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.

/pdf/a-survey-of-fault-diagnosis-and-fault-tolerant-techniques-9uuf1uqysf.pdf

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

Complex industrial automation systems and processes, such as chemical processes, manufacturing systems, wireless network systems, power and energy systems, smart grids and so forth, have greatly contributed to our daily life [...]

https://www.mdpi.com/2227-9717/9/4/664/pdf

Special Issue on “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”

It is paramount to improve operational conversion efficiency in air-conditioning refrigeration. It is noticed that control efficiency for model-based methods highly relies on the accuracy of the mechanism model, and data-driven methods would face challenges using the limited collected data to identify the information beyond. In this study, a hybrid novel approach is presented, which is to integrate a data-driven method with a coarse model. Specifically, reinforcement learning is used to exploit/explore the conversion efficiency of the refrigeration, and a coarse model is utilized to evaluate the reward, by which the requirement of the model accuracy is reduced and the model information is better used. The proposed approach is implemented based on a hierarchical control strategy which is divided into a process level and a loop level. The simulation of a test bed shows the proposed approach can achieve better conversion efficiency of refrigeration than the conventional methods.

/pdf/improvement-of-refrigeration-efficiency-by-combining-1bo07yrnik.pdf

Improvement of Refrigeration Efficiency by Combining Reinforcement Learning with a Coarse Model

Gene-based Collaborative Filtering using recommender system

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2008/data/papers/2995.pdf

High-gain observer-based parameter identification with application in a gas turbine engine

This paper considers the problem of robust fault detection for Lipschitz nonlinear systems impaired by faults and unknown inputs in both process and sensors The purpose of the proposed strategy is to estimate both the system states and the considered faults existing in actuators as well as sensors while minimize the influences from disturbances An innovative robust observer design methodology is developed through an integration of fault estimation approach and unknown input observer (UIO) In contrast to previous studies, the considered unknown inputs do not only exist in system process but also sensors Moreover, to meet the practical engineering situations, they are not assumed to be decoupled completely With the assist of linear matrix inequality (LMI) method, observer parameters are determined to attenuate the influences of unknown inputs which cannot be decoupled, as well as guarantee the stability of the estimation dynamics Simulation results are presented to demonstrate that the proposed observer scheme performs reasonably well

Zhiwei Gao

Papers

Special Issue on “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”

Improvement of Refrigeration Efficiency by Combining Reinforcement Learning with a Coarse Model

Gene-based Collaborative Filtering using recommender system

High-gain observer-based parameter identification with application in a gas turbine engine

Unknown input observers for fault diagnosis in Lipschitz nonlinear systems