Imprecise data

Procedures and methods will be needed for retrieving and operating on other types of imprecise data. 

Fuzzy logic is often referred to as a way of "reasoning with uncertainty." It provides a well-defined mechanism to deal with uncertain and incompletely defined data, so that one can make precise deductions from imprecise data. The incorporation of fuzzy ideas into expert systems allows the development of software that can reason in roughly the same way that people think when confronted with information that is ragged around the edges. Fuzzy logic is also convenient in that it can operate on not just imprecise data, but inaccurate data, or data about which we have doubts. It does not require that some underlying mathematical model be constructed before we start to assess the data. 

Gillespie et al. [17] have pointed out that static bioassays for DDT and others toxins give relatively imprecise data and suggest improvements in methodology to overcome this. 

Figure 9-3 is an example of a simple timeline using very imprecise data from the field operator. This timeline uses a portion of the approximate data from the Fictitious NDF Incident Example discussed in detail in Appendix D. 

Identify precise and imprecise data (different colors are one way). 

However, mathematical manipulations (in this case, taking logarithms of the observed concentrations or of related physical properties) increase the uncertainty in the result by expanding errors when using imprecise data. This is specially true of data collected towards the end of a reaction when the differences in concentration between successive readings are small and decreasing. 

The value of a quantitative uncertainty analysis depends on the care given to the process of constructing probability distributions. The process of constructing a distribution from limited and imprecise data can be highly subjective. This process becomes more objective as the number of data for a given parameter increases. However, a large set of data does not necessarily imply the existence of a suitable distribution function. 

We are cognizant that the structural data on hydrogen bonds derived from crystal structure analyses refer particularly to the hydrogen bond in the solid state. These data are subject to crystal field effects caused by other intermolecular forces. Just as with any discussion of covalent or ionic bonds observed in crystals, these crystal field effects have to be taken into consideration when extrapolating from the precise data available from the crystalline state to the imprecise data that applies to the liquid state in which most chemical and biochemical reactions take place. 

What factors could account for inaccurate or imprecise data Perhaps Student A did not follow the procedure with consistency. He or she might not have read the graduated cylinder at eye level for each trial. Student C may have made the same slight error with each trial. Perhaps he or she included the mass of the filter paper used to protect the balance pan. Student B may have recorded the wrong data or made a mistake when dividing the mass by the volume. External conditions such as temperature and humidity also can affect the collection of data. 

Precision is a fundamental measure of assay performance and it should be assessed whenever possible [55]. This will allow a minimum understanding of how much confidence can be placed in the analytical data. In fully characterized assays designed to support definitive nonclinical and clinical studies, between-run and within-run assay precision of 15% or less (20% at the quantitation limit) is acceptable. Because of the shortened time scale for assay development and characterization in discovery these guidelines are excessively restrictive. Precision values of 20 to 30% are acceptable, as these are still less than intersubject variability associated with many nonclinical experiments. With this level of imprecision, data quality is sufficient to answer fundamental scientific questions such as relative levels of drug absorption, relative values of absolute bioavailability, and relative degree of drug clearance. 

The wide differences that can occur in the quality of data have serious Implications for data compilations. Unless there is some way to code and/or to associate data with its uncertainty, poor data can be unduly influential in subsequent data analysis. In detection situations imprecise data leads to large detection limits hence non-detection can have a different meaning, depending on how the measurement was made. 

In view of the imprecise data basis, a quantitative approach did not appear appropriate to aggregate the assessment of each indicator. In the results table, the aggregated results are displayed, based on the authors subjective expert opinion. 

Advanced data models, to enable DBMS users to access potentially nonconventional forms of data such as multidimensional data, graphic data, spatial data, and imprecise data... 

The fuzzy set theory introduced by Zadeh (1965) to handle problems involving a source of vagueness has been utilized for incorporating imprecise data into the decision framework (Benbemou and Warwick, 2007 Tong, 1982). It can be seen from Fig. 10.1 that the fashion rrrix-and-match problem mainly corrsists of two distinct decision-making phases attribute evaluation and overall evaluation. Due... 

Tried to get precise on imprecise data. Supply chains of the future will be based on ranges, they will dance agilely with error and adapt to changing supplier demands. The future is not the integrated supply chain instead, it is about new forms of predictive analytics to deal with uncertainty. 

We can begin producing product in commercial-sized equipment with meager, imprecise data if we use a batch or semi-batch reactor. However, once the market will support it, we must optimize the reaction or process. Optimizing any chemical reaction or process requires knowledge of the chemical kinetics underlying it. ... 

Gross (general) classification of occupations can be due to inadequate use of the respective classification system or an imprecise data source. Another reason may be the lack of refinement of the system itself, which may, however, have a depth of classification in other parts that appears unnecessary from a medical point of view, e.g., listing many subtypes of office... 

So far in this chapter, we have considered only traditional cyclic voltammetric experiments where the I-E response is recorded. Moreover, we have taken a rather simplistic view of the mechanisms of homogeneous chemical reactions, assuming that they always follow simple, limiting pathways with a single rate determining step. Studies based on such principles remain important to electrochemistry and are widespread in the literature, but they ignore many mechanistic nuances and lead to imprecise data. This was also the state of the art maybe ten years ago. 

The other important feature of balancing calculations is the danger of insufficient accuracy of results. Unmeasured flows and concentrations calculated on the basis of imprecise data are in most cases differences of big numbers. This fact influences adversely the accuracy of results and in some situation makes them useless. The confidence intervals should be an integral part of balancing results based on imprecise data. Even more dangerous is the possible occurrence of gross measurement errors which can devalue results even more seriously. The problems of confidence intervals and gross errors detection and elimination can be solved by data reconciliation. 

Fuzzy Logic This logic uses fuzzy systems instead of erisp ones therefore, it leads to obtaining preeise results. This method is very effective in regulating the uneertain and imprecision data. 

Fuzzy sets were introduced as generalizations of the classical crisp sets, in order to represent and manipulate imprecise data. However, not all the properties valid for operations on crisp sets are valid for fuzzy sets, and the inability to deal with this may result in improper use of fuzzy sets." " The basic concepts are defined below ... 

Fuzzy logic is tolerant of imprecise data. Every hydrologic event is imprecise if one looks closely enough, but most things are imprecise even on careful inspection. 

Further, partially controllable risks include data transfer between CAD systems. Although a designer cannot affect the type of exported or imported file, by means of parameter settings that are required for a particular file type such as defining tolerances, sizes, and shapes of items used, with, for example, the FEM, the designer may prevent potential risks of imprecise data transfer (3-D model enhancement) or data loss during transfer. 

Pannier S, Waurick M, Graf W, Kaliske M (2013) Solutions to problems with imprecise data—an engineering perspective to generalized uncertainty models. Mech Syst Signal Process 37(Special issue imprecise probabilities) 105-120... 

The ratio MjRT /A pHj is called the ebirlliometric constant. For the determination of solvent activities from ebulliometric data, tabulated ebirlliometric constants shonld not be nsed, however. On the other side, it is sometimes recommended to nse reference solntes to establish an experimental relationship for the equipment in use, i.e., imprecise data for the enthalpy of vaporization or perhaps some non-equilibrium effects cancel out of the calculation. Enthalpies of vaporization are provided by several data collections, e.g., by Majer and Svoboda, or through the DIPPR database. ... 

---