knowcube for MCDM Visual and Interactive Support for Multicriteria Decision Making


 Myra Page
 3 years ago
 Views:
Transcription
1 T. Hanne, H. L. Trinkaus knowcube for MCDM Visual and Interactive Support for Multicriteria Decision Making Berichte des Fraunhofer ITWM, Nr. 50 (2003)
2 FraunhoferInstitut für Techno und Wirtschaftsmathematik ITWM 2003 ISSN Bericht 50 (2003) Alle Rechte vorbehalten. Ohne ausdrückliche, schriftliche Gene h mi gung des Herausgebers ist es nicht gestattet, das Buch oder Teile daraus in irgendeiner Form durch Fotokopie, Mikrofilm oder andere Verfahren zu reproduzieren oder in eine für Maschinen, insbesondere Daten ver ar be i tungsanlagen, verwendbare Sprache zu übertragen. Dasselbe gilt für das Recht der öffentlichen Wiedergabe. Warennamen werden ohne Gewährleistung der freien Verwendbarkeit benutzt. Die Veröffentlichungen in der Berichtsreihe des Fraunhofer ITWM können bezogen werden über: FraunhoferInstitut für Techno und Wirtschaftsmathematik ITWM GottliebDaimlerStraße, Geb Kaiserslautern Telefon: +49 (0) 6 31/ Telefax: +49 (0) 6 31/ Internet:
3 Vorwort Das Tätigkeitsfeld des Fraunhofer Instituts für Techno und Wirt schafts ma the ma tik ITWM um fasst an wen dungs na he Grund la gen for schung, angewandte For schung so wie Be ra tung und kun den spe zi fi sche Lö sun gen auf allen Gebieten, die für Techno und Wirt schafts ma the ma tik be deut sam sind. In der Reihe»Berichte des Fraunhofer ITWM«soll die Arbeit des Instituts kon ti  nu ier lich ei ner interessierten Öf fent lich keit in Industrie, Wirtschaft und Wis sen  schaft vor ge stellt werden. Durch die enge Verzahnung mit dem Fachbereich Mathe ma tik der Uni ver si tät Kaiserslautern sowie durch zahlreiche Kooperationen mit in ter na ti o na len Institutionen und Hochschulen in den Bereichen Ausbildung und For schung ist ein gro ßes Potenzial für Forschungsberichte vorhanden. In die Bericht rei he sollen so wohl hervorragende Di plom und Projektarbeiten und Dis ser  ta ti o nen als auch For schungs be rich te der Institutsmitarbeiter und In s ti tuts gäs te zu ak tu el len Fragen der Techno und Wirtschaftsmathematik auf ge nom men werden. Darüberhinaus bietet die Reihe ein Forum für die Berichterstattung über die zahlrei chen Ko o pe ra ti ons pro jek te des Instituts mit Partnern aus Industrie und Wirtschaft. Berichterstattung heißt hier Dokumentation darüber, wie aktuelle Er geb nis se aus mathematischer For schungs und Entwicklungsarbeit in industrielle An wen dun gen und Softwareprodukte transferiert wer den, und wie umgekehrt Probleme der Praxis neue interessante mathematische Fragestellungen ge ne rie ren. Prof. Dr. Dieter PrätzelWolters Institutsleiter Kaiserslautern, im Juni 2001
4
5 knowcube for MCDM Visual and Interactive Support for Multicriteria Decision Making Hans L. Trinkaus, Thomas Hanne Fraunhofer Institute for Industrial Mathematics (ITWM) Department of Optimization GottliebDaimlerStr Kaiserslautern Germany Abstract In this paper, we present a novel multicriteria decision support system (MCDSS), called knowcube, consisting of components for knowledge organization, generation, and navigation. Knowledge organization rests upon a database for managing qualitative and quantitative criteria, together with addon information. Knowledge generation serves filling the database via e.g. identification, optimization, classification or simulation. For finding needles in haycocks, the knowledge navigation component supports graphical database retrieval and interactive, goaloriented problem solving. Navigation helpers are, for instance, cascading criteria aggregations, modifiable metrics, ergonomic interfaces, and customizable visualizations. Examples from reallife projects, e.g. in industrial engineering and in the life sciences, illustrate the application of our MCDSS. Key words: Multicriteria decision making, knowledge management, decision support systems, visual interfaces, interactive navigation, reallife applications. 1 Introduction Usually, reallife decision problems are characterized by several criteria or objectives to be taken into consideration. Despite the fact that multiple objectives are all around us, as Zeleny (1982) points out, the dissemination of methods for multicriteria decision making (MCDM) into practice can still be regarded Corresponding author 1
6 knowcube for MCDM 2 as insufficient. A basic reason for that situation might be that practitioners are just not familiar with MCDM methods, which often are difficult to understand and to work at. One main difficulty for an easy and explorative usage of methods might be caused by the user interface, i.e. the handling of interactions with a corresponding computer program. Zionts (1999) is pointing out the necessity of userfriendly software in the following way: I strongly believe that what is needed is a spreadsheet type of method that will allow ordinary people to use MCDM methods for ordinary decisions. In this paper, we present a novel multicriteria decision support system (MCDSS), called knowcube, which mainly originates from focussing on userspecific needs. Therefore, the first aspect of the system is the provision of visualizations and interactivity in categories well understandable by nonexpert users. All technical stuff, necessary for this comfortable presentation of and access on knowledge, is hidden in the background, combined in a socalled navigation component. Its integration with further components, for generating and managing knowledge data required for decision processes, is another main feature of this MCDSS. The paper is organized as follows: