scholarly journals Stopping Accidents before They Happen: Perceiving Lane-Level Moving Vehicle Danger Regions to Warn Surrounding Drivers and Pedestrians

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Chi Guo ◽  
Guangyi Cao ◽  
Jieru Zeng ◽  
Jinsong Cui ◽  
Rong Peng
Keyword(s):  
Real Time ◽  
Large Scale ◽  
The Internet ◽  
Active Safety ◽  
Internet Of Vehicles ◽  
Moving Vehicle ◽  
Moving Vehicles ◽  
Traditional Service ◽  
New Type ◽  
New System

Perceiving the location of dangerous moving vehicles and broadcasting this information to vehicles nearby are essential to achieve active safety in the Internet of Vehicles (IOV). To address this issue, we implement a real-time high-precision lane-level danger region service for moving vehicles. A traditional service depends on static geofencing and fails to deal with dynamic vehicles. To overcome this defect, we devised a new type of IOV service that manages to track dangerous moving vehicles in real time and recognize their danger regions quickly and accurately. Next, we designed algorithms to distinguish the vehicles in danger regions and broadcast the information to these vehicles. Our system can simultaneously manipulate a mass of danger regions for various dangerous vehicles and broadcast this information to surrounding vehicles at a large scale. This new system was tested in Shanghai, Guangzhou, Wuhan, and other cities; the data analysis is presented in this paper as well.

scholarly journals Internet of Vehicles: Commencing Intellectual Hoarse Towards Self-Regulating Cars and Vehicular Clouds for Smart Transportation Structure [Vehicular Ad-Hoc Network: A Review and Application in the Internet of Vehicles]

2018 ◽  
Vol 7 (3.12) ◽  
pp. 545
Author(s):  
Risabh Mishra ◽  
M Safa ◽  
Aditya Anand
Keyword(s):  
Internet Of Things ◽  
Wireless Communication ◽  
Automobile Industry ◽  
Information Exchange ◽  
Large Scale ◽  
Intelligent Transportation System ◽  
Information Service ◽  
The Internet ◽  
Internet Of Vehicles ◽  
The Internet Of Things

Recent advances in wireless communication technologies and automobile industry have triggered a significant research interest in the field of Internet of Vehicles over the past few years.The advanced period of the Internet of Things is guiding the development of conventional Vehicular Networks to the Internet of Vehicles.In the days of Internet connectivity there is need to be in safe and problem-free environment.The Internet of Vehicles (IoV) is normally a mixing of three networks: an inter-vehicleNetwork, an intra-vehicle network, and a vehicle to vehicle network.Based on  idea of three networks combining into one, we define  Internet of Vehicles as a large-scale distributed system to wireless communication and information exchange between vehicle2X (X: vehicle, road, human and internet).It is a combined   network for supporting intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control, representation of an application of the Internet of Things (IoT) technology for intelligent transportation system (ITS).  

scholarly journals WHAT IT TAKES TO INNOVATE: THE EXPERIENCE OF PRODUCING AN ONLINE, REAL-TIME CASE STUDY

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
James Theroux
Keyword(s):  
Real Time ◽  
Teaching Methods ◽  
Recent Attempt ◽  
The Internet ◽  
Case Method ◽  
Business Courses ◽  
New Type ◽  
The One ◽  
Instantaneous Distribution

The case method can be classified as a type of experiential learning because students treat the problem in the case as if it were real and immediate. Until the Internet there was no practical way for cases to actually be real and immediate. The Internet makes possible instantaneous distribution of cases, and it makes possible their creation in real time. This article describes a recent attempt to use the Internet to bring business reality to business courses, and to facilitate communication among instructors, students, and the case company. It explores the challenges and difficulties involved in producing a new type of case study, and it assesses the feasibility of doing so on a regular basis. The goal of the author is to stimulate a dialog about how the Internet can be used to move forward all of our teaching methods, but especially the one that is prominent in schools of business: the case method.

Detecting Pressure Anomalies While Drilling Using a Machine Learning Hybrid Approach

2021 ◽  
Author(s):  
Aurore Lafond ◽  
Maurice Ringer ◽  
Florian Le Blay ◽  
Jiaxu Liu ◽  
Ekaterina Millan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Large Scale ◽  
Hybrid Approach ◽  
Physical Models ◽  
Training Data ◽  
Digital Data ◽  
Machine Learning Techniques ◽  
New System

