光学仿真分析在光电传感器开发过程中的应用

1 引言 光电传感器是指利用光电效应作为传感原理的传感器件,它主要由光源、光学通路、光电探测器等部分.光源发出的光照射到待检测的目标物体上,被目标物体调制后被光电探测器所接收.     

On‐line acoustic attenuation spectroscopy of emulsions stabilized by vinyl alcohol–vinyl acetate copolymers: a model system for the suspension polymerization of vinyl chloride

BACKGROUND: Poly[(vinyl alcohol)‐co‐(vinyl acetate)] (PVA) copolymers obtained by partial hydrolysis of poly(vinyl acetate) are currently used as industrial stabilizers in the suspension polymerization of vinyl chloride monomer (VCM). Their molecular characteristics, mainly the average degree of hydrolysis (DH ) and average degree of polymerization (DPw ), have a major influence on the monomer droplet size and the properties of the final poly(vinyl chloride) resin. RESULTS: The average droplet size and size distribution of chlorobutane/water emulsions, as a model system for VCM/water emulsions, were studied using acoustic attenuation spectroscopy on‐line with an agitated laboratory reactor. The emulsions were stabilized by PVA with DH values between 73 and 88 mol% and DPw values between 450 and 2500. The effects of agitation speed, stirring time and concentration of the PVA copolymers were investigated. An attempt was made to correlate the interfacial tension and the droplet size. CONCLUSION: On‐line acoustic spectroscopy appears to be a suitable technique for the real‐time control of the droplet size of monomer suspensions. The advantages and limitations of the technique are outlined. The validity and the application limits of the commonly cited correlation between the droplet size and the Weber number are established for polymeric surfactant‐stabilized emulsions. Copyright © 2009 Society of Chemical Industry     

Classification using belief functions: relationship between case-based and model-based approaches.

The transferable belief model (TBM) is a model to represent quantified uncertainties based on belief functions, unrelated to any underlying probability model. In this framework, two main approaches to pattern classification have been developed: the TBM model-based classifier, relying on the general Bayesian theorem (GBT), and the TBM case-based classifier, built on the concept of similarity of a pattern to be classified with training patterns. Until now, these two methods seemed unrelated, and their connection with standard classification methods was unclear. This paper shows that both methods actually proceed from the same underlying principle, i.e., the GBT, and that they essentially differ by the nature of the assumed available information. This paper also shows that both methods collapse to a kernel rule in the case of precise and categorical learning data and for certain initial assumptions, and a simple relationship between basic belief assignments produced by the two methods is exhibited in a special case. These results shed new light on the issues of classification and supervised learning in the TBM. They also suggest new research directions and may help users in selecting the most appropriate method for each particular application, depending on the nature of the information at hand.     

TGV: theory, principles and algorithms

This paper presents the TGV tool, which allows for the automatic synthesis of conformance test cases from a formal specification of a (non-deterministic) reactive system. TGV was developed by Irisa Rennes and Verimag Grenoble, with the support of the Vasy team of Inria Rhônes-Alpes. The paper describes the main elements of the underlying testing theory, which is based on a model of transitions system which distinguishes inputs, outputs and internal actions, and is based on the concept of conformance relation. The principles of the test synthesis process, as well as the main algorithms, are explained. We then describe the main characteristics of the TGV tool and refer to some industrial experiments that have been conducted to validate the approach. As a conclusion, we describe some ongoing work on test synthesis.     

A new method to extract gate coupling ratio and oxide trapped charge in flash memory cell

A new method to obtain the gate coupling ratio (α_g) and oxide trapped charge (Q_ox) as a result of cycling in flash memory cells is described here. Three cells with an equivalent physical structure but different erase characteristics are measured. The threshold changes versus erase times are fitted to the charge removal rate calculated based on Fowler-Nordheim (FN) tunneling and the capacitive relations among all nodes. The extracted α_g is independent of technologies and this method is particular useful when the profile of the floating gate is not traditionally rectangular owing to advanced processes such as trapezoidal poly etch or a poly-spacer addition on the floating gate sidewall. The Q_ox can also be determined once α_g is extracted.     

Validated design of dynamic membership services and protocols for cooperative groups

This paper first presents a groupware service and an associated protocol that manage the dynamic membership of agents inside cooperative groups and second gives the design methodology that has been used for defining the service and the protocol. The model used to represent cooperative groups is based on graphs that give the knowledge relationships between agents and define the dynamics of the group. The protocol providing the membership service is specified using the VAL formal specification environment based on a Petri net formalism. The VAL specification is verified using observational and safety projections derived from the reachability graph. Then the VAL specification is translated and adapted to an Estelle specification. The Estelle specification has been used for a final implementation of the protocol on top of a distributed platform.     

Optimal control law for classical and multiconjugate adaptive optics

Classical adaptive optics (AO) is now a widespread technique for high-resolution imaging with astronomical ground-based telescopes. It generally uses simple and efficient control algorithms. Multiconjugate adaptive optics (MCAO) is a more recent and very promising technique that should extend the corrected field of view. This technique has not yet been experimentally validated, but simulations already show its high potential. The importance for MCAO of an optimal reconstruction using turbulence spatial statistics has already been demonstrated through open-loop simulations. We propose an optimal closed-loop control law that accounts for both spatial and temporal statistics. The prior information on the turbulence, as well as on the wave-front sensing noise, is expressed in a state-space model. The optimal phase estimation is then given by a Kalman filter. The equations describing the system are given and the underlying assumptions explained. The control law is then derived. The gain brought by this approach is demonstrated through MCAO numerical simulations representative of astronomical observation on a 8-m-class telescope in the near infrared. We also discuss the application of this control approach to classical AO. Even in classical AO, the technique could be relevant especially for future extreme AO systems.     

Embedded holonic fault diagnosis of complex transportation systems

The use of electronic equipment and embedded computing technologies in modern complex transportation systems continues to grow in a highly competitive market, in which product maintainability and availability is vital. These technological advances also make fault diagnosis and maintenance interventions much more challenging, since these operations require a deep understanding of the entire system. This paper proposes a holonic cooperative fault diagnosis approach, along with a generic architecture, to increase the embedded diagnosis capabilities of complex transportation systems. This concept is applied to the fault diagnosis of door systems of a railway transportation system.     

Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent neural networks (RNNs) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine learning approaches, such as support vector machines (SVMs), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESNs), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforwardway.The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studiedyet.Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the time series. The feature selection method consists of a greedy hybrid filter-wrapper method using a NB classifier, which selects in each iteration the feature that improves prediction the best and shows little multicollinearity with the already selected set. Least squares regression with noise was used to train the linear readout function of the ESN to mitigate sensitivity to noise and overfitting. Fisher labeling was used to deal with the unbalanced data set. Parameter sweeps were performed to determine the optimal parameter values for the different classifiers. The area under the curve (AUC) and maximum balanced accuracy are used as performance measures. The required execution time was also measured.Results: The classification performance of the ESN shows significant difference at the 5% level compared to the performance of the SVM or the NB classifier combined with FE and FS. The NB+FE+FS, with an average AUC of 0.874, has the best classification performance. This classifier is followed by the ESN, which has an average AUC of 0.849. The SVM+FE+FS has the worst performance with an average AUC of 0.838. The computation time needed to pre-process the data and to train and test the classifier is significantly less for the ESN compared to the SVM andNB.Conclusion: It can be concluded that the use of ESN has an added value in predicting the need for dialysis through the analysis of time series data. The ESN requires significantly less processing time, needs no domain knowledge, is easy to implement, and can be configured using rules ofthumb.