Section 2 introduces some notation for multicriteria decision problems. (Here we restrict ourselves to tasks dealing only with quantitative criteria, other types of criteria will be topics of a paper to follow.) In Section 3, we present a survey of components of knowcube, and additionally some details on the generation and organization of decision knowledge. MCDMspecifics of knowcube, including a short excursion to human cognition, are discussed in Section 4. Two examples show the application of knowcube in reallife projects, one in the field of industrial engineering (Section 5), another one in medical radiation therapy (Section 6). The paper ends with some conclusions and an outlook to future work (Section 7), acknowledgements and references (Sections 8 and 9), and an appendix (Section10), meant to convince the sceptical reader of some ideas presented in the paper. 2 Multicriteria Decision Problems In the following we assume multicriteria decision problems (A, f) defined by a feasible set of alternatives A and a vectorvalued (q dimensional) objective function f =(f 1,..., f q ). For an alternative a A, f j (a),j {1,..., q}, represents the evaluation of the jth objective (also called criterion or attribute). Each objective function f j is assumed to be either maximized: max a A f j (a), or minimized: min a A f j (a).
7 knowcube for MCDM 3 If A is a finite set, i.e. A = {a 1,..., a r }, we also use the concise notation z ij = f j (a i ) fordenotingthejth criterion value for alternative a i. Here, Z =(z ij ) i=1..r,j=1..q is also denoted as decision matrix. In other cases, A may be a subset of a vector space, A R n, defined by a vectorvalued restriction function g : R n R m : A = {x R n : g(x) 0}. In practice, such a problem may be rather complex. For instance, restrictions defining the feasible set may be given by analytic formulas or differential equations. Usually, there does not exist a solution which optimizes all q objectives at the same time. Therefore, from a mathematical point of view, one may consider the set of efficient or Paretooptimal solutions as results to the above problem. For two solutions a, b A, wesaythata dominates b, iff(a) f(b) and { fj (a) f j (b), if f j is to be maximized, f j (a) f j (b), if f j is to be minimized, for each j {1,..., q}. The efficient set is then defined by E(A, f) :={a A : b A : b dominates a}. Since this efficient set normally contains too many solutions, its computation does not really solve the multicriteria decision problem from a practical point of view because a decision maker usually wants to get a single good solution, e.g. by choosing one from an easily to survey set of meaningful alternatives. For that purpose, an enormous number of methods for practically solving multicriteria decision problems has been developed in the meantime. Overviews and details referring to this are given, for instance, in Hanne (2001), Steuer (1986), Vincke (1992), or Zeleny (1982). One idea for example, common to some of these solution approaches, is to use additional preferencerelated information (stated by the decision maker) on the multicriteria problem, which then may result in obtaining a single solution. Popular concepts of these methods are taking specific values of the objectives (which may or may not correspond to feasible solutions) to compare solutions with. The earliest technique in MCDM using a desired solution ( the goal ) and minimizing the alternatives distance to it is goal programming, developed as an extension of linear programming. Today, there are many new methods which
8 knowcube for MCDM 4 utilize various kinds of desired and undesired solutions, different metrics, and additional features. Usually, these methods are called reference point approaches (Wierzbicki (1998)). In absence of userspecified reference points, the following two concepts are frequently used: The utopia (or ideal) point u is defined by the componentwise best values of the alternatives in objective space, i.e. { maxa A f u j = j (a), if f j is to be maximized, min a A f j (a), if f j is to be minimized, for j {1,..., q}. The nadir (or antiutopia) point v is defined by the componentwise worst values, but considering only efficient alternatives, i.e. for j {1,..., q}. { mina E(A,f) f v j = j (a), if f j is to be maximized, max a E(A,f) f j (a), if f j is to be minimized, Based on Simon s (1957) theory of satisfying, which was developed as an alternative to utility maximization, concepts using aspiration and reservation levels (or thresholds) became popular in multicriteria decision making. Taking aspiration levels for MCDM has first been pointed out by Tversky (1972a, 1972b). His approach of a successive elimination by aspects, is based on the idea that human decision making frequently works as a stochastic process for exploring alternatives. By choosing minimum requirements for the outcomes in specific criteria, the decision space A is reduced while the decision maker learns more about the feasible alternatives. Some features of the navigation process described below in this paper may be considered as an extension and userfriendly implementation of Tversky s basic concepts. Considering the limited capabilities of reallife decision makers, nowadays there is a significant number of methods providing ergonomic interactivity and means for visualization. Some of these are, for instance, described in Belton and Vickers (1993), Korhonen (1987, 1988), Korhonen and Laakso (1986a, 1986b), Korhonen and Wallenius (1988). A specific visualization technique used in our MCDSS, is called radar chart, spider web or spider chart. It is based on the idea of representing several axes in a starshaped fashion. An object characterized by multiple attributes then may be represented in the diagram as a polygon, resulting from connecting lines between the attribute scores on neighboring axes. Although such kinds of diagrams are rather common in statistics and brainstorming techniques, only few MCDM methods so far utilize this approach. And there are some additional new features in our MCDSS, as for example the fact, that the polygon representing an alternative by its objective values as corner points has got life : Online