Abstract Abnormal surface pressure is typically the first indicator of a number of problematic events, including kicks, losses, washouts and stuck pipe. These events account for 60–70% of all drilling-related nonproductive time, so their early and accurate detection has the potential to save the industry billions of dollars. Detecting these events today requires an expert user watching multiple curves, which can be costly, and subject to human errors. The solution presented in this paper is aiming at augmenting traditional models with new machine learning techniques, which enable to detect these events automatically and help the monitoring of the drilling well. Today’s real-time monitoring systems employ complex physical models to estimate surface standpipe pressure while drilling. These require many inputs and are difficult to calibrate. Machine learning is an alternative method to predict pump pressure, but this alone needs significant labelled training data, which is often lacking in the drilling world. The new system combines these approaches: a machine learning framework is used to enable automated learning while the physical models work to compensate any gaps in the training data. The system uses only standard surface measurements, is fully automated, and is continuously retrained while drilling to ensure the most accurate pressure prediction. In addition, a stochastic (Bayesian) machine learning technique is used, which enables not only a prediction of the pressure, but also the uncertainty and confidence of this prediction. Last, the new system includes a data quality control workflow. It discards periods of low data quality for the pressure anomaly detection and enables to have a smarter real-time events analysis. The new system has been tested on historical wells using a new test and validation framework. The framework runs the system automatically on large volumes of both historical and simulated data, to enable cross-referencing the results with observations. In this paper, we show the results of the automated test framework as well as the capabilities of the new system in two specific case studies, one on land and another offshore. Moreover, large scale statistics enlighten the reliability and the efficiency of this new detection workflow. The new system builds on the trend in our industry to better capture and utilize digital data for optimizing drilling.

Ground-Based Videometric Method and System for the Real-Time Measurement of Aircraft Landing Track

2014 ◽  
Vol 635-637 ◽  
pp. 824-831 ◽  
Author(s):  
Xiang Zhou ◽  
Zhi Hui Lei ◽  
Dan Fu ◽  
Xiao Hu Zhang
Keyword(s):  
Real Time ◽  
Global Positioning System ◽  
Large Scale ◽  
Time Measurement ◽  
Landing Point ◽  
The Real ◽  
Aircraft Landing ◽  
Real Time Measurement ◽  
Global Positioning ◽  
New Type

This paper proposes a ground-based videometric method and system for measuring the glide track of landing aircraft in real time. The proposed method is applicable for large-scale measurement via regional relays with multiple cameras. Its measurement ranges from kilometers away to the landing point, and it simultaneously fulfills the real-time measurement of the position and trajectory of aircraft. The real-time measurement result of the actual aircraft landing process shows a deviation from DGPS(Difference Global Positioning System) as small as 20 cm in the measuring region of 1 km. The proposed measurement method for aircraft landing track based on videometrics can establish a new type of landing aid system removed from radar and GPS.

Visual Detection of Moving Vehicles Ahead Based on the Characteristics

2011 ◽  
Vol 103 ◽  
pp. 165-169
Author(s):  
Hou Yun Yu ◽  
Wei Gong Zhang
Keyword(s):  
Machine Vision ◽  
Visual Detection ◽  
Region Of Interest ◽  
Safety System ◽  
Experimental Results ◽  
Active Safety ◽  
Moving Vehicle ◽  
Correct Information ◽  
Active Safety System ◽  
Moving Vehicles

Machine vision perception technology is widely used in the vehicle’s active safety system. It provides more immediate and correct information of road and vehicles around, in which inspection of moving vehicle ahead is one of the important items. A method of inspection fused of detection of the shadow under the vehicle and symmetry of the vehicle’s tail is presented in this paper. At first, a region of interest is selected according to the lane lines. Then, the shadow can be detected with grayscale histogram in the region of interest and a suspected area of vehicle is obtained by expanding the shadow with empirical proportion. At last, the vehicle ahead is further affirmed by calculating the symmetry of such characteristic at its tail as grayscale value, taillight and the edges. Experimental results prove that this method can well solve the actual problems of vehicle detection.

scholarly journals Data Anomaly Detection for Internet of Vehicles Based on Traffic Cellular Automata and Driving Style

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4926 ◽  
Author(s):  
Nan Ding ◽  
Haoxuan Ma ◽  
Chuanguo Zhao ◽  
Yanhua Ma ◽  
Hongwei Ge
Keyword(s):  
Cellular Automata ◽  
Anomaly Detection ◽  
Real Time ◽  
Safety Data ◽  
Gaussian Mixture ◽  
The Internet ◽  
Data Sets ◽  
Driving Style ◽  
Safe Driving ◽  
Internet Of Vehicles

The data validity of safe driving in the Internet of Vehicles (IoV) is the basis of improving the safety of vehicles. Different from a traditional information systems, the data anomaly analysis of vehicle safety driving faces the diversity of data anomaly and the randomness and subjectivity of the driver’s driving behavior. How to combine the characteristics of the IOV data with the driving style analysis to provide effective real-time anomaly data detection has become an important issue in the IOV applications. This paper aims at the critical safety data analysis, considering the large computing cost generated by the real-time anomaly detection of all data in the data package. We preprocess it through the traffic cellular automata model which is built to achieve the ideal abnormal detection effect with limited computing resources. On the basis of this model, the Anomaly Detection based on Driving style (ADD) algorithm is proposed to realize real-time and online detection of anomaly data related to safe driving. Firstly, this paper designs the driving coefficient and proposes a driving style quantization model to represent the driving style of the driver. Then, based on driving style quantization model and vehicle driving state information, a data anomaly detection algorithm is developed by using Gaussian mixture model (GMM). Finally, combining with the application scenarios of multi-vehicle collaboration in the Internet of Vehicles, this paper uses real data sets and simulation data sets to analyze the effectiveness of the proposed ADD algorithm.

