scholarly journals High-Occupancy Vehicle Lane Enforcement System

2021 ◽  
Vol 15 (1) ◽  
pp. 194-200
Author(s):  
Jinhwan Jang
Keyword(s):  
Detection Error ◽  
Detection Capability ◽  
Real World Data ◽  
Core Element ◽  
Detection Model ◽  
Laser Sensors ◽  
Speed Measurement ◽  
Traffic Delays ◽  
Infrared Cameras ◽  
Hov Lanes

Introduction: An automatic High-Occupancy Vehicle (HOV) lane enforcement system is developed and evaluated. Current manual enforcement practices by the police bring about safety concerns and unnecessary traffic delays. Only vehicles with more than five passengers are permitted to use HOV lanes on freeways in Korea. Hence, detecting the number of passengers in HOVs is a core element for their development. Methods: For a quick detection capability, a YOLO-based passenger detection model was built. The system comprises three infrared cameras: two are for compartment detection and the other is for number plate recognition. Multiple infrared illuminations with the same frequency as the cameras and laser sensors for vehicle detection and speed measurement are also employed. Results: The performance of the developed system is evaluated with real-world data collected on proving ground. As a result, it showed a passenger detection error of nine percent on average. The performances revealed no difference in vehicle speeds and the number of passengers according to ANOVA tests. Conclusion: Using the developed system, more efficient and safer HOV lane enforcement practices can be made.

scholarly journals Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yerim Choi ◽  
Namyeon Kwon ◽  
Sungjun Lee ◽  
Yongwook Shin ◽  
Chuh Yeop Ryo ◽  
...  
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Military Technology ◽  
Real World Data ◽  
Accident Rate ◽  
Detection Model ◽  
Aerial Vehicles ◽  
Unmanned Combat Aerial Vehicles

With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators’ dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed.

scholarly journals A Framework for Anomaly Detection under Dynamic and Distributed Scenarios

2021 ◽  
Author(s):  
◽  
Murugaraj Odiathevar
Keyword(s):  
Anomaly Detection ◽  
Real World ◽  
Detection System ◽  
Traffic Monitoring ◽  
Streaming Data ◽  
Multiple Sources ◽  
Real World Data ◽  
Detection Model ◽  
Healthcare Data ◽  
Robust Model

<p><b>Anomaly Detection is an important aspect of many application domains. It refers to the problem of finding patterns in data that do not conform to expected behaviour. Hence, understanding of expected behaviour well is fundamental to performing effective anomaly detection. However, data profiles constantly evolve in certain domains such as computer networks. In other domains such as traffic monitoring and healthcare, data are distributed and are either too large or there are privacy concerns in transmitting them to a central location. These situations pose a challenge to obtain an accurate understanding of non-anomalous profiles. Changing profiles undermine existing anomaly detection models and make them less effective. Training a robust model with data from multiple sources is also challenging. Moreover, in real world scenarios, it is not apparent how an anomaly detection model can be built to address the problem.</b></p> <p>This thesis focuses on the building of a robust anomaly detection system where data profiles evolve and/or are distributed. It proposes a novel Online Offline Framework to separate existing expected behaviour, new possible expected behaviour and anomalies in streaming data. It also addresses the distributed scenario using a theoretically sound fully Bayesian approach. These methods improve performances of anomaly detection systems and work well with biased and uneven data partitions.</p> <p>The proposed methods are validated using real world data in three different domains. This thesis identifies the implementation difficulties in these domains and produces three novel methodologies to address each of the core anomaly detection problems.</p>

Simulation on Urban High Occupancy Vehicle Lanes - A Case Study in Nanjing

2014 ◽  
Vol 1065-1069 ◽  
pp. 3339-3342
Author(s):  
Ding Xin Wu ◽  
Wei Deng ◽  
Yan Song ◽  
Xin Luan
Keyword(s):  
Urban Areas ◽  
Queue Length ◽  
Cost Effective ◽  
Urban Transport ◽  
Traffic Delays ◽  
Bus Lane ◽  
Simulation Results ◽  
High Occupancy Vehicle Lanes ◽  
Hov Lanes