9 knowcube for MCDM 5 mousemanipulations on the screen enable an interactive navigation through the database of decision alternatives. By talking so much about visualization, it should be appropriate just to do this as the sketch shown in Figure 1 perfectly illustrates the scope of knowcube: A decision maker, comfortably interacting (by ear, eye, mouth, or hand) with an MCDSS! black / white / hybrid multi criteria system Figure 1: One picture means more than thousand words. More explanations and especially practical details will be given in the next sections, here we only want to look still somewhat closer on the core of the multicriteria decision task, where we distinguish the following cases: If the description of the problem is known and completely given, it is called a white (box) multicriteria problem. This is the case mostly assumed in multicriteria optimization methods, applied for finding the best of all possible solutions. If nothing is known about structure of or dependencies within the multicriteria problem, it is denoted as black. Typically, outcomes of such systems are, for example, found by trial and error, or won by general inquiries. Frequently, such results are not reproducible, and in many cases all results are evolving in the sense that the problem settings are changing with time. Most real decision situations however are somewhere in between black and white, which lead to hybrid multicriteria problems. Then carrying out costly experiments and importing the results obtained into the database are the means chosen very often. But also simulation methods gain in increasing significance for getting decision suggestions. As already indicated in Section 1, here we are considering only quantitative criteria variables to keep it simple, to introduce the main concepts of knowcube,
10 knowcube for MCDM 6 as a first step, and to have place for explaining the basics by examples. For working with other types of criteria like qualitative, objective/subjective, rational/irrational, active/passive, dependent/independent, deterministic/statistic, hard/soft, timely,..., and all mixed together the topics introduced so far must be slightly extended. This will happen in a paper to follow. 3 Scope of knowcube, Knowledge Organization and Knowledge Generation knowcube, seen as a general framework of an MCDSS, consists of three main components: knoworg, knowgen, and knownav (knowledge organization, generation, and navigation). These are put together with their sub modules into a common box, showing to various decision makers varied views like the different faces of a cube. This gives a hint at the term knowledge cube, abbreviated knowcube, taken as a logo for the complete MCDSS. Figure 2 illustrates its structure. knowcube knoworg knowblo knowbas knowmet knowsto knowgen knowopt knowexp knowsim knowimp knownav knowvis knowsel knowdis Figure 2: The Structure of knowcube. The bricks shown there may act selfcontained up to a certain extent, but they also are designed for efficient interaction in the configuration of special
11 knowcube for MCDM 7 buildings for specific needs. So knowcube is customizable for use as a decision support system in various application domains. This will become more evident in Sections 5 and 6, where two examples are presented, one in the field of industrial engineering, another one from among medical applications. For an easy integration of knowcube into an institution s workflow, the component knoworg provides comfortable tools for data collection, recording, administration, and maintenance and all that with comparatively little effort. Open data interfaces enable information exchange with external data sources, such that the database of knowcube may be extended by arbitrary external, and also historic, decision support information. These topics mainly are handled by the modules knowbas (knowledge base) and knowsto (knowledge storage). (More details will be worked out in a future paper.) Making decisions presupposes some knowledge. So, in any case, a data base must be filled with decision information, where its fundamental unit is called knowblo (knowledge block). Such a block consists of two parts, one containing all those criteria values corresponding to a specific decision alternative (e.g. a row of the decision matrix Z, in case of a finite set of alternatives A), the other one references to additional information attachments. Those may be, for instance, documents, diagrams, graphics, videos, audios,..., any media are allowed. The usage of criteria and attachment information is best explained within application contexts, which will be shown later in the examples already mentioned. The last brick in the knoworg component to talk about is knowmet (knowledge metric). Here, the singular of the word metric is a bit misleading: There is not only one metric or one class of similar metrics defined on the data base, but additionally a big family of metric like orderings. For instance, all l p