7,392 research outputs found
The Use of a Factory Simulation to Evaluate a Flexible Control Structure for Integrated Manufacturing
Once a control structure for an integrated manufacturing system is decided upon, manufacturing activities are limited by that structure. A flexible control structure is presented as an approach for accommodating a variety of manufacturing activities, without being limited to a single control structure. A flexible control structure is one that allows multiple types of control structure in the manufacturing process. For example, both hierarchical and non-hierarchical structures may be used in a flexible structure. The properties of a flexible control structure are discussed from the point of view of graph theory.
Control structures for automated manufacturing are difficult to evaluate without actually setting up a pilot production system. Since this is often not possible for reasons of expense or equipment availability, it would be advantageous to be able to simulate alternative control structures for their various characteristics. In this research, flexible control is demonstrated with a factory simulation of an automated on-line/post-process inspection system. Factory simulations present special problems when used for evaluation purposes. An approach to using a factory simulation is developed, and alternative control structures are evaluated with respect to their fault tolerance characteristics. The results of this research indicate that flexible control may be cost effective when a large variety of manufacturing activities must be accommodated, but further research is needed to confirm precisely how wide a range and what types of activities would justify this approach
Rethinking Item Importance in Session-based Recommendation
Session-based recommendation aims to predict users' based on anonymous
sessions. Previous work mainly focuses on the transition relationship between
items during an ongoing session. They generally fail to pay enough attention to
the importance of the items in terms of their relevance to user's main intent.
In this paper, we propose a Session-based Recommendation approach with an
Importance Extraction Module, i.e., SR-IEM, that considers both a user's
long-term and recent behavior in an ongoing session. We employ a modified
self-attention mechanism to estimate item importance in a session, which is
then used to predict user's long-term preference. Item recommendations are
produced by combining the user's long-term preference and current interest as
conveyed by the last interacted item. Experiments conducted on two benchmark
datasets validate that SR-IEM outperforms the start-of-the-art in terms of
Recall and MRR and has a reduced computational complexity
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
Bike sharing provides an environment-friendly way for traveling and is
booming all over the world. Yet, due to the high similarity of user travel
patterns, the bike imbalance problem constantly occurs, especially for dockless
bike sharing systems, causing significant impact on service quality and company
revenue. Thus, it has become a critical task for bike sharing systems to
resolve such imbalance efficiently. In this paper, we propose a novel deep
reinforcement learning framework for incentivizing users to rebalance such
systems. We model the problem as a Markov decision process and take both
spatial and temporal features into consideration. We develop a novel deep
reinforcement learning algorithm called Hierarchical Reinforcement Pricing
(HRP), which builds upon the Deep Deterministic Policy Gradient algorithm.
Different from existing methods that often ignore spatial information and rely
heavily on accurate prediction, HRP captures both spatial and temporal
dependencies using a divide-and-conquer structure with an embedded localized
module. We conduct extensive experiments to evaluate HRP, based on a dataset
from Mobike, a major Chinese dockless bike sharing company. Results show that
HRP performs close to the 24-timeslot look-ahead optimization, and outperforms
state-of-the-art methods in both service level and bike distribution. It also
transfers well when applied to unseen areas
Entanglement wedge cross section inequalities in AdS/BCFT
The entanglement wedge cross section in holographic picture provides a
geometrical description of the entanglement for mixed states. In this paper we
study the inequalities for the entanglement wedge cross section in AdS/BCFT
duality. In the presence of the boundary in conformal field theory (CFT), the
dual entanglement wedge cross section exhibits abundant phase structures since
the extremal surface may end on the brane. We present a universal treatment
which is applicable for all the possible phases such that the inequalities for
the entanglement wedge cross section can be proved in an algebraic manner
rather than a diagrammatic manner. We show that the entanglement wedge cross
section in AdS/BCFT satisfies the same inequalities as in AdS/CFT
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