Discovery of Stable Abstractions for Aspect-Oriented Composition in the Car Crash Management Domain

2010 ◽  
pp. 375-422 ◽  
Author(s):  
Dimitri Van Landuyt ◽  
Eddy Truyen ◽  
Wouter Joosen
Keyword(s):  
Management Domain ◽  
Car Crash

scholarly journals CrashNet: an encoder–decoder architecture to predict crash test outcomes

2021 ◽  
Author(s):  
Mohamed Karim Belaid ◽  
Maximilian Rabus ◽  
Ralf Krestel
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Architecture ◽  
Time Series Prediction ◽  
Crash Test ◽  
Fem Simulations ◽  
Management Domain ◽  
Car Crash ◽  
Car Crashes ◽  
Crash Tests

AbstractDestructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder–decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional neural network (CNN) and CNN transpose layers. We propose to concatenate those with an MLP capable of learning how to inject the given scalars into the output time series. In addition, we replace the CNN transpose with 2D CNN transpose layers in order to force the model to process the hidden state of the set of scalars as one time series. The proposed CrashNet model can be trained efficiently and is able to process scalars and time series as input in order to infer the results of crash tests. CrashNet produces results faster and at a lower cost compared to destructive tests and FEM simulations. Moreover, it represents a novel approach in the car safety management domain.

DESIGN, MODELING AND FABRICATION OF AN EFFICIENT CAR CRASH MANAGEMENT SYSTEM

2020 ◽  
Author(s):  
Aishani Mukerji ◽  
Rounak Chakraborty ◽  
Kalyan Chatterjee ◽  
Sayanti Banerjee
Keyword(s):  
Management System ◽  
Car Crash

scholarly journals Empirical Study of Car Crash Simulation Analysis within the Development Phase

2019 ◽  
Vol 1 (1) ◽  
pp. 2843-2852 ◽  
Author(s):  
Naouress Fatfouta ◽  
Julie Stal-Le Cardinal ◽  
Christine Royer
Keyword(s):  
Empirical Study ◽  
Qualitative Analysis ◽  
Simulation Analysis ◽  
Development Phase ◽  
Vehicle Model ◽  
Crash Simulation ◽  
Automotive Company ◽  
Car Crash ◽  
Industrial Context ◽  
Current Practices

AbstractCar crash simulation analysis is an important phase within the vehicle development. It intends to analyse the crashworthiness of the vehicle model and examine the level of passive security. However, this activity is not trivial because of the considerable collaboration within the project, the large amount of analysed and exchanged data and a high exigency. Consequently, a solution to assist, ease and reduce the time of the process is desired.To study the current practices followed in the car crash simulation analysis an empirical study has been conducted. This study has been applied within the simulation analysis team, in the development phase, within an automotive company. This paper describes a qualitative analysis of the industrial context and diagnoses the dysfunctions in the current practices. This paper also highlights the current challenges encountered in the car crash simulation analysis.

Designing an Emergency Management Simulation Testbed to Investigate Incident Management Team Performance

2020 ◽  
Vol 64 (1) ◽  
pp. 1881-1885
Author(s):  
Changwon Son ◽  
Farzan Sasangohar ◽  
S. Camille Peres ◽  
Jukrin Moon
Keyword(s):  
Emergency Management ◽  
Team Performance ◽  
Real Life ◽  
Management Team ◽  
Future Research ◽  
Incident Management ◽  
Emergency Operations ◽  
Management Domain ◽  
Qualitative Approaches ◽  
Complex Phenomena

Investigating real-life disasters and crises has been challenging due to accompanying difficulties and risks posed by these complex phenomena. Previous research in the emergency management domain has largely relied on qualitative approaches to describe the event after it occurred. To facilitate investigations for more generalizable findings, this paper documents ongoing efforts to design an emergency management simulation testbed called Team Emergency Operations Simulation (TEOS) in which an incident management team (IMT) is situated. First, we describe the design process based on our previous work. Next, we present the overall description of TEOS including representative roles, tasks, and team environments. We also propose measures of team performance of the IMT and propose future research that can be realized through TEOS.

scholarly journals Sound Event Detection in Underground Parking Garage Using Convolutional Neural Network

2020 ◽  
Vol 4 (3) ◽  
pp. 20 ◽  
Author(s):  
Giuseppe Ciaburro
Keyword(s):  
Event Detection ◽  
High Accuracy ◽  
Surveillance Systems ◽  
Urban Mobility ◽  
Parking Garage ◽  
Large Size ◽  
Car Crash ◽  
Sound Event ◽  
Parking Management ◽  
Sound Event Detection

Parking is a crucial element in urban mobility management. The availability of parking areas makes it easier to use a service, determining its success. Proper parking management allows economic operators located nearby to increase their business revenue. Underground parking areas during off-peak hours are uncrowded places, where user safety is guaranteed by company overseers. Due to the large size, ensuring adequate surveillance would require many operators to increase the costs of parking fees. To reduce costs, video surveillance systems are used, in which an operator monitors many areas. However, some activities are beyond the control of this technology. In this work, a procedure to identify sound events in an underground garage is developed. The aim of the work is to detect sounds identifying dangerous situations and to activate an automatic alert that draws the attention of surveillance in that area. To do this, the sounds of a parking sector were detected with the use of sound sensors. These sounds were analyzed by a sound detector based on convolutional neural networks. The procedure returned high accuracy in identifying a car crash in an underground parking area.

Simulation Tool including Failure for Structural Adhesives in Full-Car Crash Models

2009 ◽  
Author(s):  
Horst Lanzerath ◽  
Niels Nowack ◽  
Erwan Mestres
Keyword(s):  
Simulation Tool ◽  
Structural Adhesives ◽  
Car Crash

Measuring the Complexity of Additive Manufacturing Supply Chains

2017 ◽  
Author(s):  
Ardeshir Raihanian Mashhadi ◽  
Sara Behdad
Keyword(s):  
Supply Chain ◽  
Additive Manufacturing ◽  
Supply Chains ◽  
Product Complexity ◽  
Information Theoretic ◽  
Manufacturing Method ◽  
Management Domain ◽  
Information Theoretic Measures ◽  
Production Cycles ◽  
Supply Planning

Complexity has been one of the focal points of attention in the supply chain management domain, as it deteriorates the performance of the supply chain and makes controlling it problematic. The complexity of supply chains has been significantly increased over the past couple of decades. Meanwhile, Additive Manufacturing (AM) not only revolutionizes the way that the products are made, but also brings a paradigm shift to the whole production system. The influence of AM extends to product design and supply chain as well. The unique capabilities of AM suggest that this manufacturing method can significantly affect the supply chain complexity. More product complexity and demand heterogeneity, faster production cycles, higher levels of automation and shorter supply paths are among the features of additive manufacturing that can directly influence the supply chain complexity. Comparison of additive manufacturing supply chain complexity to its traditional counterpart requires a profound comprehension of the transformative effects of AM on the supply chain. This paper first extracts the possible effects of AM on the supply chain and then tries to connect these effects to the drivers of complexity under three main categories of 1) market, 2) manufacturing technology, and 3) supply, planning and infrastructure. Possible impacts of additive manufacturing adoption on the supply chain complexity have been studied using information theoretic measures. An Agent-based Simulation (ABS) model has been developed to study and compare two different supply chain configurations. The findings of this study suggest that the adoption of AM can decrease the supply chain complexity, particularly when product customization is considered.

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