Ceramic composites with enhanced fracture toughness may trigger a paradigm for design and application of the brittle components. This paper reviews the toughening mechanisms for the nanolayered MAX phase ceramics. The main toughening mechanisms for these ternary compounds were controlled by particle toughening, phase-transformation toughening and fiber-reinforced toughening, as well as texture toughening. Based on the various toughening mechanisms in MAX phase, models of SiC particles and fibers toughening Ti₃SiC₂ are established to predict and explain the toughening mechanisms. The modeling work provides insights and guidance to fabricate MAX phase-related composites with optimized microstructures in order to achieve the desired mechanical properties required for harsh application environments.

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Many private BitTorrent communities employ Sharing Ratio Enforcement
(SRE) schemes to incentivize users to contribute. It has been demonstrated
that users in private communities are highly dedicated and that they seed much
longer than users in communities where SRE is not employed. While most pre-
vious studies focus on showing the positive effect of user dedication in achieving
high download speed, in this paper we explore the user behaviors in private
communities, we argue the reasons for these behaviors, and we demonstrate
both the positive and the negative effects of these behaviors. We show that
under SRE, users seed for excessively long times to maintain required sharing
ratios, but that their seedings are often not very productive (in terms of low
upload speed) and that their long seeding times do not necessarily lead to large
upload amounts. We find that as users evolve in the community, some users
become more committed, in terms of increasing ratios between their seeding
and leeching times. In the mean time, some users game the system by keeping
risky and low sharing ratios while leeching more often than seeding. Based on
these observations, we analyze strategies that alleviate the negative effects of
these user behaviors from both the user’s and the community administrator’s
perspective.@en