scholarly journals Estimating Network Flow Length Distributions via Bayesian Nonnegative Tensor Factorization

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Barış Kurt ◽  
Ali Taylan Cemgil ◽  
Güneş Karabulut Kurt ◽  
Engin Zeydan
Keyword(s):  
Network Flow ◽  
High Speed ◽  
Sampling Methods ◽  
Matrix Multiplication ◽  
Tensor Factorization ◽  
Real World Data ◽  
Data Set ◽  
Flow Length ◽  
Packet Sampling ◽  
True Flow

In this paper, we develop a framework to estimate network flow length distributions in terms of the number of packets. We model the network flow length data as a three-way array with day-of-week, hour-of-day, and flow length as entities where we observe a count. In a high-speed network, only a sampled version of such an array can be observed and reconstructing the true flow statistics from fewer observations becomes a computational problem. We formulate the sampling process as matrix multiplication so that any sampling method can be used in our framework as long as its sampling probabilities are written in matrix form. We demonstrate our framework on a high-volume real-world data set collected from a mobile network provider with a random packet sampling and a flow-based packet sampling methods. We show that modeling the network data as a tensor improves estimations of the true flow length histogram in both sampling methods.

scholarly journals Application of Nonnegative Tensor Factorization for Intercity Rail–Air Transport Supply Configuration Pattern Recognition

2019 ◽  
Vol 11 (6) ◽  
pp. 1803
Author(s):  
Han Zhong ◽  
Geqi Qi ◽  
Wei Guan ◽  
Xiaochen Hua
Keyword(s):  
Pattern Recognition ◽  
High Speed ◽  
Large Scale ◽  
Rapid Expansion ◽  
Air Transport ◽  
Tensor Factorization ◽  
Nonnegative Tensor ◽  
Data Set ◽  
Nonnegative Tensor Factorization ◽  
Supply Level

With the rapid expansion of the railway represented by high-speed rail (HSR) in China, competition between railway and aviation will become increasingly common on a large scale. Beijing, Shanghai, and Guangzhou are the busiest cities and the hubs of railway and aviation transportation in China. Obtaining their supply configuration patterns can help identify defects in planning. To achieve that, supply level is proposed, which is a weighted supply traffic volume that takes population and distance factors into account. Then supply configuration can be expressed as the distribution of supply level over time periods with different railway stations, airports, and city categories. Furthermore, nonnegative tensor factorization (NTF) is applied to pattern recognition by introducing CP (CANDECOMP/PARAFAC) decomposition and the block coordinate descent (BCD) algorithm for the selected data set. Numerical experiments show that the designed method has good performance in terms of computation speed and solution quality. Recognition results indicate the significant pattern characteristics of rail–air transport for Beijing, Shanghai, and Guangzhou are extracted, which can provide some theoretical references for practical policymakers.

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

2021 ◽  
pp. 036119812110098
Author(s):  
Miao Yu ◽  
Jinxing Shen ◽  
Changxi Ma
Keyword(s):  
High Speed ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Research Effort ◽  
Random Parameter ◽  
Policy Implications ◽  
Contributing Factors ◽  
Severe Injuries ◽  
Data Set ◽  
Severity Levels

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

Application of convolution neural network in medical image processing

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Medical Image ◽  
Sampling Methods ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Data Set ◽  
Convolution Kernels ◽  
Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

scholarly journals Time-ResNeXt for epilepsy recognition based on EEG signals in wireless networks

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shaoqiang Wang ◽  
Shudong Wang ◽  
Song Zhang ◽  
Yifan Wang
Keyword(s):  
Deep Learning ◽  
Network Structure ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Real World Data ◽  
Data Set ◽  
Practical Applications ◽  
Epilepsy Diagnosis ◽  
Single Data ◽  
Electroencephalogram Eeg

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

Rapid video assessment for monitoring testing facility fraud

2018 ◽  
Vol 35 (8) ◽  
pp. 1508-1518
Author(s):  
Rosembergue Pereira Souza ◽  
Luiz Fernando Rust da Costa Carmo ◽  
Luci Pirmez
Keyword(s):  
Processing Time ◽  
Threshold Value ◽  
Real World Data ◽  
Data Set ◽  
Content Type ◽  
Video Assessment ◽  
Technical Requirements ◽  
Testing Facility ◽  
Rapid Processing ◽  
Temporal Differencing

