Reliability index and Asian barrier option pricing formulas of the uncertain fractional first-hitting time model with Caputo type

Jin, Ting; Xia, Hongxuan; Bao, Jinfeng

doi:10.1016/j.chaos.2020.110409

Cited by 43 publications

(26 citation statements)

References 28 publications

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“…In the future, we will deploy our algorithm to real distributed systems and study the problems on advanced persistent user behavior identification and parameter optimization [30][31][32][33]. In addition, we will generate the data by simulating abnormal user behavior to validate the performance of our algorithm [34,35].…”

Section: Discussionmentioning

confidence: 99%

Detecting Persistent User Behavior Using Probabilistic Counting in Network-Wide View

Zhou

Jin

2021

Mathematical Problems in Engineering

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Persistent user behavior monitoring, which deals with finding users that occur persistently over a measurement period, is one hot topic in traffic measurement. It is significant for many applications, such as anomaly detection. Former works concentrate on monitoring frequent user behavior, such as users occurring frequently either over one measurement period or on one monitor. They have paid little attention to detect persistent user behavior over a long measurement period on multiple monitors. However, persistent users do not necessarily appear frequently in a short measurement period, but appear persistently in a long measurement period. Due to limited resource on monitors, it is not practical to collect a tremendous amount of network traffic in a long measurement period on one single monitor. Moreover, since network attackers deliberately send packets flowing through the entire managed network, it is difficult to detect abnormal behavior on one single monitor. To solve the above challenges, a novel method for detecting persistent user behavior called DPU is proposed, and it contains both online distributed traffic collection in a long measurement period on multiple monitors and offline centralized user behavior detection on the central server. The key idea of DPU is that we design the compact distributed synopsis data structure to collect the relevant information with users occurring in a long measurement period on each monitor, and we can reconstruct user IDs using simple calculations and bit settings to find users with persistent behavior on the basis of estimated occurrence frequency of users on the central server when user IDs are unknown in advance. The experiments are conducted on real traffic to evaluate the performance of detecting persistent user behavior, and the experimental results illustrate that our method can improve about 30% estimation accuracy, 40% detection precision, and accelerate about 3 times in comparison with the related method.

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Section: Discussionmentioning

confidence: 99%

Detecting Persistent User Behavior Using Probabilistic Counting in Network-Wide View

Zhou

Jin

2021

Mathematical Problems in Engineering

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“…Fault detection is also important in economy in which, very often, fault assumes the meaning of risk. In [ 4 ], a novel first-hitting time model is established to measure the relationship between the option reliability index and the value of risky assets. Detecting faults and isolating them using observer-based techniques has been applied to many technical fields.…”

Section: Introductionmentioning

confidence: 99%

A Sensor Fault Detection Scheme as a Functional Safety Feature for DC-DC Converters

Schmidt

Oberrath

Mercorelli

2021

Sensors

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DC-DC converters are widely used in a large number of power conversion applications. As in many other systems, they are designed to automatically prevent dangerous failures or control them when they arise; this is called functional safety. Therefore, random hardware failures such as sensor faults have to be detected and handled properly. This proper handling means achieving or maintaining a safe state according to ISO 26262. However, to achieve or maintain a safe state, a fault has to be detected first. Sensor faults within DC-DC converters are generally detected with hardware-redundant sensors, despite all their drawbacks. Within this article, this redundancy is addressed using observer-based techniques utilizing Extended Kalman Filters (EKFs). Moreover, the paper proposes a fault detection and isolation scheme to guarantee functional safety. For this, a cross-EKF structure is implemented to work in cross-parallel to the real sensors and to replace the sensors in case of a fault. This ensures the continuity of the service in case of sensor faults. This idea is based on the concept of the virtual sensor which replaces the sensor in case of fault. Moreover, the concept of the virtual sensor is broader. In fact, if a system is observable, the observer offers a better performance than the sensor. In this context, this paper gives a contribution in this area. The effectiveness of this approach is tested with measurements on a buck converter prototype.

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“…With the substantial development of the mathematics industry and the computer industry, there has been some progress in processing time series data [1,2]. As a new method of monitoring the state of rocks in recent years, the microseismic monitoring system has been used in mines, tunnels, petroleum, and so on.…”

Section: Introductionmentioning

confidence: 99%

Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning

2021

Shock and Vibration

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The feature extraction of high-precision microseismic signals is an important prerequisite for multicategory recognition of microseismic signals, and it is also an important basis for intelligent sensing modules in smart mines. Aiming at the problem of unobvious feature extraction of multiclass mine microseismic signals, this paper is based on the unsupervised learning method in the deep learning method, combined with wavelet packet energy ratio and empirical modulus singular value decomposition, and proposes a method based on wavelet packet energy and empirical modulus singular value decomposition and proposes a method (M-W&E) based on wavelet packet energy and empirical modulus singular value decomposition. This method firstly performs empirical modulus singular value decomposition and wavelet packet energy ratio on the microseismic signal to construct the basic feature vector and then uses the unsupervised learning algorithm to perform the unsupervised learning method feature fusion of the basic feature vector to construct the fused feature vector. After visualization by t-SNE, various distinctions in the fusion feature vector are more obvious. After testing the fusion feature classification using SVM, it is found that the recognition rate of the new feature after feature fusion is better than that of a single wavelet packet empirical energy component and singular value of empirical modulus, which basically meets the engineering needs and is a mine microseism. The signal extraction and feature enhancement fusion of multiclass samples provide a new idea.

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Reliability index and Asian barrier option pricing formulas of the uncertain fractional first-hitting time model with Caputo type

Cited by 43 publications

References 28 publications

Detecting Persistent User Behavior Using Probabilistic Counting in Network-Wide View

Detecting Persistent User Behavior Using Probabilistic Counting in Network-Wide View

A Sensor Fault Detection Scheme as a Functional Safety Feature for DC-DC Converters

Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning

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