System Identification Practice

Answers can be found in most mineral field guides (see Appendix 7). 

Muscovite (Diamond Mica Mine, Keystone, Pennington, Scouth Carolina) 

Tetrahedrite (The Judge Tunnel, Park City, Utah) 

Grossular (near Sadiola Gold Mine, Kanzs, Mali) 

Epidote (Green Monster Mt., Prince Of Wales Island, Alaska) 

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Other recent developments in the field of adaptive control of interest to the processing industries include the use of pattern recognition in lieu of explicit models (Bristol (66)), parameter estimation with closed-loop operating data (67), model algorithmic control (68), and dynamic matrix control (69). It is clear that discrete-time adaptive control (vs. continuous time systems) offers many exciting possibilities for new theoretical and practical contributions to system identification and control. 

A major advance in the automation of specimen identification in the clinical laboratory has been the incorporation of bar coding technology into analytical systems.In practice, a bar coded label (often generated by the laboratory information system and bearing the specimen accession number) is placed onto the specimen container and is subsequently read by one or more bar code readers that have been strategically placed at key positions in the analytical train. The resultant identifying and ancillary information is then transferred to and processed by the system software. 

However, this method is not applicable to all situations. Figure 3.8 d shows that detecting the input/output characteristics at several points is not sufficient to calibrate sensitivity and offset errors if there are nonlinear effects involved. Here, a complete system identification might be necessary, depending on the order of the effects. Calibration of this kind of error is possible in theory, but not practicable, considering the expense in time and money. 

Experimental identification of process dynamics has been an active area of research for many years by workers in several areas of engineering. The literature is extensive, and entire books have been devoted to the subject. The theoretical aspects are covered in System Identification, by L. Ljung (1987, Prentice-Hall, Englewood Cliffs, NJ.) A user-friendly discussion of some of the practical aspects of identification is provided by R. C. McFarlane and D. E. Rivera in Identification of Distillation Systems, Chapter 7 in Practical Distillation Control (1992, Van Nostrand Reinhold, New York). 

Irrespective of the type of classification system used, practical classification and identification on a day to day basis must, of necessity, be based on characteristics which are easy and simple to observe and measure. One obvious example is motility, which depends on flagella. Motility/non-motility is a useful differential character, which is easy to observe using simple techniques and is therefore widely used in classification. However, the number and position of the flagella, which may be polar or peritrichous (all round the cell), is also a differential character. To observe these reliably requires an electron microscope, which is not... 

Schoukens, J. and Pintelon, R. Identification of Linear Systems A Practical Guideline for Accurate Modeling. Pergamon Press, London, 1991. 

Optimization cf Hologram for Security Applications studies optical security systems for practical applications in authentication, such as in a card system. The advantages of the optical method in a security are the fast decoding of an encrypted image and the identification of it. Firstly, the authors study a common method of joint transform correlation for optical security systems and the optimization of binary holograms, and prove that the optimization of the hologram can be a powerful tool in the enhancement of system performance. An alternative method employs a phase-coding technique which enables easier realization of practical applications in optical security systems. 

Monitoring the evolution of the features over time allows, in principle, to detect structural damage. In practice, however, this needs to be applied with care because of two reasons. Firstly, many features cannot be measured directly, but they have to be estimated from measured data using system identification techniques. Modal characteristics, for instance, can be estimated from vibration response data such as accelerations or strains, but this introduces estimation errors (Reynders et al. (2008) Reynders 2012). Secondly, nearly all features are not only sensitive to structural damage but also to changes in temperature, relative humidity, wind speed, operational loading, etc. This means that both the accuracy of the estimated features and the environmental and operational influences should be accounted for. 

Visualization techniques have not only been useful in confirming the existence of double emulsions but also in improving our understanding of various phenomena occurring in these complex systems. Microscopic techniques have enabled identification of mechanisms that were more difficult to detect with other indirect methods of investigation, yielding information that is beneficial in the design of double-emulsion systems for practical applications. 

Error analysis techniques can be used in accident analysis to identify the events and contributory factors that led to an accident, to represent this information in a clear and simple manner and to suggest suitable error reduction strategies. This is achieved in practice by identification of the causal event sequence that led to the accident and the analysis of this sequence to identify the root causes of the system malfunction. A discussion of accident analysis techniques is included in Chapter 6. 

MORT excels in terms of organizational root cause identification, as factors such as functional responsibilities, management systems and policies are well covered, but this strength of the method requires an accurate description of the incident process, and an experienced MORT analyst who is knowledgeable and well-practiced in the methodology. 

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