scholarly journals ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks

2022 ◽  
Vol 15 ◽  
Author(s):  
Patrick Herbers ◽  
Iago Calvo ◽  
Sandra Diaz-Pier ◽  
Oscar D. Robles ◽  
Susana Mata ◽  
...  
Keyword(s):  
Neural Networks ◽  
Visual Representation ◽  
Visual Language ◽  
Use Cases ◽  
Symbolic Language ◽  
The Road ◽  
Visual Model ◽  
Front End ◽  
On The Road ◽  
Structural Levels

An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels—ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce the features and possibilities of our proposed visual language and associated workflow. We demonstrate that ConGen enables the creation, editing, and visualization of multiscale biological neural networks and provides a whole workflow to produce simulation scripts from the visual representation of the model.

Study of Highway Crack Diagnosis Based on Cellular Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 2013-2017
Author(s):  
Sheng Zhuo Yao ◽  
Guo Dong Li ◽  
Fu Xin Zhang ◽  
Lin Ge
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Quality Information ◽  
Image Detection ◽  
The Road ◽  
Standard Level ◽  
On The Road

Road quality information detect system is an important component in architecture quality detect system, also is the basement of successfully working of other related project for the whole country. The study of detecting the road crack is the key to insure the security of accurately detect the road quality in transportation system. In this paper, we come up with a fixed way of road undersized rift image detection by using cellular neural networks. By image processing, building rift networks and details networks and adding the model of similarity undersized rift networks. It can avoid the problem that can not accurately detect undersized crack by only taking the crack feature value. The experiment proved that fixed crack detect computing is easy to do, more accurate to detect the undersized cracks on the road and can reach the standard level of current detect technique.

scholarly journals OPTIMIZATION OF MANAGEMENT OF URBAN LIGHTS WITH THE USE OF NEURAL NETWORKS

THE BULLETIN ◽  
2021 ◽  
Vol 389 (1) ◽  
pp. 14-17
Author(s):  
A.А. Suleimen ◽  
G.B. Kashaganova ◽  
G.B. Issayeva ◽  
B.R. Absatarova ◽  
M.C. Ibraev
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Traffic Management ◽  
Urban Traffic ◽  
Control Parameters ◽  
Traffic Flows ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
On The Road

One of the most pressing problems of large cities is the problem of traffic management of vehicles. The reason for this problem is an imperfect way to manage traffic flows. Traffic light regulation is of particular importance in traffic management. Most modern traffic light control systems operate at set time intervals and are not able to cope with the constantly changing situation on the road. A promising direction for solving this problem is to optimize the system using artificial neural networks. The advantage of neural networks is self-learning, which allows the system to adapt to the changing situation on the road. Despite numerous attempts, it has not yet been possible to obtain a high-quality mathematical model of urban traffic management. This model should determine the functional dependence of transport flow parameters on control parameters. Nowadays, traffic flows are regulated everywhere by means of traffic lights. If we can get a fairly accurate mathematical model of traffic flows, we can determine the optimal duration of the traffic signal phases to achieve the maximum capacity of the road network node. A fairly accurate mathematical model of traffic management that works in predictive mode will display an estimate of the optimal control parameters, as well as make correct decisions in emergency situations. Well-known mathematical models of road traffic take into account only the average values of traffic flows, and not the exact number of cars on each road section at a particular time.

scholarly journals Neural networks applications for environmental safety assessment of roads in Ukraine

2014 ◽  
Vol 13 (1) ◽  
pp. 339-347
Author(s):  
Vitalii Naumov ◽  
Natalia Vnukova ◽  
Ganna Zhelnovach
Keyword(s):  
Neural Networks ◽  
Numerical Evaluation ◽  
Safety Assessment ◽  
Environmental Safety ◽  
Fuzzy Information ◽  
Safety Level ◽  
The Road ◽  
On The Road

The analysis of problems and of approaches for ensuring the environmental safety of roads in Ukraine has been performed. The proposed mathematical model on the basis of neural networks allows numerical evaluation of quality of road area in the conditions of incomplete and fuzzy information. The proposed approach allows the determination of roads’ environmental safety level, the indica­tion of necessity for arrangement of environmental monitoring stations, and allows the development of a number of activities for environmental protection on the road sections as well.

scholarly journals RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

2020 ◽  
Vol 34 (07) ◽  
pp. 10965-10972
Author(s):  
Songtao He ◽  
Favyen Bastani ◽  
Satvat Jagwani ◽  
Edward Park ◽  
Sofiane Abbar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Receptive Field ◽  
Spatial Correlation ◽  
Satellite Imagery ◽  
Road Network ◽  
Network Graph ◽  
The Road ◽  
Road Type ◽  
On The Road ◽  
Graph Neural Networks

Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of road attributes, a road attribute at one position on a road may only be apparent when considering far-away segments of the road. Thus, to robustly infer road attributes, the model must integrate scattered information and capture the spatial correlation of features along roads. Existing solutions that rely on image classifiers fail to capture this correlation, resulting in poor accuracy. We find this failure is caused by a fundamental limitation – the limited effective receptive field of image classifiers.To overcome this limitation, we propose RoadTagger, an end-to-end architecture which combines both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to infer road attributes. Using a GNN allows information to propagate on the road network graph and eliminates the receptive field limitation of image classifiers. We evaluate RoadTagger on both a large real-world dataset covering 688 km2 area in 20 U.S. cities and a synthesized dataset. In the evaluation, RoadTagger improves inference accuracy over the CNN image classifier based approaches. In addition, RoadTagger is robust to disruptions in the satellite imagery and is able to learn complicated inductive rules for aggregating scattered information along the road network.

