26,804 research outputs found
Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences
The predominant knowledge-based approach to automated model construction,
compositional modelling, employs a set of models of particular functional
components. Its inference mechanism takes a scenario describing the constituent
interacting components of a system and translates it into a useful mathematical
model. This paper presents a novel compositional modelling approach aimed at
building model repositories. It furthers the field in two respects. Firstly, it
expands the application domain of compositional modelling to systems that can
not be easily described in terms of interacting functional components, such as
ecological systems. Secondly, it enables the incorporation of user preferences
into the model selection process. These features are achieved by casting the
compositional modelling problem as an activity-based dynamic preference
constraint satisfaction problem, where the dynamic constraints describe the
restrictions imposed over the composition of partial models and the preferences
correspond to those of the user of the automated modeller. In addition, the
preference levels are represented through the use of symbolic values that
differ in orders of magnitude
Microscopic Investigation of Vortex Breakdown in a Dividing T-Junction Flow
3D-printed microfluidic devices offer new ways to study fluid dynamics. We
present the first clear visualization of vortex breakdown in a dividing
T-junction flow. By individual control of the inflow and two outflows, we
decouple the effects of swirl and rate of vorticity decay. We show that even
slight outflow imbalances can greatly alter the structure of vortex breakdown,
by creating a net pressure difference across the junction. Our results are
summarized in a dimensionless phase diagram, which will guide the use of vortex
breakdown in T-junctions to achieve specific flow manipulation.Comment: 5 pages, 5 figure
An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control.
With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment
Anti-shielding Effect and Negative Temperature in Instantaneously Reversed Electric Fields and Left-Handed Media
The connections between the anti-shielding effect, negative absolute
temperature and superluminal light propagation in both the instantaneously
reversed electric field and the left-handed media are considered in the present
paper. The instantaneous inversion of the exterior electric field may cause the
electric dipoles into the state of negative absolute temperature and therefore
give rise to a negative effective mass term of electromagnetic field (i. e.,
the electromagnetic field propagating inside the negative-temperature medium
will acquire an imaginary rest mass), which is said to result in the potential
superluminality effect of light propagation in this anti-shielding dielectric.
In left-handed media, such phenomena may also arise.Comment: 9 pages, Late
Integrate the GM(1,1) and Verhulst models to predict software stage effort
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Software effort prediction clearly plays a crucial role in software project management. In keeping with more dynamic approaches to software development, it is not sufficient to only predict the whole-project effort at an early stage. Rather, the project manager must also dynamically predict the effort of different stages or activities during the software development process. This can assist the project manager to reestimate effort and adjust the project plan, thus avoiding effort or schedule overruns. This paper presents a method for software physical time stage-effort prediction based on grey models GM(1,1) and Verhulst. This method establishes models dynamically according to particular types of stage-effort sequences, and can adapt to particular development methodologies automatically by using a novel grey feedback mechanism. We evaluate the proposed method with a large-scale real-world software engineering dataset, and compare it with the linear regression method and the Kalman filter method, revealing that accuracy has been improved by at least 28% and 50%, respectively. The results indicate that the method can be effective and has considerable potential. We believe that stage predictions could be a useful complement to whole-project effort prediction methods.National Natural Science Foundation of
China and the Hi-Tech Research
and Development Program of Chin
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