Feng Lin

Bio: Feng Lin is an academic researcher from Wayne State University. The author has contributed to research in topics: Supervisory control & Computer science. The author has an hindex of 54, co-authored 497 publications receiving 11936 citations. Previous affiliations of Feng Lin include National University of Singapore & Carnegie Mellon University.

Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

Abstract: Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data, whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases. Especially, our method has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection.

On observability of discrete-event systems

Abstract: The observability of discrete-event systems is investigated. A discrete-event system G is modeled as the controlled generator of a formal language L m ( G ) in the framework of Ramadge and Wonham. To control G, a supervisor S is developed whose action is to enable and disable the controllable events of G according to a record of occurrences of the observable events of G, in such a way that the resulting closed-loop system obeys some prespecified operating rules embodied in a given language K. A necessary and sufficient condition is found for the existence of a supervisor S such that L m ( S/G ) = K . Based on this condition, a solution of the supervisory control and observation problem (SCOP) is obtained. Two examples are provided.

Diagnosability of discrete event systems and its applications

Abstract: As man-made systems become more and more complex, diagnostics of component failures is no longer an easy task that can be performed based on experience and intuition. Therefore, it is important to develop a systematic approach to diagnostic problems. Diagnostics can be done either on-line or off-line. By on-line diagnostics, we mean diagnostics performed while the system to be diagnosed is in normal operation. On the other hand, in off-line diagnostics, the system is not in normal operation. We will study both on-line and off-line diagnostics in this paper and identify main features and differences of these two types of diagnostics. We will also introduce the concept of diagnosability and study its properties, all in the framework of discrete event systems. This study is motivated by diagnostic problems in the automotive industry and we will emphasize its applications.

Decentralized supervisory control of discrete-event systems

Abstract: A discrete-event system G is modeled as the controlled generator of a formal language L( G ) , in the framework of Ramadge and Wonham. In general a centralized global supervisory controller S for G can be defined which generates a suitable closed-loop languageL(S/G). The paper develops the idea of local supervisors Si whose concurrent operation results in the closed-loop language L( ΛS i /G ) . Conditions are obtained which guarantee that L( ΛS i /G ) = L( S/G ) , namely, distributed local supervision is equivalent to global supervision. For illustration a simple manufacturing system is discussed.

Robust control design : an optimal control approach

Abstract: Preface. Notation. 1 Introduction. 1.1 Systems and Control 1.2 Modern Control Theory 1.3 Stability 1.4 Optimal Control 1.5 Optimal Control Approach 1.6 Kharitonov Approach 1.7 H- and H2 Control 1.8 Applications 1.9 Use of This Book 2 Fundamentals of Control Theory. 2.1 State Space Model 2.2 Responses of Linear Systems 2.3 Similarity Transformation 2.4 Controllability and Observability 2.5 Pole Placement by State Feedback 2.6 Pole Placement Using Observer 2.7 Notes and References 2.8 Problems 3 Stability Theory. 3.1 Stability and Lyapunov Theorem 3.2 Linear Systems 3.3 Routh-Hurwitz Criterion 3.4 Nyquist Criterion 3.5 Stabilizability and Detectability 3.6 Notes and References 3.7 Problems 4 Optimal Control and Optimal Observers. 4.1 Optimal Control Problem 4.2 Principle of Optimality 4.3 Hamilton-Jacobi-Bellman Equation 4.4 Linear Quadratic Regulator Problem 4.5 Kalman Filter 4.6 Notes and References 4.7 Problems 5 Robust Control of Linear Systems. 5.1 Introduction 5.2 Matched Uncertainty 5.3 Unmatched Uncertainty 5.4 Uncertainty in the Input Matrix 5.5 Notes and References 5.6 Problems 6 Robust Control of Nonlinear Systems. 6.1 Introduction 6.2 Matched Uncertainty 6.3 Unmatched Uncertainty 6.4 Uncertainty in the Input Matrix 6.5 Notes and References 6.6 Problems 7 Kharitonov Approach. 7.1 Introduction 7.2 Preliminary Theorems 7.3 Kharitonov Theorem 7.4 Control Design Using Kharitonov Theorem 7.5 Notes and References 7.6 Problems 8 H and H2 Control. 8.1 Introduction 8.2 Function Space 8.3 Computation of H2 and H- Norms 8.4 Robust Control Problem as H2 and H- Control Problem 8.5 H2/H- Control Synthesis 8.6 Notes and References 8.7 Problems 9 Robust Active Damping. 9.1 Introduction 9.2 Problem Formulation 9.3 Robust Active Damping Design 9.4 Active Vehicle Suspension System 9.5 Discussion 9.6 Notes and References 10 Robust Control of Manipulators. 10.1 Robot Dynamics 10.2 Problem Formulation 10.3 Robust Control Design 10.4 Simulations 10.5 Notes and References 11 Aircraft Hovering Control. 11.1 Modelling and Problem Formulation 11.2 Control Design for Jet-borne Hovering 11.3 Simulation 11.4 Notes and References Appendix A: Mathematical Modelling of Physical Systems. References and Bibliography. Index.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The control of discrete event systems

Abstract: A discrete event system (DES) is a dynamic system that evolves in accordance with the abrupt occurrence, at possibly unknown irregular intervals, of physical events. Such systems arise in a variety of contexts ranging from computer operating systems to the control of complex multimode processes. A control theory for the logical aspects of such DESs is surveyed. The focus is on the qualitative aspects of control, but computation and the related issue of computational complexity are also considered. Automata and formal language models for DESs are surveyed. >