scholarly journals Towards the Automatic Mathematician

2021 ◽  
pp. 25-37
Author(s):  
Markus N. Rabe ◽  
Christian Szegedy
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Mathematical Reasoning ◽  
The Road ◽  
Fine Grained ◽  
Recent Developments ◽  
On The Road

AbstractOver the recent years deep learning has found successful applications in mathematical reasoning. Today, we can predict fine-grained proof steps, relevant premises, and even useful conjectures using neural networks. This extended abstract summarizes recent developments of machine learning in mathematical reasoning and the vision of the N2Formal group at Google Research to create an automatic mathematician. The second part discusses the key challenges on the road ahead.

scholarly journals Neural Networks Applied to Microsimulation: A Prediction Model for Pedestrian Crossing Time

2020 ◽  
Vol 12 (13) ◽  
pp. 5355
Author(s):  
Chiara Gruden ◽  
Irena Ištoka Otković ◽  
Matjaž Šraml
Keyword(s):  
Neural Networks ◽  
Original Form ◽  
Model Parameters ◽  
Pedestrian Behavior ◽  
Calibration Process ◽  
The Road ◽  
Crossing Time ◽  
The Difference ◽  
On The Road ◽  
Pedestrian Crossing

Walking is the original form of transportation, and pedestrians have always made up a significant share of transportation system users. In contrast to motorized traffic, which has to move on precisely defined lanes and follow strict rules, pedestrian traffic is not heavily regulated. Moreover, pedestrians have specific characteristics—in terms of size and protection—which make them much more vulnerable than drivers. In addition, the difference in speed between pedestrians and motorized vehicles increases their vulnerability. All these characteristics, together with the large number of pedestrians on the road, lead to many safety problems that professionals have to deal with. One way to tackle them is to model pedestrian behavior using microsimulation tools. Of course, modeling also raises questions of reliability, and this is also the focus of this paper. The aim of the present research is to contribute to improving the reliability of microsimulation models for pedestrians by testing the possibility of applying neural networks in the model calibration process. Pedestrian behavior is culturally conditioned and the adaptation of the model to local specifics in the calibration process is a prerequisite for realistic modeling results. A neural network is formulated, trained and validated in order to link not-directly measurable model parameters to pedestrian crossing time, which is given as output by the microsimulation tool. The crossing time of pedestrians passing the road on a roundabout entry leg has been both simulated and calculated by the network, and the results were compared. A correlation of 94% was achieved after both training and validation steps. Finally, tests were performed to identify the main parameters that influence the estimated crossing time.

scholarly journals Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5501 ◽  
Author(s):  
Chanjun Chun ◽  
Seung-Ki Ryu
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Supervised Learning ◽  
Convolutional Neural Networks ◽  
Surface Damage ◽  
Road Surface ◽  
Training Dataset ◽  
The Road ◽  
Fully Convolutional Neural Networks ◽  
On The Road

The various defects that occur on asphalt pavement are a direct cause car accidents, and countermeasures are required because they cause significantly dangerous situations. In this paper, we propose fully convolutional neural networks (CNN)-based road surface damage detection with semi-supervised learning. First, the training DB is collected through the camera installed in the vehicle while driving on the road. Moreover, the CNN model is trained in the form of a semantic segmentation using the deep convolutional autoencoder. Here, we augmented the training dataset depending on brightness, and finally generated a total of 40,536 training images. Furthermore, the CNN model is updated by using the pseudo-labeled images from the semi-supervised learning methods for improving the performance of road surface damage detection technique. To demonstrate the effectiveness of the proposed method, 450 evaluation datasets were created to verify the performance of the proposed road surface damage detection, and four experts evaluated each image. As a result, it is confirmed that the proposed method can properly segment the road surface damages.

scholarly journals Road Pothole Detection using Deep Learning Classifiers

2020 ◽  
Vol 8 (6) ◽  
pp. 563-570 ◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Data Collection ◽  
Convolutional Neural Networks ◽  
Road Maintenance ◽  
The Road ◽  
Wear And Tear ◽  
On The Road ◽  
Maintenance Process ◽  
Pothole Detection

Pothole is one of the major types of defects frequently found on the road whose assessment is necessary to process. It is one of the important reason of accidents on the road along with the wear and tear of vehicles. Road defects assessment is to be done through defects data collection and processing of this collected data. Currently, using various types of imaging systems data collection is near about becomes automated but an assessment of defects from collected data is still manual. Manual classification and evaluation of potholes are expensive, labour-intensive, time-consuming and thus slows down the overall road maintenance process. This paper describe a method for classification and detection of the potholes on road images using convolutional neural networks which are deep learning algorithms. In the proposed system we used convolutional neural networks based approach with pre-trained models to classify given input images into a pothole and non-pothole categories. The method was implemented in python using OpenCV library under windows and colab environment, trained on 722 and tested on 116 raw images. The results are evaluated and compared for convolutional neural networks and various seven pre-trained models through accuracy, precision and recall metrics. The results show that pre-trained models InseptionResNetV2 and DenseNet201 can detect potholes on road images with reasonably good accuracy of 89.66%.

Community Colleges: Vital Partners on the Road to Diversity

ASHA Leader ◽  
2006 ◽  
Vol 11 (5) ◽  
pp. 14-17 ◽  
Author(s):  
Shelly S. Chabon ◽  
Ruth E. Cain
Keyword(s):  
Community Colleges ◽  
The Road ◽  
On The Road
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