Confidence Level Based on Ridge Estimator in Process Measurement and Its Application

Ordinary least squares(OLS) algorithm is widely applied in process measurement, because the sensor model used to estimate unknown parameters can be approximated through multivariate linear model. However, with few or noisy data or multi-collinearity, unbiased OLS leads to large variance. Biased estimators, especially ridge estimator, have been introduced to improve OLS by trading bias for variance. Ridge estimator is feasible as an estimator with smaller variance. At the same confidence level, with additive noise as the normal random variable, the less variance one estimator has, the shorter the two-sided symmetric confidence interval is. However, this finding is limited to the unbiased estimator and few studies analyze and compare the confidence levels between ridge estimator and OLS. This paper derives the matrix of ridge parameters under necessary and sufficient conditions based on which ridge estimator is superior to OLS in terms of mean squares error matrix, rather than mean squares error.Then the confidence levels between ridge estimator and OLS are compared under the condition of OLS fixed symmetric confidence interval, rather than the criteria for evaluating the validity of different unbiased estimators. We conclude that the confidence level of ridge estimator can not be directly compared with that of OLS based on the criteria available for unbiased estimators, which is verified by a simulation and a laboratory scale experiment on a single parameter measurement.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

OPTIONAL AND PREDICTABLE PROJECTIONS OF SET-VALUED MEASURABLE PROCESSES

In this paper,the optional and predictable projections of set-valued measurable processes are studied.The existence and uniqueness of optional and predictable projections of set-valued measurable processes are proved under proper circumstances.

The Construction of a Class of Measure-valued Processes of Stochastic Flows

In this article, we give a description of measure-valued processes with interactive stochastic flows. It is a unified construction for superprocesses with dependent spatial motion constructed by Dawson, LI, Wang and superprocesses of stochastic flows constructed by Ma and Xiang.

ON THE MEASURABILITY FOR FUNCTIONAL OF EMPIRICAL PROCESSES

In this paper, we give some results for checking the measurability of functional of empirical processes, their direct applications concern with the measurability of statis-tics constructed by Projection pursuit technique, thus the results obtained provide a basis in theory for projection pursuit study.

Iterative Dichotomiser Posteriori Method Based Service Attack Detection in Cloud Computing

Cloud computing(CC)is an advanced technology that provides access to predictive resources and data sharing.The cloud environment represents the right type regarding cloud usage model ownership,size,and rights to access.It introduces the scope and nature of cloud computing.In recent times,all processes are fed into the system for which consumer data and cache size are required.One of the most security issues in the cloud environment is Distributed Denial of Ser-vice(DDoS)attacks,responsible for cloud server overloading.This proposed sys-tem ID3(Iterative Dichotomiser 3)Maximum Multifactor Dimensionality Posteriori Method(ID3-MMDP)is used to overcome the drawback and a rela-tively simple way to execute and for the detection of(DDoS)attack.First,the pro-posed ID3-MMDP method calls for the resources of the cloud platform and then implements the attack detection technology based on information entropy to detect DDoS attacks.Since because the entropy value can show the discrete or aggregated characteristics of the current data set,it can be used for the detection of abnormal dataflow,User-uploaded data,ID3-MMDP system checks and read risk measurement and processing,bug ratingfile size changes,orfile name changes and changes in the format design of the data size entropy value.Unique properties can be used whenever the program approaches any data error to detect abnormal data services.Finally,the experiment also verifies the DDoS attack detection capability algorithm.

New Monitoring Technologies for Overhead Contact Line at 400 km.h-1

Various technologies have recently been developed for high-speed railways, in order to boost commercial speeds from 300 km.h： to 400 km.h-1. Among these technologies, this paper introduces the 400 km-h-1 class current collection performance evaluation methods that have been developed and demonstrated by Korea. Specifically, this paper reports details of the video-based monitoring techniques that have been adopted to inspect the stability of overhead contact line （OCL） components at 400 km.h-1 without direct contact with any components of the power supply system. Unlike conventional OCL monitoring systems, which detect contact wire positions using either laser sensors or line cameras, the developed system measures parameters in the active state by video data. According to experimental results that were obtained at a field-test site established at a commercial line, it is claimed that the proposed mea- surement system is capable of effectively measuring OCL parameters.

A convenient look-up-table based method for the compensation of non-linear error in digital fringe projection

Although the structured light system that uses digital fringe projection has been widely implemented in three-dimensional surface profile measurement, the measurement system is susceptible to non-linear error. In this work, we propose a convenient look-up-table-based （LUT-based） method to compensate for the non-linear error in captured fringe patterns. Without extra calibration, this LUT-based method completely utilizes the captured fringe pattern by recording the full-field differences. Then, a phase compensation map is established to revise the measured phase. Experimental results demonstrate that this method works effectively.

Variation Analysis and Stability Control for Measurement Process

Based on a two-section measuring method, a data collection sheet is formed. According to the data collected, the variation sources of a measurement process are analyzed and general guidelines for variation diagnosis are suggested. In our previous study, a method is presented to control the deviation of measurement system, but it is not sensitive to mean value variation and mean value drift. In this paper, a simplified data collection scheme via two-section measuring method is introduced to control both variation and center drift of measurement process. Based on this scheme, traditional SPC charts for variation control and EWMA chart for mean value drift are given.