Purpose The purpose of this paper is to present a procedure for finding unusual patterns in accredited tests using a rapid processing method for analyzing video records. The procedure uses the temporal differencing technique for object tracking and considers only frames not identified as statistically redundant. Design/methodology/approach An accreditation organization is responsible for accrediting facilities to undertake testing and calibration activities. Periodically, such organizations evaluate accredited testing facilities. These evaluations could use video records and photographs of the tests performed by the facility to judge their conformity to technical requirements. To validate the proposed procedure, a real-world data set with video records from accredited testing facilities in the field of vehicle safety in Brazil was used. The processing time of this proposed procedure was compared with the time needed to process the video records in a traditional fashion. Findings With an appropriate threshold value, the proposed procedure could successfully identify video records of fraudulent services. Processing time was faster than when a traditional method was employed. Originality/value Manually evaluating video records is time consuming and tedious. This paper proposes a procedure to rapidly find unusual patterns in videos of accredited tests with a minimum of manual effort.

scholarly journals Forensic Engineering Analysis Of Bicycle Versus Pickup Collision

2010 ◽  
Vol 27 (1) ◽  
Author(s):  
Jerry S. Ogden
Keyword(s):  
High Speed ◽  
Engineering Analysis ◽  
Test Procedures ◽  
Data Set ◽  
Vehicle To Vehicle ◽  
Vehicle Collision ◽  
Vehicle Impact ◽  
General Test ◽  
Forensic Engineering ◽  
A Minor

The Forensic Engineering Analysis Of Bicycle-Vehicle Incidents Presents Its Own Unique Set Of Challenges. Often, The Forensic Engineer Is Faced With A Limited Data Set For Determining Vehicle Impact Speed From The Physical Evidence Produced By A Bicycle Collision With An Automobile, Which May Not Be Of Issue For A Vehicle-To-Vehicle Collision At Similar Speeds. This Paper Analyzes A Collision Between A Light Duty Pickup Pulling A Tandem Axle Utility Trailer And A Bicycle Ridden By A Minor Child. There Were Allegations That The Pickup Was Traveling At A High Speed Above The Speed Limit, As Well As Passing Another Vehicle At The Time Of The Incident. In Order To Accurately And Dependably Determine The Speed Of The Ford F350 Pickup Involved In This Incident Event, This Forensic Engineer Elected To Recreate The Vehicle Locked Wheel Skidding Evidence That Was Produced During The Incident Event And Photographically Recorded By Police Investigators. The Dynamic Skid Testing Technique, Test Equipment, And General Test Procedures Used To Accurately Determine Vehicle Speeds For This Incident Event, And How It Can Be Applied To Similar Collision Events Are Discussed In This Paper

scholarly journals Context-Aware Driver’s Behaviour Monitoring System in Vehicular Ad-Hoc Network

2021 ◽  
Vol 10 (2) ◽  
pp. 701-709
Keyword(s):  
Monitoring System ◽  
Ad Hoc Network ◽  
High Speed ◽  
Ad Hoc ◽  
Detection System ◽  
Vehicular Ad Hoc Network ◽  
Context Aware ◽  
Road Accidents ◽  
Road Transportation ◽  
Data Set

The number of deaths resulting from road accidents and mishaps has increased at an alarming rate over the years. Road transportation is the most popularly used means of transportation in developing countries like Nigeria and most of these road accidents are associated with reckless driving habits. Context-aware systems provide intelligent recommendations allowing digital devices to make correct and timely recommendations when required. Furthermore, in a Vehicular Ad-hoc Network (VANET), communication links between vehicles and roadside units are improved thus enabling vehicle and road safety. Hence, a non-intrusive driver behaviour detection system that incorporates context-aware monitoring features in VANET is proposed in this study. By making use of a one-dimensional highway (1D) road with one-way traffic movement and incorporating GSM technology, irregular actions (high speed, alcohol while driving, and pressure) exhibited by drivers are monitored and alerts are sent to other nearby vehicles and roadside units to avoid accidents. The proposed system adopted a real-time VANET prototype with three entities involved in the context-aware driver’s behaviour monitoring system namely, the driver, vehicle, and environment. The analytical tests with actual data set indicate that, when detected, the model measures the pace of the vehicle, the level of alcohol in the breath, and the driver's heart rate in-breath per minute (BPM). Therefore, it can be used as an appropriate model for the Context-aware driver’s monitoring system in VANET.

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