metrics (1 p ) may be used, at least for quantitative criteria blocks, together with superimposed priority rankings which furthermore are online user adaptive. Besides that, certain filtering functions allow some clusterings and inclusions or exclusions of sets of data blocks. So far for the first component of knowcube, corresponding to the data base symbol on the top in the sketch of Figure 1. There the dotted lines indicate, that the decision maker usually does not take care about these interrelations, they must act silently on the back stage. An engaged decision maker or, at least, the decision provider and analyst will show more interest in the second component knowgen (knowledge generation), which comprises the framework for producing decision information to fill the data base for a given multicriteria problem. Thereby, the main tasks are to formulate and answer questions such as e.g.: What kind of system will model the decision situation? Is the model static or does it change in course of time?
12 knowcube for MCDM 8 Which criteria are identifiable? How may the criteria variables be arranged in groups? Could information be aggregated following a topdown approach? Which addon media should be attached for supporting decision processes? After these preliminaries the interfaces between the acute knowledge generation domain and the components knoworg and knownav are adjusted. Then, one or more of the following activities may be started, corresponding to the bricks of knowgen in Figure 2: Optimization calculations (in the white system case) delivering e.g. Pareto solutions as outcomes of the module knowopt (knowledge optimization). Performing experiments (in the hybrid system case) observing results, e.g. from optimal experimental design methods, as produced by knowexp (knowledge experiments). Doing simulations (again in the hybrid system case) getting e.g. data records with a mixture of some hard/soft criteria, where the input and output values are generated by some standard simulation software tools, gathered in knowsim (knowledge simulation). Collecting data events (in the black system case) e.g. by reading information from external sources via knowimp (knowledge import). Using some other problem specific method which is not discussed above, and indicated in Figure 2 by dotted lines. In any case, the modules knowmet and knowsto are closely working together, if some newly generated or acquired decision knowledge (knowblo) is put into the data base (knowbas). The data base filling processes may be organized via batch procedures, interactively, and also in an evolving way, i.e. within several time slots and under various external conditions exactly as in real life, and also with the well known effect that old decisions are overtaken by new ones. As illustrated in Figure 1, knowledge generation happens mainly in the center of knowcube. So for example, by feeding or controlling the black/white/hybridsystem (possibly also by acoustic commands), and by receiving or observing corresponding system answers. A more distinct explanation of so far introduced concepts will be given below, especially in context with the two already mentioned application examples after a short break:
13 knowcube for MCDM 9 4 About Visual Data Processing and Human Cognition Towards knowcube Navigation Some simple tests (as given by the Appendix in Section 10) and thereof derived conclusions will motivate the design and functionalities of the visual interface of knowcube in general, and especially the ideas of the socalled knowcube Navigator. Let us summarize some basic principles of visual data processing which are well known long ago, and may be found e.g. in Cognitive Psychology by Wickelgren (1979) by three statements: Statement 1: Reading text information or listening to a speaker is not sufficient to generate knowledge for solving problems or making decisions! Statement 2: The human visual system has a highly efficient information processing architecture. It allows inferences to be drawn based on visual spatial relationships fast and with small demands on working space capacity! Statement 3: The human visual system is also highly sensitive for moving objects and changing shapes! Quintessence: Supporting decision making should appeal to the human visual system, transforming complex situations into timeanimated, spatial presentations! So, visual spatial relationships, moving objects and changing shapes all these general topics should be integrated in a tool for supporting decision making, at the least. But, additionally to that, such a tool asks for some more specific and practical demands on its man/machine interface. These requirements, gained by experience and evaluated by experiments, will be formulated in the sequel as axioms, and visualized by easy examples. The first step in the process of optical perception is the division of a picture into a figure, which comes by a filtering process to consciousness, and into the background, which is no longer of interest. The main question for designing visual interfaces based on figures is: What are the main principles for recognizing figures out of arbitrary forms? Many of the answers given below as axioms already have been discovered during the development of Gestalt Psychology, as a theory of perception by Wertheimer (1923) and others, during the 1910s and 20s. Nowadays, these ideas are common knowledge, and extensively used in multimedia applications; cf. Macromedia Director 7 (2000). Axiom 1: Closedness. The most elementary forms that are interpreted as figures are surrounded by closed curves.
