scholarly journals ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jingming Xia ◽  
Dawei Xuan ◽  
Ling Tan ◽  
Luping Xing
Keyword(s):  
Intelligent Transportation System ◽  
Rapid Development ◽  
Weather Condition ◽  
Network Models ◽  
Weather Conditions ◽  
Severe Weather ◽  
Urban Traffic ◽  
Deep Convolutional Neural Networks ◽  
Traffic Condition ◽  
Softmax Classifier

Severe weather conditions will have a great impact on urban traffic. Automatic recognition of weather condition has important application value in traffic condition warning, automobile auxiliary driving, intelligent transportation system, and other aspects. With the rapid development of deep learning, deep convolutional neural networks (CNN) are used to recognize weather conditions on traffic road. A new simplified model named ResNet15 is proposed based on the residual network ResNet50 in this paper. The convolutional layers of ResNet15 are utilized to extract weather characteristics, and then the characteristics extracted at the previous layer are shortcut to the next layer through four groups of residual modules. Finally, the weather images are classified and recognized through the fully connected layer and Softmax classifier. In addition, we build a medium-scale dataset of weather images on traffic road, called “WeatherDataset-4,” which consists of 4 categories and contains 4983 weather images covering most of the severe weather. In this paper, ResNet15 is used to train and test on the “WeatherDataset-4,” and desirable recognition results are obtained. The evaluation of a large number of experiments demonstrates that the proposed ResNet15 is superior to traditional network models such as ResNet50 in recognition accuracy, recognition speed, and model size.

scholarly journals Terminal Airspace Capacity Evaluation Model under Weather Condition from Perspective of a Controller

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Lu Liu
Keyword(s):  
Network Flow ◽  
Topological Structure ◽  
Evaluation Model ◽  
Weather Condition ◽  
Weather Conditions ◽  
Severe Weather ◽  
Safe Operation ◽  
Weather Factor ◽  
Route Network ◽  
Capacity Evaluation

A route network lays in the terminal airspace. The route network can be divided into multiple subnetworks according to sectors. When severe weather conditions occur, a controller takes measures to obtain safe operation of flights, such as navigation guidance or changing the availability of routes. In such circumstances, the route structure of a subnetwork is changed, and the controller’s attention paid to each route is also changed as well as the unit workload on it. As the subnetwork is handled by one controller, capacities of routes in it are associated. We find the way to determine the “related capacity” of a route in the conditions that whether topological structure of the terminal route network is changed or not. The capacity of the terminal route network calculated by network flow theory represents the capacity of terminal airspace. According to the analysis results, the weather factor reduces capacity of terminal airspace directly by reducing the capacities of routes blocked. Indirectly, it diverts controller’s attention to change capacities of other routes in the subnetwork.

Field Test of Nonintrusive Traffic Detection Technologies

1998 ◽  
Vol 1643 (1) ◽  
pp. 161-170 ◽  
Author(s):  
Stephen J. Bahler ◽  
James M. Kranig ◽  
Erik D. Minge
Keyword(s):  
Field Test ◽  
Intelligent Transportation System ◽  
General System ◽  
Weather Conditions ◽  
Test Site ◽  
Urban Traffic ◽  
Minimal Impact ◽  
Traffic Conditions ◽  
Traffic Detection ◽  
Detection Technologies

The results of a 2-year field test of nonintrusive traffic detection technologies are presented. Seventeen devices representing the following eight technologies were evaluated: passive infrared, active infrared, magnetic, radar, doppler microwave, pulse ultrasonic, passive acoustic, and video. The devices were tested in a variety of environmental and traffic conditions at both intersection and freeway test sites. Emphasis was placed on urban traffic conditions, such as heavy congestion; locations that typify temporary counting situations, such as 48-hour or peak hour counts; and performance in the wide variety of weather conditions found in Minnesota. The evaluation also focused on the ease of system set-up and general system reliability. The results show that nonintrusive technologies are capable of performing as well as conventional methods in some, but not all, situations. At the freeway test site, most nonintrusive devices counted within 3 percent of baseline data. At the intersection test site, however, congested stop-and-go traffic hindered the performance of the majority of the devices. Weather and other environmental variables were found to have minimal impact on the majority of devices. This test is the first phase of an ongoing project to evaluate new, nonintrusive technologies and devices. Further research will expand into areas such as real-time datacollection to support intelligent transportation system applications.