On a Heuristic Point of View Concerning the Mechanics and Electrodynamics of Moving Bodies

The aspiration to clarify and visualize Einstein’s interpretation of the notion of simultaneity and relativistic understanding of length and time interval measurements led to the development of a model in which light pulses were replaced by sound signals. The model revealed the essence of Einstein’s mathematical constructs and the mechanism for origination of relativistic effects. Moreover, its consistent application produced novel results in particular, constructing a relativity theory based on derivation of its fundamentals out of a thought experiment. Using this approach, the ex-perimentally observed independence of the speed of light from the motion of source and observer was a necessary consequence of the finiteness of propagation speed for all kinds of in-formation. The paper provides a rationale for the logical framework formulated in article [1], and describes the experimental device built to validate the predictions of the theory and the results obtained.

Heuristic Approach to Kinematics and Electrodynamics of Moving Bodies

Relativity theory formulation is proposed, based not on the axiomatic postulation of its main principles but on their inference out of a thought experiment. With this approach, the experimentally observed independence of the speed of light from the motion of source and observer is a necessary consequence of the finiteness of propagation speed of all kinds of information. The mechanism of relativistic effects origination is described;the formulas of Lorentz transformations, Doppler quadratic effect, electromagnetic interaction and centrifugal force of inertia are derived.

Data-driven discovery of linear dynamical systems from noisy data

In modern science and engineering disciplines,data-driven discovery methods play a fundamental role in system modeling,as data serve as the external representations of the intrinsic mechanisms within systems.However,empirical data contaminated by process and measurement noise remain a significant obstacle for this type of modeling.In this study,we have developed a data-driven method capable of directly uncovering linear dynamical systems from noisy data.This method combines the Kalman smoothing and sparse Bayesian learning to decouple process and measurement noise under the expectation-maximization framework,presenting an analytical method for alternate state estimation and system identification.Furthermore,the discovered model explicitly characterizes the probability distribution of process and measurement noise,as they are essential for filtering,smoothing,and stochastic control.We have successfully applied the proposed algorithm to several simulation systems.Experimental results demonstrate its potential to enable linear dynamical system discovery in practical applications where noise-free data are intractable to capture.

Lecture notes on quantum entanglement:From stabilizer states to stabilizer channels

We study mathematical,physical and computational aspects of the stabilizer formalism arising in quantum information and quantum computation.The measurement process of Pauli observables with its algorithm is given.It is shown that to detect genuine entanglement we need a full set of stabilizer generators and the stabilizer witness is coarser than the GHZ(Greenberger-Horne-Zeilinger)witness.We discuss stabilizer codes and construct a stabilizer code from a given linear code.We also discuss quantum error correction,error recovery criteria and syndrome extraction.The symplectic structure of the stabilizer formalism is established and it is shown that any stabilizer code is unitarily equivalent to a trivial code.The structure of graph codes as stabilizer codes is identified by obtaining the respective stabilizer generators.The distance of embeddable stabilizer codes in lattices is obtained.We discuss the Knill-Gottesman theorem,tableau representation and frame representation.The runtime of simulating stabilizer gates is obtained by applying stabilizer matrices.Furthermore,an algorithm for updating global phases is given.Resolution of quantum channels into stabilizer channels is shown.We discuss capacity achieving codes to obtain the capacity of the quantum erasure channel.Finally,we discuss the shadow tomography problem and an algorithm for constructing classical shadow is given.

In-line imaging measurements of particle size,velocity and concentration in a particulate two-phase flow

A novel method is developed for in-line measurements of particle size, velocity and concentration in a dilute, particulate two-phase flow based on trajectory image processing. The measurement system consists of a common industrial CCD camera, an inexpensive LED light and a telecentric lens. In this work, the image pre-processing steps include stitching, illumination correction, binarization, denoising, and the elimination of unreal and defocused particles. A top-hat transformation is found to be very effective for the binarization of images with non-uniform background illumination. Particle trajectories measured within a certain exposure time are used to directly obtain particle size and velocity. The particle concentration is calculated by using the statistics of recognized particles within the field of view. We validate our method by analyzing experiments in a gas-droplet cyclone separator. This in-line image processing method can significantly reduce the measurement cost and avoid the data inversion process involved in the light scattering method.

A structural comparison of measurement-based admission control algorithms

Measurement-based admission control （MBAC） algorithm is designed for the relaxed real-time service. In contrast to traditional connection admission control mechanisms, the most attractive feature of MBAC algorithm is that it does not require a prior traffic model and that is very difficult for the user to come up with a tight traffic model before establishing a flow. Other advantages of MBAC include that it can achieve higher network utilization and offer quality service to users. In this article, the study of the equations in the MBAC shows that they can all be expressed in the same form. Based on the same form, some MBAC algorithms can achieve same performance only if they satisfy some conditions.

FORWARD-BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS WITH BROWNIAN MOTION AND POISSON PROCESS

Existence and uniqueness results of the solution to fully coupled forward-backward stochastic defferential equations with Brownian motion and Poisson process are obtained. Many stochastic Hamilton systems arising in stochastic optimal control systems with random jump and in mathemstical finance with security price discontinuously changing can be treated with these results. The continuity of the solution depending on parameters is also proved in this paper.

Landmarks in the application of electrical tomography in particle science and technology

Selected milestones in the development and use of electrical tomography in powder conveying, slurry processing and multi-phase flow are highlighted. The ability to map concentration in opaque mixtures under process-realistic conditions was a major innovation for the method and has had far reaching implications. Subsequent developments have enabled velocity information to be abstracted resulting in the ability to measure component flux and motion.

GIRSANOV'S THEOREM ON ABSTRACT WIENER SPACES

The moments and absolute continuity of measure valued branching Brownian motions with bounded interacting intensity are investigated. An estimate of higher order moments is obtained.The absolute continuity is verified in the one dimension case. This thereby verifies the conjecture of Méléard and Roelly in .