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 …

I and i

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.).

Machine learning

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

A review of shape memory alloy research, applications and opportunities

The science and technology of ultracapacitors are reviewed for a number of electrode materials, including carbon, mixed metal oxides, and conducting polymers. More work has been done using microporous carbons than with the other materials and most of the commercially available devices use carbon electrodes and an organic electrolytes. The energy density of these devices is 3¯5 Wh/kg with a power density of 300¯500 W/kg for high efficiency (90¯95%) charge/discharges. Projections of future developments using carbon indicate that energy densities of 10 Wh/kg or higher are likely with power densities of 1¯2 kW/kg. A key problem in the fabrication of these advanced devices is the bonding of the thin electrodes to a current collector such the contact resistance is less than 0.1 cm2. Special attention is given in the paper to comparing the power density characteristics of ultracapacitors and batteries. The comparisons should be made at the same charge/discharge efficiency.

Ultracapacitors: Why, How, and Where is the Technology

Recent applications of fiber optic sensors to health monitoring in civil engineering

http://smsl.egr.uh.edu/sites/smsl/files/files/publications/Song_Ma_Li_%20SMA_civil_application.pdf

Applications of shape memory alloys in civil structures

This paper presents the classical Preisach hysteresis modeling and tracking control of a curved pre-stressed piezoceramic patch actuator system with severe hysteresis The actuator is also flexible with very small inherent damping It has potential applications in active antennas A series of tests are conducted to study the hysteresis properties of the piezoceramic actuator system The numerical expressions of the classical Preisach model for different input variations are presented The classical Preisach model is applied to simulate the static hysteresis behavior of the system Higher order hysteresis reversal curves predicted by the classical Preisach model are verified experimentally The good agreement found between the measured and predicted curves showed that the classical Preisach model is an effective mean for modeling the hysteresis of the piezoceramic actuator system Subsequently, the inverse classical Preisach model is established and applied to cancel the hysteresis the piezoceramic actuator system for the real-time microposition tracking control In order to improve the control accuracy and to increase damping of the actuator system, a cascaded PD/lead-lag feedback controller is designed with consideration of the dynamics of the actuator In the experiments, two cases are considered, control with major loop hysteresis compensation, and control with minor loop hysteresis compensation Experimental results show that RMS tracking errors are reduced by 50% to 70% if the hysteresis compensation is added in the feedforward path in both cases Therefore, hysteresis compensation with the feedback controller greatly improves the tracking control accuracy of the piezoceramic actuator

Tracking control of a piezoceramic actuator with hysteresis compensation using inverse Preisach model

Health monitoring of reinforced concrete bridges and other large-scale civil infrastructures has received considerable attention in recent years However, traditional inspection methods (x-ray, C-scan, etc) are expensive and sometimes ineffective for large-scale structures Piezoceramic transducers have emerged as new tools for the health monitoring of large-scale structures due to their advantages of active sensing, low cost, quick response, availability in different shapes, and simplicity for implementation In this research, piezoceramic transducers are used for damage detection of a 61 m long reinforced concrete bridge bent-cap Piezoceramic transducers are embedded in the concrete structure at pre-determined spatial locations prior to casting This research can be considered as a continuation of an earlier work, where four piezoceramic transducers were embedded in planar locations near one end of the bent-cap This research involves ten piezoceramic patches embedded at spatial locations in four different cross-sections To induce cracks in the bent-cap, the structure is subjected to loads from four hydraulic actuators with capacities of 80 and 100 ton In addition to the piezoceramic sensors, strain gages, LVDTs, and microscopes are used in the experiment to provide reference data During the experiment, one embedded piezoceramic patch is used as an actuator to generate high frequency waves, and the other piezoceramic patches are used as sensors to detect the propagating waves With the increasing number and severity of cracks, the magnitude of the sensor output decreases Wavelet packet analysis is used to analyze the recorded sensor signals A damage index is formed on the basis of the wavelet packet analysis The experimental results show that the proposed methods of using piezoceramic transducers along with the damage index based on wavelet packet analysis are effective in identifying the existence and severity of cracks inside the concrete structure The experimental results demonstrate that the proposed method has the ability to predict the failure of a concrete structure as verified by results from conventional microscopes (MSs) and LVDTs

https://iopscience.iop.org/article/10.1088/0964-1726/16/4/003/pdf

Concrete structural health monitoring using embedded piezoceramic transducers

This paper summarizes the authors' recent pioneering research work in piezoceramic-based smart aggregates and their innovative applications in concrete civil structures. The basic operating principle of smart aggregates is first introduced. The proposed smart aggregate is formed by embedding a waterproof piezoelectric patch with lead wires into a small concrete block. The proposed smart aggregates are multi-functional and can perform three major tasks: early-age concrete strength monitoring, impact detection and structural health monitoring. The proposed smart aggregates are embedded into the desired location before the casting of the concrete structure. The concrete strength development is monitored by observing the high frequency harmonic wave response of the smart aggregate. Impact on the concrete structure is detected by observing the open-circuit voltage of the piezoceramic patch in the smart aggregate. For structural health monitoring purposes, a smart aggregate-based active sensing system is designed for the concrete structure. Wavelet packet analysis is used as a signal-processing tool to analyze the sensor signal. A damage index based on the wavelet packet analysis is used to determine the structural health status. To better describe the time-history and location information of damage, two types of damage index matrices are proposed: a sensor-history damage index matrix and an actuator–sensor damage index matrix. To demonstrate the multi-functionality of the proposed smart aggregates, different types of concrete structures have been used as test objects, including concrete bridge bent-caps, concrete cylinders and a concrete frame. Experimental results have verified the effectiveness and the multi-functionality of the proposed smart aggregates. The multi-functional smart aggregates have the potential to be applied to the comprehensive monitoring of concrete structures from their earliest stages and throughout their lifetime.

https://iopscience.iop.org/article/10.1088/0964-1726/17/3/033001/pdf

Gangbing Song

Papers

Recent applications of fiber optic sensors to health monitoring in civil engineering

Applications of shape memory alloys in civil structures

Tracking control of a piezoceramic actuator with hysteresis compensation using inverse Preisach model

Concrete structural health monitoring using embedded piezoceramic transducers

Smart aggregates: multi-functional sensors for concrete structures—a tutorial and a review