14 knowcube for MCDM 10 Axiom 2: Closeness. Objects close together are seen as figures, distant objects are assigned to the background. An example is given in Figure 3. Each sketch contains eight domains, which are all closed. But they are hardly perceived all at the same time. The sketch on the left shows a cross standing upright, on the right it is inclined. Narrow parts are taken together as the arms of the crosses, broader parts within the sketches are seen as interspaces or background. (Only with a certain effort it is possible to discover an upright cross with broad arms in the sketch on the right, too.) Figure 3: Picture, Figure and Background. Now knowing the two basic principles of identifying figures: closedness and closeness, the question arises: Which principle dominates? Axiom 3: Closedness versus closeness. If closedness and closeness are competing, it s more likely that closed forms are taken as the figures looked for. Figure 4 should convince us: Broader domains become figures, because they are closed. The narrow sectors no longer appear as arms of a cross, since little parts of their boundaries are missing. Figure 4: Closedness versus Closeness.
15 knowcube for MCDM 11 Finally, to get closer to our goal: the knowcube Navigator, wepresentasa last concept needed: Axiom 4: Convexity. All convex forms or roughly convex forms as e.g. starlike shapes are particularly easy recognized as figures. This was already realized by v. Hornbostel (1922). Here is an example: Figure 5: Starlike Shape or Nearly Convexity. Though not looking quite nice, the polygon bounding a nearly convex domain is eyecatching. The line segments, oriented radially towards a common invisible center, are obtaining less attention. This contrast will still increase, if the line segments stay at rest, and the polygon starts to move. Such a polygonal representation is the most central visualization tool of knowcube s main component knownav, which provides the core navigation tools. So, we are getting closer now towards knowcube navigation: Each variable is related to a line segment, the line segments are arranged like a glory, each segment shows a scale (linear or something else), where small values always are put towards the center to avoid confusion. Each segment gets an arrowhead pointing towards that direction (bigger or smaller values), which is preferred by the decision maker for the corresponding criterion. Little circles on the segments represent the current parameter values of a data base record or decision alternative, respectively. Adjacent circles are connected by straight lines, all together defining a closed polygon, which bounds a convex or at least starshaped region. By this little trick the observer or decision maker comprehends what belongs together. Summing all up: A closed polygon within a starshaped scale arrangement visualizes a record in the data base knowbas. Figure 6 corresponds to a decision task, where eight criteria are involved.
16 knowcube for MCDM 12 Three of them are to be minimized, the other ones are to be maximized. (For the sake of simplicity, the scales on the segments are omitted here but they are shown in the second application example in Section 6.) The order of the criteria s arrangement may, for instance, reflect their importance, or it may be chosen interactively according to the user s preferences. Figure 6: A Decision Alternative, Represented in knowcube. To the end (of this Section) two questions still are open: How are information attachments supporting the decision making? What about the dynamic behavior of polygons? Best answers will be given by two already announced reallife applications: 5 Designing Best Trunking Devices We consider the industrial engineering problem of designing the profile of a new electrical trunking device that is usually fixed at the wall, and used for a comfortable laying of multitudes of cables. The problem is to find a best crosssection profile for such trunking devices. Many objectives are to be taken into account in such a development process. Among them there is one group of criteria, which should be minimized: a m, the total amount of material needed for one unit of the trunking device; t p, the time to produce one unit; c u, the overall cost for one unit. For a better understanding of a second group of variables, Figure 7 will help, showing in
17 knowcube for MCDM 13 front the approximately Ushaped crosssection of a trunking device. r p points to the resistance of the trunking device against pressing from opposite sides, r t to its resistance against torsion. These two variables should have big values, of course. Figure 7: A Trunking Device. The five criteria introduced so far may be considered as characterizing the outcome of a specific design for a trunking device. Their resulting values obviously are caused by a third group of (in a certain sense independent ) variables: t b denotes the thickness of the bottom face, t s the thickness of the side face of the device. A last variable p r (not visualized, but only mentioned in Figure 7) specifies the portion of recycling material, used e.g. for producing PVC trunking devices. All these eight variables are working together, in nontrivial, highly nonlinear, and partially conflicting combinations. Figure 8: Visualization of an Alternative.
