Structures and civil infrastructure systems, including bridges, buildings, dams, and pipelines, are exposed to various external loads throughout their lifetimes. As they age and deteriorate, effective inspection, monitoring, and maintenance of these systems becomes increasingly important. However, conventional practice based on periodic human visual inspection is time-consuming, labor-intensive, subjective, and prone to human error. Nondestructive testing techniques have shown potential for detecting hidden damages, but the large size of the structural systems presents a significant challenge for conducting such localized tests. Over the past few decades, a significant number of studies have been conducted in the area of structural health monitoring (SHM), aiming at timely, objective detection of damage or anomalies and quantitative assessment of structural integrity and safety based on measurements by various on-structure sensors [1–4]. Most of the SHM techniques are based on structural dynamics, and the basic principle is that any structural damage or degradation would result in changes in structural dynamic responses as well as the corresponding modal characteristics. The SHM process is implemented in four key steps: data acquisition, system identification, condition assessment, and decision-making.

Readers will learn: How to read a feature control frame’s symbology as a sentence. How switching datums affects gage design, part fixturing and accumulated tolerance error. How to visualize and calculate datum feature shift. How to tolerance a PC board. When a simultaneous requirement applies to single features or patterns of features and when they are a separate requirement. Angular orientation datum features and sliding versus stationary datum feature simulators. Profile tolerance zone and pattern shift. How to tolerance gears and keyways. How to apply and read sequential tolerancing schemes.

Any motion of a rigid body in 3-D space is accompanied with six small deviations along and around its six DOF in infinitesimal motions. Sources of these errors are considered in detail as applied to geometrical errors [1]. For the 6-DOF manipulators, the total geometrical error vector depends on (6 n + 6) components, where n is the number of links, among them only 6 errors are induced by actuated links. This approach is formulated through the variational method of accuracy calculation [2], which models any small displacement of the rigid body as a result of total variation of the nominal function (the form-shaping function [1, 2] fulfils the role of the nominal function as applied to machines and robots).

In routine applications, the legacy of solving the optimization and using the results will have given customers and implementers a good understanding of the context of the application and the appropriate choices for the models OF, DV, convergence criteria, thresholds, and constraints. However, for new applications, the implementer must design these procedures and must make the appropriate choices. The question is “How?” The answer is “trial and error.” Try something, and when you have the result, consider all aspects of the outcome. Then evaluate whether all aspects are satisfactory. If not satisfactory, improve the procedure.

Values of data from online sensors are corrupted by sensor drift, which could be due to ambient conditions (temperature, humidity, etc.), dust accumulation, element wear, corrosion, cumulative vibration damage, etc. It could also be that the sensor was not perfectly calibrated, which leads to incorrect values. These aspects mean that the value is biased, is in error, and has a systematic error.

The word “accuracy” has a wide spectrum of everyday applications. “Merriam-Webster’s Online” dictionary defines the accuracy as: (1) freedom from mistake or error, i.e., correctness; (2a) conformity to truth or to a standard or model, i.e., exactness; (2b) degree of conformity of a measure to a standard or a true value; this meaning is close to precision .

Actual machine motions are inevitably accompanied by small non-designed deviations of position and orientation of all the links, relative to each other. The sum of the elementary errors together with their transfer coefficients presents the error budget [1]. The number of the errors associated with all interacting links is several dozens, and an effective way to analyze their contribution to the machine accuracy and precision is formulated as total variation of the FSF [2]. In this chapter, a linearized mathematical model for a budget of geometric and kinematic errors suitable for the FSS of serial-, parallel-, and hybrid-kinematics machines is described.

Consideration of the position error problem uses the models entered for solution of inverse kinematics for parallel-kinematics machines. Obviously, an actual motion of the moving platform of the l i -DOF mechanism ( l i = 2 ÷ 6) is accompanied with the same sextet of position-orientation errors δ xi , δ yi , δ zi , δ θi , δ ψi , δ φi , composing the twist δ for 1-DOF body. Since the set of drives of the moving platform is simultaneously served as a movement issue and as a suspension system, the moving platform can be modelled as a rigid body on six rigid supports whose supports are the limbs. Hence, the solution obtained for the guideways accuracy (Subsection 9.1.2.3 ) can be applied to the position accuracy problem of the moving platform of the parallel mechanism taking into account that:

The error budgeting technique presented in Chapter 8 and developed in Chapters 9-11 uses linearization of trigonometric functions realized through the standard error matrices E , Eq. (8.9). However, in studies of accuracy problems, there are specific cases in which the linearization technique is unsuitable for accuracy calculations, and a non-linear problem has to be solved. There are two main groups of such situations:

Direct assessment methodologies are only applicable for time dependent threats (i.e., internal corrosion, external corrosion, and stress corrosion cracking). Time-independent threats (i.e., operator error, weather related/outside force, and third party/excavation damage) and stable threats (i.e., equipment failure, manufacturing defects, and construction defects) do not have industry recognized direct assessment methodologies. Prudent operators should assess time-independent and stable threats by other means than direct assessment. Operator training audits, monitoring of third party activity, inspection of equipment, pressure testing, and In-Line Inspection (ILI) are options for assessing these threats.