Dedicated bus lane (DBL) operation has been implemented in dozens of urban areas in China, and it is considered as one of the most efficient ways to solve the urban transport problem. Since the capacity of DBL is underutilized, it could be enhanced by allowing high occupancy vehicles (HOV) to use DBL lane. And this will turn DBL lane into HOV lane. However, HOV lanes are currently most used in freeways instead of urban areas especially in western countries. There is almost no HOV lane has been implemented in China nowadays.That is why research on HOV is worthy of attention. Simulation is risk-free and cost-effective way to evaluate the hypothetical implemented HOV lane. The hypothetical implemented HOV lane is located in Nanjing and evaluated using VISSIM. The simulation results shows that the HOV lane is suitable for urban areas and it can enhance speed of social vehicles with no significant effect on bus operation. At the same time, traffic delays and queue length of intersections are reduced.

scholarly journals Zone-based public transport route optimisation in an urban network

2020 ◽  
Author(s):  
Philipp Heyken Soares
Keyword(s):  
Real World ◽  
Public Transport ◽  
Hybrid Approach ◽  
Future Research ◽  
Real World Data ◽  
Core Element ◽  
Urban Network ◽  
Route Network ◽  
Input Dataset ◽  
Planning Processes

Abstract The majority of academic studies on the optimisation of public transport routes consider passenger trips to be fixed between pairs of stop points. This can lead to barriers in the use of the developed algorithms in real-world planning processes, as these usually utilise a zone-based trip representation. This study demonstrates the adaptation of a node-based optimisation procedure to work with zone-to-zone trips. A core element of this process is a hybrid approach to calculate zone-to-zone journey times through the use of node-based concepts. The resulting algorithm is applied to an input dataset generated from real-world data, with results showing significant improvements over the existing route network. The dataset is made publicly available to serve as a potential benchmark dataset for future research.

scholarly journals Towards an Optimization of Sample Plot Size and Scanner Position Layout for Terrestrial Laser Scanning in Multi-Scan Mode

Forests ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1099
Author(s):  
Tim Ritter ◽  
Christoph Gollob ◽  
Arne Nothdurft
Keyword(s):  
Detection Probability ◽  
Laser Scanning ◽  
Distance Sampling ◽  
Terrestrial Laser Scanning ◽  
Sample Plot ◽  
Imperfect Detection ◽  
Real World Data ◽  
Detection Model ◽  
Novel Approach ◽  
A Minor

A novel approach is presented to model the tree detection probability of terrestrial laser scanning (TLS) in forest inventory applications using a multi-scan mode. The traditional distance sampling framework is further extended to account for multiple scan positions at a single sample plot and to allow for an imperfect detection probability at distance r = 0. The novel methodology is tested with real world data, as well as in simulations. It is shown that the underlying detection model can be parameterized using only data from single scans. Hereby, it is possible to predict the detection probability also for different sample plot sizes and scanner position layouts in a multi-scan setting. Simulations showed that a minor discretization bias can occur if the sample size is small. The methodology enables a generalized optimization of the scanning layout in a multi-scan setting with respect to the detection probability and the sample plot area. This will increase the efficiency of multi-scan TLS-based forest inventories in the future.

scholarly journals Camouflage is NOT easy: Uncovering adversarial fraudsters in large online app review platform

2020 ◽  
pp. 002029402097021
Author(s):  
Jianyu Wang ◽  
Chunming Wu
Keyword(s):  
Urgent Problem ◽  
Real World Data ◽  
Convolutional Network ◽  
Data Set ◽  
Detection Model ◽  
Detection Systems ◽  
Small Set ◽  
Text Information ◽  
And Behavior ◽  
Fake Reviews

Given users and products that he/she reviews, can we recognize fake reviews just using the text information, or determine whether a reviewer is a fraud or not? Automatically detecting fake reviews and reviewers is an urgent problem and lots of work attempts for discovering linguistics, behaviors and graph patterns. However, in reality, there are new kinds of fraudsters who can change their behaviors to camouflage as genuine reviewers to avoid detection systems. With the fraudsters become distributed, dynamic, and adversarial, anti-spam tasks face a new challenge. In this paper, we tackle the challenge of adversarial fraudsters in online app review platform and propose a system called DDF (Detect, Defense, and Forecast) to uncover camouflage accounts. Firstly, we select a small set of seed with high-precision based on text and behavior features; Secondly, we build our graph-based detection model for uncovering hidden (distant) users who serve structurally similar to the seed by utilizing Graph Convolutional Network (GCN) algorithm. Thirdly, we evaluate DDF using real-world data set from Tencent APP Store and analyze the potential fraudsters detected by DDF. It is worth mentioning that precision can achieve 0.95+. Finally, we validate the efficiency and scalability of DDF and show that it can be well transferred to other anti-spam tasks.

