117,262 research outputs found
Integration of multi-scale biosimulation models via light-weight semantics
Currently, biosimulation researchers use a variety of computational environments and languages to model biological processes. Ideally, researchers should be able to semi- automatically merge models to more effectively build larger, multi-scale models. How- ever, current modeling methods do not capture the underlying semantics of these models sufficiently to support this type of model construction. In this paper, we both propose a general approach to solve this problem, and we provide a specific example that demon- strates the benefits of our methodology. In particular, we describe three biosimulation models: (1) a cardio-vascular fluid dynamics model, (2) a model of heart rate regulation via baroreceptor control, and (3) a sub-cellular-level model of the arteriolar smooth mus- cle. Within a light-weight ontological framework, we leverage reference ontologies to match concepts across models. The light-weight ontology then helps us combine our three models into a merged model that can answer questions beyond the scope of any single model
A new fuzzy set merging technique using inclusion-based fuzzy clustering
This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
A Product Line Systems Engineering Process for Variability Identification and Reduction
Software Product Line Engineering has attracted attention in the last two
decades due to its promising capabilities to reduce costs and time to market
through reuse of requirements and components. In practice, developing system
level product lines in a large-scale company is not an easy task as there may
be thousands of variants and multiple disciplines involved. The manual reuse of
legacy system models at domain engineering to build reusable system libraries
and configurations of variants to derive target products can be infeasible. To
tackle this challenge, a Product Line Systems Engineering process is proposed.
Specifically, the process extends research in the System Orthogonal Variability
Model to support hierarchical variability modeling with formal definitions;
utilizes Systems Engineering concepts and legacy system models to build the
hierarchy for the variability model and to identify essential relations between
variants; and finally, analyzes the identified relations to reduce the number
of variation points. The process, which is automated by computational
algorithms, is demonstrated through an illustrative example on generalized
Rolls-Royce aircraft engine control systems. To evaluate the effectiveness of
the process in the reduction of variation points, it is further applied to case
studies in different engineering domains at different levels of complexity.
Subject to system model availability, reduction of 14% to 40% in the number of
variation points are demonstrated in the case studies.Comment: 12 pages, 6 figures, 2 tables; submitted to the IEEE Systems Journal
on 3rd June 201
Towards a Semantic-based Approach for Modeling Regulatory Documents in Building Industry
Regulations in the Building Industry are becoming increasingly complex and
involve more than one technical area. They cover products, components and
project implementation. They also play an important role to ensure the quality
of a building, and to minimize its environmental impact. In this paper, we are
particularly interested in the modeling of the regulatory constraints derived
from the Technical Guides issued by CSTB and used to validate Technical
Assessments. We first describe our approach for modeling regulatory constraints
in the SBVR language, and formalizing them in the SPARQL language. Second, we
describe how we model the processes of compliance checking described in the
CSTB Technical Guides. Third, we show how we implement these processes to
assist industrials in drafting Technical Documents in order to acquire a
Technical Assessment; a compliance report is automatically generated to explain
the compliance or noncompliance of this Technical Documents
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