다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

A most critical and important issue surrounding the design of automatic control systems with the successively increasing complexity is guaranteeing a high system performance over a wide operating range and meeting the requirements on system reliability and dependability. As one of the key technologies for the problem solutions, advanced fault detection and identification (FDI) technology is receiving considerable attention. The objective of this book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers.

Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools

With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

Time Synchronization (TS) is a key enabling technology of mission-critical Wireless Sensor Networks (WSNs) to provide a common timescale for distributed sensor nodes. Inspired by synchronous flashing of fireflies, a bio-inspired model, Pulse-Coupled Oscillators (PCO), has been intensively studied. The most studies on PCOs are theoretical work, and the assumption is given that oscillators broadcast and receive the Pulses simultaneously when synchronization of a network is achieved. This is not true when it comes to any real-world environments. From the viewpoint of WSNs, the clock of a sensor node driven by crystal oscillators can be modelled as an oscillator, and Pulse firing can be implemented by transmitting a packet. However, the concurrent transmission of Pulse packets is impossible due to the packet collision in the single wireless channel. To avoid this issue in WSNs, this paper adopts a desynchronization mechanism, in which the Pulse packets are transmitted to the wireless channel in a uniformly distributed fashion and in accordance with standard IEEE 802.15.4. A hardware testbed is developed to implement the desynchronized pulse-coupled oscillators, and it can also be extended to the large-scale wireless sensor networks.

/pdf/implementation-of-timestamped-pulse-coupled-oscillators-in-30vv60v5i9.pdf

Implementation of Timestamped Pulse-Coupled Oscillators in IEEE 802.15.4 Networks

This paper proposes a novel high-order associative memory system (AMS) based on the Newton's forward interpolation (NFI), The Interpolation Polynomials and training algorithms for the new AMS scheme are derived. The proposed novel AMS is capable of implementing error-free approximation to complex nonlinear functions of arbitrary order. A method Based on NFI-AMS is designed to model and predict network traffic dynamics, which is capable of modeling the complex nonlinear behavior of a traffic time series and capturing the properties of network traffic. The simulation results showed that the proposed scheme is feasible and efficient. Furthermore, the NFI-AMS based traffic prediction can be used in more fields for network design, management and control.

A Novel Associative Memory System Based Modeling and Prediction of TCP Network Traffic

Based on reduced-order causal observer-based controllers for singular systems, a new state-space realization of Bezout factorization composed of all proper stable transfer 
functions is established. The resulting parametrization of all causal properly stabilizing controllers is characterized. The relationships among the Bezout factorization in the present paper and the previous results are also discussed, which give explicit formulae of computing one from another. Keywords.

On state-space realization of Bezout factorizations in singular systems

This paper develops a multisensor data fusion-based deep learning algorithm to locate and classify faults in a leader-following multiagent system. First, sequences of one-dimensional data collected from multiple sensors of followers are fused into a two-dimensional image. Then, the image is employed to train a convolution neural network with a batch normalisation layer. The trained network can locate and classify three typical fault types: the actuator limitation fault, the sensor failure and the communication failure. Moreover, faults can exist in both leaders and followers, and the faults in leaders can be identified through data from followers, indicating that the developed deep learning fault diagnosis is distributed. The effectiveness of the deep learning-based fault diagnosis algorithm is demonstrated via Quanser Servo 2 rotating inverted pendulums with a leader-follower protocol. From the experimental results, the fault classification accuracy can reach 98.9%.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09712261.pdf

A Deep Learning-Based Fault Diagnosis of Leader-Following Systems

This paper investigates the observer-based fault detection filtering problem for continuous-time descriptor Markov jump linear systems (DMJLSs). Both parameter uncertainties and state delays are assumed to be time-varying but bounded. A fault detection filter (FDF) is designed such that for all admissible uncertainties, the resultant filtering dynamics is robust stochastically stable, and the residual signal is robust to the unknown input and the known control input but sensitive to the fault. Moreover, an iterative linear matrix inequality (LMI) approach for filter parameter computation as well as the residual evaluation and threshold calculation are also given. The validity of the proposed method is illustrated by a numerical example.

Zhiwei Gao

Papers

Implementation of Timestamped Pulse-Coupled Oscillators in IEEE 802.15.4 Networks

A Novel Associative Memory System Based Modeling and Prediction of TCP Network Traffic

On state-space realization of Bezout factorizations in singular systems

A Deep Learning-Based Fault Diagnosis of Leader-Following Systems

Fault detection for descriptor systems with Markov jump parameters and time-varying delays