Junku Yuh

Bio: Junku Yuh is an academic researcher from National Science Foundation. The author has contributed to research in topics: Adaptive control & Control theory. The author has an hindex of 35, co-authored 109 publications receiving 4018 citations. Previous affiliations of Junku Yuh include Oregon State University & University of Miami.

Design and Control of Autonomous Underwater Robots: A Survey

Abstract: During the 1990s, numerous worldwide research and development activities have occurred in underwater robotics, especially in the area of autonomous underwater vehicles (AUVs). As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Great efforts have been made in developing AUVs to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. In the 1990s, about 30 new AUVs have been built worldwide. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, R&D activities in the AUV community have increased. However, this is just the beginning for more advanced, yet practical and reliable AUVs. This paper surveys some key areas in current state-of-the-art underwater robotic technologies. It is by no means a complete survey but provides key references for future development. The new millennium will bring advancements in technology that will enable the development of more practical, reliable AUVs.

Modeling and control of underwater robotic vehicles

Abstract: With the increased utilization of remotely operated vehicles in subsea applications, the development of autonomous vehicles becomes highly desirable to enhance operator efficiency. The dynamic model of an untethered remotely operated underwater vehicle is presented, and an adaptive control strategy for such vehicles is described. The robustness of the control system with respect to nonlinear dynamic behavior and parameter uncertainties is investigated by computer simulation. The results show that the use of the adaptive control system can provide high performance of the vehicle in the presence of unpredictable changes in the dynamics of the vehicle and its environment. >

A neural net controller for underwater robotic vehicles

Abstract: Results of a study on the application of neural networks to the control system of underwater robotic vehicles (URVs) are presented. The robustness of the control system with respect to nonlinear dynamic behavior and parameter uncertainties is investigated by computer simulation. The results show the feasibility of using unpredictable changes in the dynamics of the vehicle and its environment. >

Underwater robotics

Abstract: During the 1990s, numerous worldwide research and development activities occurred in underwater robotics, especially in the area of autonomous underwater vehicles (AUVs). As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Great efforts have been made in developing AUVs to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. In the 1990s, about 30 new AUVs have been built worldwide. It is just the beginning for more advanced, yet practical and reliable AUVs. The new millennium will bring advancements in technology that will enable the development of more practical, reliable AUVs. The paper describes some key areas in current state-of-the-art underwater robotic technologies and presents future research direction.

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.).

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Human-Robot Interaction: A Survey

Abstract: Human-Robot Interaction (HRI) has recently received considerable attention in the academic community, in labs, in technology companies, and through the media. Because of this attention, it is desirable to present a survey of HRI to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HRI within the field. The goal of this review is to present a unified treatment of HRI-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coherent "story" of HRI means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HRI story from multiple perspectives with an eye toward identifying themes that cross applications. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HRI, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design.

Flexible metal-oxide devices made by room-temperature photochemical activation of sol–gel films

Abstract: A method for annealing metal-oxide semiconductor films with deep-ultraviolet light yields thin-film transistors with performance comparable to that of thermally annealed devices, and widens the range of substrates on which such devices can be fabricated. Solution-processable metal-oxide semiconductors are attractive materials for low-cost, flexible electronics, but the need to anneal the deposited materials at relatively high temperatures limits the range of substrates on which such devices can be fabricated. Now Yong-Hoon Kim and colleagues demonstrate that irradiating the solution-cast films with deep ultraviolet light can obviate the need for an annealing step. In this system, photochemical activation serves essentially the same purpose as annealing, and the resulting semiconducting materials have device performance levels comparable to those produced using the high-temperature techniques. Amorphous metal-oxide semiconductors have emerged as potential replacements for organic and silicon materials in thin-film electronics. The high carrier mobility in the amorphous state, and excellent large-area uniformity, have extended their applications to active-matrix electronics, including displays, sensor arrays and X-ray detectors1,2,3,4,5,6,7. Moreover, their solution processability and optical transparency have opened new horizons for low-cost printable and transparent electronics on plastic substrates8,9,10,11,12,13. But metal-oxide formation by the sol–gel route requires an annealing step at relatively high temperature2,14,15,16,17,18,19, which has prevented the incorporation of these materials with the polymer substrates used in high-performance flexible electronics. Here we report a general method for forming high-performance and operationally stable metal-oxide semiconductors at room temperature, by deep-ultraviolet photochemical activation of sol–gel films. Deep-ultraviolet irradiation induces efficient condensation and densification of oxide semiconducting films by photochemical activation at low temperature. This photochemical activation is applicable to numerous metal-oxide semiconductors, and the performance (in terms of transistor mobility and operational stability) of thin-film transistors fabricated by this route compares favourably with that of thin-film transistors based on thermally annealed materials. The field-effect mobilities of the photo-activated metal-oxide semiconductors are as high as 14 and 7 cm2 V−1 s−1 (with an Al2O3 gate insulator) on glass and polymer substrates, respectively; and seven-stage ring oscillators fabricated on polymer substrates operate with an oscillation frequency of more than 340 kHz, corresponding to a propagation delay of less than 210 nanoseconds per stage.