scholarly journals Modelling and Analysis of IoT Technology Using Neural Networks In Agriculture Environment

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Emre Özbilge ◽  
Yönal Kırsal ◽  
Ersin Çaglar
Keyword(s):  
Neural Networks ◽  
Internet Of Things ◽  
Network Model ◽  
Rapid Development ◽  
Weather Condition ◽  
Weather Conditions ◽  
Research Area ◽  
Extreme Weather ◽  
Learning System ◽  
Agricultural Environment

The rapid development of internet, cloud computing and sensor networks lead to develop and deploy the Internet of Things (IoT) which is a hot topic for the researchers. It has started to be used in various areas. Thus, agriculture is one of the most popular IoT research area. In agriculture environment, farming platform area is being a huge open structure and farmers must protect the crops from extreme weather conditions namely; wind speed/direction, precipitation, air temperature, solar radiations, and relative humidity etc. These extreme weather conditions effect crops and farms very significantly. But with the benefits of Internet of Things technologies, an agriculture business become more easy and efficient despite extreme weather conditions. This paper provides a model of smart agriculture environment using neural networks that helps the farmers to make more accurate predictions for the future according to weather conditions. This paper proposed a time-delay radial basis function (TDRBF) network approach to model temporal and sequential relationship between the various weather condition sensor readings from the agricultural environment. The performance of the acquired network model was analysed statistically and presented in this paper. As a result, the results of the neural network model show that it could be used to predict the desired weather condition sensor readings beforehand in order to increase the productivity in agricultural environment and also it is possible that by using such an intelligent learning system could provide a life-long learning for the changing weather conditions in the farming area over the years.

Toward the Development of Weather-Dependent Microsimulation Models

2019 ◽  
Vol 2673 (7) ◽  
pp. 143-156 ◽  
Author(s):  
Britton E. Hammit ◽  
Rachel James ◽  
Mohamed Ahmed ◽  
Rhonda Young
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Optimal Parameter ◽  
Weather Condition ◽  
Weather Conditions ◽  
Model Parameters ◽  
Car Following ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Microsimulation Models

Adverse weather conditions severely affect transportation networks. Decades of research have been dedicated to analyzing these impacts and developing countermeasures to reduce their negative effects on travelers and infrastructure. Recent developments in technology have enabled the introduction of intelligent transportation system applications used for network planning, safety assessments, countermeasure evaluation, and roadway operations. One such application is microsimulation modeling, which is a powerful tool used to emulate traffic flow. Agencies are interested in using microsimulation to forecast the effects on safety and mobility of adverse weather conditions; however, there is limited knowledge on how to calibrate the model to reflect different weather conditions. This paper contributes a methodology for calibrating car-following behavior required for successful development of microsimulation models. This research was completed using SHRP2 Naturalistic Driving Study (NDS) data to capture realistic driving behavior in a variety of weather conditions. This study has two primary objectives. First, calibrate the Wiedemann 1999 car-following model for a subset of NDS trips, cluster trips with similar weather conditions, and identify an optimal parameter set to represent that condition. Second, apply the optimal model parameters in a realistic microsimulation network to assess the predicted traffic flow in each weather condition. Findings support the hypothesis that the calibration of driving models for use in microsimulation results in more realistic estimations of traffic flow. Moreover, this research illustrates that the use of high resolution trajectory-level data can successfully capture weather-dependent driving behaviors.

