Reservoir space and enrichment model of shale oil in the first member of Cretaceous Qingshankou Formation in the Changling Sag, southern Songliao Basin, NE China

Traffic classification, a branch of passive network measurement, becomes more and more important for network management. As traditional traffic classification techniques like port-based and payload-based techniques become ineffective for complicated internet applications which use dynamic port number and encryption techniques to avoid detection, machine learning based techniques gained more and more attentions in the past few years. But there are few studies that focus on applying neural computation techniques for traffic classification. In this paper, we use a distributed host based traffic collection platform (DHTCP) to gather traffic samples with accurate application information on user hosts. Then probabilistic neural network was used to traffic classification. Web and P2P traffics were studied since they are the most predominant internet traffic types. experimental results show that probabilistic neural network is an effective machine learning technique for traffic identification

Traffic classification using probabilistic neural networks

Traffic classification acquired the interest of the Internet community early on Different approaches have been proposed to classify Internet traffic to manage both security and Quality of Service (QoS) However, traditional classification approaches consisting of modifying the Transmission Control Protocol/Internet Protocol (TCP/IP) scheme have not been adopted due to their complex management In addition, port-based methods and deep packet inspection have limitations in dealing with new traffic characteristics (eg, dynamic port allocation, tunneling, encryption) Conversely, machine learning (ML) solutions effectively classify traffic down to the device type and specific user action Another research direction aims to anonymize Internet traffic and thwart classification to maintain user privacy Existing traffic surveys focus on classification and do not consider anonymization Here, we review the Internet traffic classification and obfuscation techniques, largely considering the ML-based solutions In addition, this paper presents a comprehensive review of various data representation methods, and the different objectives of Internet traffic classification Finally, we present the key findings, limitations, and recommendations for future research

A review on machine learning–based approaches for Internet traffic classification

In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments.

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Context-Based Energy Disaggregation in Smart Homes

In modern networks, different applications generate various traffic types with diverse service requirements. Thereby the identification and classification of traffic play an important role for increasing the performance in network management. Primitive applications were using well-known ports in transport layer, so their traffic classification can be performed based on the port number. However, the recent applications progressively use unpredictable port numbers. Consequently the later methods are based on "deep packet inspection". Notwithstanding proper accuracy, these methods impose heavy operational load and are vulnerable to encrypted flows. The recent methods classify the traffic based on statistical packet characteristics. However, having access to a little part of statistical flow information in real-time traffic may jeopardize the performance of these methods. Regarding the advantages and disadvantages of the two later methods, in this paper we propose an approach based on payload content and statistical traffic characteristics with Naive Bayes algorithm for real-time network traffic classification. The performance and low complexity of the propose approach confirm its competency for real-time traffic classification.

Real-Time Traffic Classification Based on Statistical and Payload Content Features

With a dramatic increase in the number and variety of applications running over the internet, it is very important to be capable of dynamically identifying and classifying flows/traffic according to their network applications. Meanwhile, internet application classification is fundamental to numerous network activities. In this paper, we present a novel methodology for identifying different internet applications. The major contributions are: (1) we propose a Gaussian mixture model (GMM)-based semi-supervised classification system to identify different internet applications; (2) we achieve an optimum configuration for the GMM-based semi-supervised classification system. The effectiveness of these proposed approaches is demonstrated through experimental results.

Semi-supervised internet network traffic classification using a Gaussian mixture model

Seismic fault detection based on 3D Unet++ model

The invention discloses a space curved surface reconstruction method based on a boundary constraint. The space curved surface reconstruction method based on the boundary constraint mainly comprises the steps of (1) collecting original horizon data points and fault data points, and drawing a hanging wall polygon and a heading wall polygon; (2) obtaining height attributes, relation attributes and projection attributes of the original horizon data points and points on the fault polygons; (3) carrying out connection on the original horizon data points and the points of the fault polygons through triangle gridding with the constraint; (4) accomplishing splicing of internal areas and external areas of the hanging wall polygon and the heading wall polygon, namely, accomplishing reconstruction of a three-dimensional space curved surface. The space curved surface reconstruction method based on the boundary constraint solves the problem of reconstruction of the horizon curved surface of the complex terrain in the process of three-dimensional geologic modeling, provides a new idea for isoline drawing, geological block forming and the like, can be applicable to various discrete points or lateral line data, and low in requirement for data, can support various faults such as complex terrain structures such as normal faults, reverse faults and vertical faults, and has good adaptability.

Space curved surface reconstruction method based on boundary constraint

Significant advances have been made towards fault detection using deep learning. However, the fault labelling of seismic data requires great human effort. The resulting small sample problem makes traditional deep learning methods difficult to achieve desired results. Existing research proposes to train a deep learning model with labelled synthetic seismic data to get good fault detection results. However, due to the complexity of the actual geological situation, there are inevitable differences between synthetic seismic data and real seismic data in many aspects such as seismic signal frequency, frequency of fault distribution and degree of noise disturbance, which lead to the fact that the deep learning model trained by synthetic seismic data is difficult to get good fault detection result in field data applications. We propose to use transfer learning to reduce the impact of data differences to solve this problem: part of the deep transfer learning model is used to learn fault‐related features. And the other part of the deep transfer learning model is used to mine common features between the real seismic data and the synthetic seismic data, which makes the deep transfer learning model more suitable for real seismic data. Compared with the latest research progress, our method can greatly improve the effect of fault detection without real data label, which can significantly save the cost of manual label processing.

Learning from unlabelled real seismic data: Fault detection based on transfer learning

Fault interpretation plays a critical role in understanding the crustal development and exploring the subsurface reservoirs such as gas and oil. Recently, significant advances have been made towards fault semantic segmentation using deep learning. However, few studies employ deep learning in fault instance segmentation. We introduce mask propagation neural network for fault instance segmentation. Our study focuses on the description of the differences and relationships between each fault profile and the consistency of fault instance segmentations with adjacent profiles. Our method refers to the reference-guided mask propagation network, which is firstly used in video object segmentation: taking the seismic profiles as video frames while the seismic data volume as a video sequence along the inline direction we can achieve fault instance segmentation based on the mask propagation method. As a multi-level convolutional neural network, the mask propagation network receives a small number of user-defined tags as the guidance and outputs the fault instance segmentation on 3D seismic data, which can facilitate the fault reconstruction workflow. Compared with the traditional deep learning method, the introduced mask propagation neural network can complete the fault instance segmentation work under the premise of ensuring the accuracy of fault detection. Highlights •We demonstrate the results of fault instance segmentation in seismic profiles on the first try.•We show fault reconstruction based on fault instance segmentation.•The effectiveness of our method is verified by experiments.

Xingmiao Yao

Papers

Semi-supervised internet network traffic classification using a Gaussian mixture model

Seismic fault detection based on 3D Unet++ model

Space curved surface reconstruction method based on boundary constraint

Learning from unlabelled real seismic data: Fault detection based on transfer learning

Automatic fault instance segmentation based on mask propagation neural network