scholarly journals A Framework for Anomaly Detection under Dynamic and Distributed Scenarios

2021 ◽  
Author(s):  
◽  
Murugaraj Odiathevar
Keyword(s):  
Anomaly Detection ◽  
Real World ◽  
Detection System ◽  
Traffic Monitoring ◽  
Streaming Data ◽  
Multiple Sources ◽  
Real World Data ◽  
Detection Model ◽  
Healthcare Data ◽  
Robust Model

<p><b>Anomaly Detection is an important aspect of many application domains. It refers to the problem of finding patterns in data that do not conform to expected behaviour. Hence, understanding of expected behaviour well is fundamental to performing effective anomaly detection. However, data profiles constantly evolve in certain domains such as computer networks. In other domains such as traffic monitoring and healthcare, data are distributed and are either too large or there are privacy concerns in transmitting them to a central location. These situations pose a challenge to obtain an accurate understanding of non-anomalous profiles. Changing profiles undermine existing anomaly detection models and make them less effective. Training a robust model with data from multiple sources is also challenging. Moreover, in real world scenarios, it is not apparent how an anomaly detection model can be built to address the problem.</b></p> <p>This thesis focuses on the building of a robust anomaly detection system where data profiles evolve and/or are distributed. It proposes a novel Online Offline Framework to separate existing expected behaviour, new possible expected behaviour and anomalies in streaming data. It also addresses the distributed scenario using a theoretically sound fully Bayesian approach. These methods improve performances of anomaly detection systems and work well with biased and uneven data partitions.</p> <p>The proposed methods are validated using real world data in three different domains. This thesis identifies the implementation difficulties in these domains and produces three novel methodologies to address each of the core anomaly detection problems.</p>

Abstract #946: Assessment of Acromegaly Patients with and Without Diabetes Treated with Lanreotide Depot: 2-Year Real World Data

2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  
Keyword(s):  
Real World ◽  
Real World Data ◽  
World Data

Safety and efficacy of direct acting oral anticoagulants and vitamin K antagonists in nonvalvular atrial fibrillation – a network meta-analysis of real-world data

VASA ◽  
2019 ◽  
Vol 48 (2) ◽  
pp. 134-147 ◽  
Author(s):  
Mirko Hirschl ◽  
Michael Kundi
Keyword(s):  
Real World ◽  
Meta Analysis ◽  
Vitamin K Antagonists ◽  
Oral Anticoagulants ◽  
Efficacy And Safety ◽  
Nonvalvular Atrial Fibrillation ◽  
Real World Data ◽  
Hazard Ratios ◽  
Direct Acting ◽  
Event Rates

Abstract. Background: In randomized controlled trials (RCTs) direct acting oral anticoagulants (DOACs) showed a superior risk-benefit profile in comparison to vitamin K antagonists (VKAs) for patients with nonvalvular atrial fibrillation. Patients enrolled in such studies do not necessarily reflect the whole target population treated in real-world practice. Materials and methods: By a systematic literature search, 88 studies including 3,351,628 patients providing over 2.9 million patient-years of follow-up were identified. Hazard ratios and event-rates for the main efficacy and safety outcomes were extracted and the results for DOACs and VKAs combined by network meta-analysis. In addition, meta-regression was performed to identify factors responsible for heterogeneity across studies. Results: For stroke and systemic embolism as well as for major bleeding and intracranial bleeding real-world studies gave virtually the same result as RCTs with higher efficacy and lower major bleeding risk (for dabigatran and apixaban) and lower risk of intracranial bleeding (all DOACs) compared to VKAs. Results for gastrointestinal bleeding were consistently better for DOACs and hazard ratios of myocardial infarction were significantly lower in real-world for dabigatran and apixaban compared to RCTs. By a ranking analysis we found that apixaban is the safest anticoagulant drug, while rivaroxaban closely followed by dabigatran are the most efficacious. Risk of bias and heterogeneity was assessed and had little impact on the overall results. Analysis of effect modification could guide the clinical decision as no single DOAC was superior/inferior to the others under all conditions. Conclusions: DOACs were at least as efficacious as VKAs. In terms of safety endpoints, DOACs performed better under real-world conditions than in RCTs. The current real-world data showed that differences in efficacy and safety, despite generally low event rates, exist between DOACs. Knowledge about these differences in performance can contribute to a more personalized medicine.

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