While teaching classes from this book, some discussion of uncertainty and vibration complement the other interrelated topics in this book. Discussions of “Uncertainty Analysis” and “Vibration Analysis” are followed by section on “Corrections”, which includes typographical errors, misprints and clarifications.

In this chapter, an accurate ultrasonic method for measurement of small motion and deformation of biological tissue are described. In the displacement estimation based on the phase shift of echoes, the displacement estimates are biased when the center frequency of the RF echo changes. Such an apparent change in the center frequency could be caused by the interference of echoes from scatterers. To reduce the influence of the center frequency variation on the estimation of motion and deformation, an error correcting function, which does not require the assumption that the center frequency distributions in 2 different frames are same, was introduced. As a result, the proposed method provides better strain estimates in comparison with conventional phase-sensitive correlation methods.

The glucose sensing industry is primarily driven by the diabetes epidemic. The number of people diagnosed with diabetes in UK has more than doubled from 1.4 m in 1996 to 2.9 m in 2011, and is expected to cross 5 m by 2025 (Diabetes UK, 2013). Similarly, diabetes prevalence worldwide of 366 m in 2011 is expected to increase to 552 m by 2030 [287–289]. The long-standing goal for reliable continuous glucose monitoring is still unmet, the finger-prick home glucose monitoring devices are indicated as acceptable with an error margin of ±20%, and laboratory testing is still the gold standard for diabetes diagnosis. Nanomaterials have an important role in making glucose sensing technologies cross the frontiers of traditional electrochemical and optical methods through solving the problems with large error margins and reliable in vivo lifetime and function of implantable glucose biosensors. In addition, the need for glucose sensing is also rapidly increasing in food, cell culture and biotechnology industries that help sustain the growing human population.

In this paper, the set point tracking and the regulatory responses over the entire operating range is controlled by PID controller. The performance of boost converter and startup behavior has been analyzed for open loop response using PID controller. The neuro controller is designed using NARMA-L2 control for testing error convergence in plant model. Finally, the performance of the proposed system was evaluated by with and without conventional PID controller.

The emergence of use case analysis method provides an effective way of capturing and analyzing software requirements, but for beginners, how to use it efficiency is always a problem. In paper, it presents some common mistakes that users often make and principles and standards users should comply with. Finally, it proposes a granularity-oriented use case analysis method in detail.

In order to estimate the effect of geometric error sources and to compensate the geometric error, a systematic approach for error modeling is presented to build a integrated geometric error model. The error sources are categorized and the error model is analyzed to get simplified. A compensation method according to the error model is also proposed. Simulation results prove the validity of the analysis and effectiveness of the compensation method.

Small scale fading causes severe fluctuations in the received signal strength. So, accurate channel estimation and equalization of the received signal is one of the most challenging problems of wireless communication. In this work, the performance of 64-QAM under fading condition is analyzed and efficient adaptive equalization technique is implemented using LMS algorithm. The Bit Error Rate (BER) of 64-QAM is determined after passing it through flat Rayleigh fading channel. Unlike conventional channel estimation techniques, such as PSAM, gradient approach is found to be more effective when input symbol constantly changes. By Law of large numbers we have considered that impulse response of the channel has almost unity amplitude and concentrated on estimating the phase of the channel coefficient. Here, the incoming symbols to the channel are constantly changing causing the behavior of the channel to vary according to the data. In gradient approach we estimate the channel using incoming symbols alone. The simulated results are found to be in good agreement with the theoretically determined results.

Accurate monthly electricity demand forecasting is the foundation of power system operation and planning. Grey Model (Short of “GM”) has been used to forecast widely in many domains, due to its effective prediction with little samples, incomplete information. Electricity demand of throughout society can be divided into the consumption of residents and the demand of three industries. Meanwhile, it can be regard as a grey system, so it makes sense to forecast it by GM(1,n) model. This paper proposes a novel approach to monthly electricity demand forecasting combining seasonal exponential transformation and residual error modified method with GM(1,n) model. Finally, apply the novel model to a real dataset in Guangzhou, the result shows that it performs well.

Two optimum power transfer models of self-excited induction generators (SEIGs) have been studied with a view to validating previous findings. The first model employed a capacitor bank-load approach to optimising by sizing of shunt and shunt-series compensation capacitors for SEIG variable speed wind turbines, and the use of 50uF shunt + 45uF series capacitors in this first model was validated and supported. The second model employed an extrapolated magnetizing characteristic resulting in three curves, in which the generated voltage provides a high degree of voltage stability without changing the excitation capacitor or exceeding the generator rating. Errors were found in the best-fit coefficients presented in this second model, which are corrected, represented, and validated.

Nowadays, there are a large number of frameworks for developing Web applications such as Struts, JSF, Ruby on Rails or, CakePHP, among others. Each framework gives features to make easy the Web applications development. Lift emerges as a new Web framework which has own features and learns from the mistakes of other Web frameworks. Some companies such as Siemens and IBM are deploying theirs applications in Scala and Lift, as well as social networks like Twitter and Foursquare. However, Lift is a new Web framework so there is few information for developing Web applications through of best practices. This paper discusses the best practices in order to develop Web applications with Lift avoiding common mistakes of design and implementation that other Web frameworks have. Finally, as proof of concept we developed a set of Lift-based Web applications taking into account some best practices such as actors, lazy loading, sitemap and wiring.