Train detection and classification Using distributed Fiber optic acoustic sensing

2021 ◽  
pp. 1-46
Author(s):  
Yunpeng Cai ◽  
Jihui Ma ◽  
Xu Tuanwei ◽  
Wenfa Yan
Keyword(s):  
Fiber Optic ◽  
Rapid Development ◽  
Weather Conditions ◽  
Severe Weather ◽  
Petroleum Exploration ◽  
Support Vector ◽  
Long Distance ◽  
Acoustic Sensing ◽  
Detection Techniques ◽  
The Impact

With the rapid development of the high-speed railway industry, train detection and identification play a vital role in capacity improvement and safe operation in railway systems. Conventional detection methods such as track circuit and axle counting tend to be interfered with by severe weather conditions and irrelevant conductive objects, leading to false detections. Fiber-optic distributed acoustic sensing (DAS) technology is a prevailing sensing method in geophysics research, petroleum exploration, and structure inspection. Compared to traditional detection techniques, DAS is suitable for long-distance detection and is resistant to severe weather conditions and electrical interference. We have developed a train detection and classification system using DAS technology and have explored an effective classification method for train identification. Specifically, we conduct a field experiment by the side of a railroad over viaducts and the data are collected with the DAS detection system. To eliminate the impact of background noise, DC noise, and motor vehicle signals from the original data, we adopt a wavelet denoising method and Chebyshev filter to extract the features of three types of train signals. The vibration signals of these different trains indicate remarkable cyclical variations related to the number of wheelsets in the time domain and have similar narrow-band discrete spectrums with different characteristic peak frequencies. Furthermore, based on the features of the train signals, we select a support vector machine classifier to identify three types of trains, with accuracy greater than 97%.

scholarly journals Calculating the Contribution Rate of Intelligent Transportation System in Improving Urban Traffic Smooth Based on Advanced DID Model

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ming-wei Li ◽  
Jun Yun ◽  
Na Liu
Keyword(s):  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Rapid Development ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Urban Traffic ◽  
Evaluation Index System ◽  
Capital City ◽  
Contribution Rate ◽  
Urban City

Recent years have witnessed the rapid development of intelligent transportation system around the world, which helps to relieve urban traffic congestion problems. For instance, many mega-cities in China have devoted a large amount of money and resources to the development of intelligent transportation system. This poses an intriguing and important issue: how to measure and quantify the contribution of intelligent transportation system to the urban city, which is still a puzzle. This paper proposes a matching difference-in-difference model to calculate the contribution rate of intelligent transportation system on traffic smoothness. Within the model, the main effect indicators of traffic smoothness are first identified, and then the evaluation index system is built, and finally the ideas of the matching pool are introduced. The proposed model is illustrated in Guangzhou, China (capital city of Guangdong province). The results show that introduction of ITS contributes 9.25% to the improvement of traffic smooth in Guangzhou. Also, the research explains the working mechanism of how ITS improves urban traffic smooth. Eventually, some strategy recommendations are put forward to improve urban traffic smooth.

scholarly journals An investigation of the effects of environmental and ecologic factors on cutaneous leishmaniasis in the old world: a systematic review study

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolfazl Mohammadbeigi ◽  
Salman Khazaei ◽  
Hamidreza Heidari ◽  
Azadeh Asgarian ◽  
Shahram Arsangjang ◽  
...  
Keyword(s):  
Systematic Review ◽  
Environmental Factors ◽  
Land Cover ◽  
Cutaneous Leishmaniasis ◽  
Weather Condition ◽  
Weather Conditions ◽  
Eligibility Criteria ◽  
Screening Process ◽  
Old World ◽  
Review Study