scholarly journals Implementation for Comparison Analysis System of Used Transaction Using Big Data

2020 ◽  
Vol 12 (19) ◽  
pp. 8029
Author(s):  
Byungjoon Park ◽  
Hasung Kim ◽  
Byeongtae Ahn
Keyword(s):  
Big Data ◽  
Real Time ◽  
The Internet ◽  
Comparison Analysis ◽  
Transaction Data ◽  
Web Environment ◽  
Recent Increase ◽  
New Type ◽  
Analysis System ◽  
The Web

With the recent increase in used trading sites that support used trading, users want to find various information in real time, and the development of the Internet consists of direct and indirect connections between businesses and consumers. This change created a new type of C2C (Commerce to Commerce) transaction. However, each used trading site has its own characteristics, making it difficult to standardize one. Therefore, in this paper, we construed a system that provides the user’s used transaction data in real time and provides the desired information quickly. In this paper, we developed the crawler system needed to develop an integrated transaction system for second-hand goods through Internet e-commerce transactions, defined morphological analyzers, and described the service that users can employ in the web environment by using the system developed in the paper.

scholarly journals Real-Time Performance Evaluation of IEEE 802.11p EDCA Mechanism for IoV in a Highway Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hong Li ◽  
Qiong Wu ◽  
Jing Fan ◽  
Qiang Fan ◽  
Bo Chang ◽  
...  
Keyword(s):  
Real Time ◽  
Network Performance ◽  
Dynamic Network ◽  
Ieee 802.11P ◽  
The Internet ◽  
Public Infrastructure ◽  
Internet Of Vehicles ◽  
Time Performance ◽  
Time Requirements ◽  
The Internet Of Things

With the development of 5G, the Internet of Vehicles (IoV) evolves to be one important component of the Internet of Things (IoT), where vehicles and public infrastructure communicate with each other through a IEEE 802.11p EDCA mechanism to support four access categories (ACs) to access a channel. Due to the mobility of the vehicles, the network topology is time varying and thus incurs a dynamic network performance. There are many works on the stationary performance of 802.11p EDCA and some on real-time performance, but existing work does not consider real-time performance under extreme highway scenario. In this paper, we consider four ACs defined in the 802.11p EDCA mechanism to evaluate the limit of the real-time network performance in an extreme highway scenario, i.e., all vehicles keep the minimum safety distance between each other. The performance of the model has been demonstrated through simulations. It is found that some ACs can meet real-time requirements while others cannot in the extreme scenario.

scholarly journals Robustness of Internet of Battlefield Things (IoBT): A Directed Network Perspective

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1166
Author(s):  
Yuan Feng ◽  
Menglin Li ◽  
Chengyi Zeng ◽  
Hongfu Liu
Keyword(s):  
Real Time ◽  
Situation Awareness ◽  
Information Exchange ◽  
Large Scale ◽  
Network Connectivity ◽  
The Internet ◽  
Directed Network ◽  
Communication Link ◽  
Attack Behavior ◽  
Communication Links

Through the combination of various intelligent devices and the Internet to form a large-scale network, the Internet of Things (IoT) realizes real-time information exchange and communication between devices. IoT technology is expected to play an essential role in improving the combat effectiveness and situation awareness ability of armies. The interconnection between combat equipment and other battlefield resources is referred to as the Internet of Battlefield Things (IoBT). Battlefield real-time data sharing and the cooperative decision-making among commanders are highly dependent on the connectivity between different combat units in the network. However, due to the wireless characteristics of communication, a large number of communication links are directly exposed in the complex battlefield environment, and various cyber or physical attacks threaten network connectivity. Therefore, the ability to maintain network connectivity under adversary attacks is a critical property for the IoBT. In this work, we propose a directed network model and connectivity measurement of the IoBT network. Then, we develop an optimal attack strategy optimization model to simulate the optimal attack behavior of the enemy. By comparing with the disintegration effect of some benchmark strategies, we verify the optimality of the model solution and find that the robustness of the IoBT network decreases rapidly with an increase of the unidirectional communication links in the network. The results show that the adversary will change the attack mode according to the parameter settings of attack resources and network communication link density. In order to enhance the network robustness, we need to adjust the defense strategy in time to deal with this change. Finally, we validated the model and theoretical analysis proposed in this paper through experiments on a real military network.

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