Intelligent Component Selection System

The RACHEL software has a far greater problem. While current builder-type software packages contain databases with 1000 components or less, RACHEL can extract upward of500 000 components depending upon the size and diversity of the corporate database. Thus, the number of potential fragment combinations is nearly immeasurable. Clearly, a method is needed to rapidly focus on the appropriate combinations that are likely to satisfy binding requirements. 

The greatest benefit of RACHEL S component extraction method is that a massive property index of the entire corporate database is created. Along with the atomic coordinates of each component, a wealth of chemical information characterizing each building block is stored. Data such as the size of the component, atom composition, connectivity, ring structure, and electrostatic charges are included. As such, a means of rapidly cross-referencing chemical components on demand is available. 

This property index offers a powerful means to improve the generation of complementary ligands. Over time, builder-type programs evolve compounds with 

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The framework of presented intelligent multi-sensor system is reflected by its data processing flow as illustrated in Fig. 3. Diversified sensors in field and sophisticated algorithms make the system scalable and adaptive to different driving profiles and scenarios. Data sets of complementary sensors are synchronized on the same time base before being conveyed to feature computation components. Based on the outcome of feature computation selected data sets are fused on the Mature level to construct input vectors for pattern classification so as to detect driver drowsiness. The classifier being used in this work is built upon Artificial Neural Network (ANN) or, more particularly. Multilayer Perceptrons (MLP) with supervised training procedure. 

On the other hand the synthetic problem is the design of the whole control system, including in its broadest implication the design of process, as well as the specification of, control instruments. Before the synthetic problem can be tackled intelligently, the criteria of satisfactory control must be identified. These criteria are different for different systems, but most usually they are described in terms of the response of the system to certain stimuli. Having established the criteria of control, the problem of synthesis is one of optimizing the selection of control system components and their disposition in the control loop, so that the criteria are met. 

Johnson, K.J. Synovec, R.E. (2002). Pattern recognition of jet fuels comprehensive GCxGC with ANOVA-based feature selection and principal component analysis. Chemometrics and Intelligent Laboratory Systems, Vol.60, No.1-2, (January 2002), pp. 225-237, ISSN 0169-7439... 

Chemically modified electrodes (CMEs) are part of the so-called integrated chemical systems, gathering assemblies of a number of different chemical components, each specially selected to carry out a particular function, put together in a well-defined way in order to produce a functional structure. Compared with the conventional electrodes, the distinguishing feature of a CME is that a modifier is intelligently combined to an electrode surface to endow the electrode with the chemical, electrochemical, optical, electrical, transport, and other desirable properties of the modifier in a rational way, most often determined by the target application. 

We have decided to take an approach in which specialized hardware is employed to perform a sequential scan of the database in conjunction with a moderately efficient screen. The screening is performed in real time by an intelligent disk controller as the data passes the read head of the disk. This is followed by a very efficient atom and bond candidate selection step and then a final atom-by-atom match. These last two steps are performed on a dedicated minicomputer. The general component configuration of the SS system is shown in Figure 7. 

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