AbstractObjectivesLeishmaniasis is a neglected and widespread parasitic disease that can lead to serious health problems. The current review study aimed to synthesize the relationship between ecologic and environmental factors (e.g., weather conditions, climatology, temperature and topology) and the incidence of cutaneous leishmaniasis (CL) in the Old World.ContentA systematic review was conducted based on English, and Persian articles published from 2015 to 2020 in PubMed/Medline, Science Direct, Web of Science and Google Scholar. Keywords used to search articles were leishmaniasis, environmental factors, weather condition, soil, temperature, land cover, ecologic* and topogr*. All articles were selected and assessed for eligibility according to the titles or abstracts. The quality screening process of articles was carried out by two independent authors. The selected articles were checked according to the inclusion and exclusion criteria.Summary and outlookA total of 827 relevant records in 2015–2020 were searched and after evaluating the articles, 23 articles met the eligibility criteria; finally, 14 full-text articles were included in the systematic review. Two different categories of ecologic/environmental factors (weather conditions, temperature, rainfall/precipitation and humidity) and land characteristics (land cover, slope, elevation and altitude, earthquake and cattle sheds) were the most important factors associated with CL incidence.ConclusionsTemperature and rainfall play an important role in the seasonal cycle of CL as many CL cases occurred in arid and semiarid areas in the Old World. Moreover, given the findings of this study regarding the effect of weather conditions on CL, it can be concluded that designing an early warning system is necessary to predict the incidence of CL based on different weather conditions.

scholarly journals It’s the Weather: Quantifying the Impact of Weather on Retail Sales

2021 ◽  
Author(s):  
Natalie Rose ◽  
Les Dolega
Keyword(s):  
Weather Condition ◽  
Weather Conditions ◽  
Influential Factor ◽  
Retail Sector ◽  
Retail Sales ◽  
Weather Impact ◽  
High Street ◽  
Sector Decision ◽  
The Impact ◽  
The Relationship

AbstractThe weather is considered as an influential factor on consumer purchasing behaviours and plays a significant role in many aspects of retail sector decision making. As a result, better understanding of the magnitude and nature of the influence of variable UK weather conditions can be beneficial to many retailers and other stakeholders. This study addresses the dearth of research in this area by quantifying the relationship between different weather conditions and trading outcomes. By employing comprehensive daily sales data for a major high street retailer with over 2000 stores across England and adopting a random forest methodology, the study quantifies the influence of various weather conditions on daily retail sales. Results indicate that weather impact is greatest in the summer and spring months and that wind is consistently found to be the most influential weather condition. The top five most weather-dependent categories cover a range of different product types, with health foods emerging as the most susceptible to the weather. Also, sales from out-of-town stores show a far more complex relationship with the weather than those from traditional high street stores with the regions London and the South East experiencing the greatest levels of influence. Various implications of these findings for retail stakeholders are discussed and the scope for further research outlined.

scholarly journals Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1263
Author(s):  
Zhaojun Wang ◽  
Jiangning Wang ◽  
Congtian Lin ◽  
Yan Han ◽  
Zhaosheng Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Digital Technology ◽  
Rapid Development ◽  
Image Data ◽  
Image Database ◽  
Test Accuracy ◽  
Practical Application ◽  
Accuracy Rate ◽  
Deep Convolutional Neural Networks

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

Key IOT Technology and Application Research

2014 ◽  
Vol 543-547 ◽  
pp. 3411-3414
Author(s):  
Xu Bing
Keyword(s):  
Wireless Sensor Network ◽  
Internet Of Things ◽  
Intelligent Transportation System ◽  
Rapid Development ◽  
Intelligent Transportation ◽  
Wireless Sensor ◽  
Medical Monitoring ◽  
Application Research ◽  
Iot Applications ◽  
Architectural Model

Key core technologies of IOT (internet of things) have to be addressed to achieve rapid development. This paper focused on studying RFID, wireless sensor network (WSN) and TCCP which were integrated to address the IOT application problems. Meanwhile, an IOT architectural model was established and the IOT applications in real-time medical monitoring, intelligent transportation system (ITS), intelligent appliances and intelligent agriculture were introduced.

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