18 knowcube for MCDM 14 Assuming now, that the engineering team has designed plenty of different profile versions, evaluated them virtually (e.g. by some finite element methods out of knowsim), according to the above criteria (which correspond to f 1,..., f 8, as introduced in Section 2), and put all together with some operational data into the data base, then some data blocks could be presented as shown in Figures Figure 9: Design Alternative 98. Figure 10: Design Alternative 746. With the generation of these data, the job of the engineering team is done. Now the time has come for the management of the enterprise to decide which new type of trunking devices is going to be launched. But, of course, a top manager would not want to look over hundreds of data sheets. Here the knowcube Navigator supports the user to stroll easily, quickly, and goal oriented through the data base. By pulling at a vertex of the navigation polygon, at a socalled grip point, towards smaller or larger values (inwards or outwards), the polygon moves or transforms to a neighboring polygon, representing another element of the data base. In the background the module knowmet is looking online for that new data record, which in the first place is different but closest to the old one, with respect to the criterion that was gripped, and which in the second place is
19 knowcube for MCDM 15 closest to the old record, with respect to all other criteria. It certainly would be an essential advantage for the top manager always to know where he/she is, and which goals are achievable in the best case. An additional graphical assistance will help here: For each criterion there are taken the respective values from all records in the data base. Virtually, the resulting point set is plotted on the corresponding line segment. The smallest interval containing such a set defines a decision range of potential alternatives for that specific criterion. Then connecting inner (neighboring) interval endpoints by straight lines determines the inner decision boundary, the outer decision boundary results by analogy. Both together construct the decision horizon. The metaphor horizon is used, because the decision maker knows and sees in advance that each possible alternative, respectively selection of a data record or corresponding polygon representation, will lie somewhere within this area that is emphasized in the sketch in Figure 11 by some color shading. (The utopia point u, mentioned in Section 2, corresponds to that polygon, which connects those boundary points of the shaded area, lying on the variables line segments and closest to their arrow heads. The nadir point v results vice versa.) Figure 11: What Is Achievable What Not?. By the way, the decision horizon serves as an ideal base for discussions at meetings, where many decision makers are fighting with plenty, usually conflicting, arguments. Acute viewpoints may be ranged in easily by the location of the navigation polygon, extreme positions may be estimated by the inner and outer boundaries. Alternatives, their pros and cons, are visualized interactively. The attachment knowledge, as e.g. the sketches of the cross sections in
Planning for home health care services
S. Nickel, M. Schröder, J. Steeg Planning for home health care services Berichte des Fraunhofer ITWM, Nr. 173 (2009) FraunhoferInstitut für Techno und Wirtschaftsmathematik ITWM 2009 ISSN 14349973 Bericht
More informationBlocked neural networks for knowledge extraction in the software development process
P. Lang, A. Sarishvili, A. Wirsen Blocked neural networks for knowledge extraction in the software development process Berichte des Fraunhofer ITWM, Nr. 56 (2003) FraunhoferInstitut für Techno und Wirtschaftsmathematik
More informationStochastic programming approaches for risk aware supply chain network design problems
S. Nickel, F. SaldanhadaGama, H.P. Ziegler Stochastic programming approaches for risk aware supply chain network design problems Berichte des Fraunhofer ITWM, Nr. 181 (2010) FraunhoferInstitut für
More informationQuantification of the effectiveness of a safety function in passenger vehicles on the basis of realworld accident data
J.P. Kreiss, T. Zangmeister Quantification of the effectiveness of a safety function in passenger vehicles on the basis of realworld accident data Berichte des Fraunhofer ITWM, Nr. 203 (2011) FraunhoferInstitut
More informationA MultiObjective Evolutionary Algorithm for Scheduling and Inspection Planning in Software Development Projects
T. Hanne, S. Nickel A MultiObjective Evolutionary Algorithm for Scheduling and Inspection Planning in Software Development Projects Berichte des Fraunhofer ITWM, Nr. 42 (2003) FraunhoferInstitut für
More informationBringing robustness to patient flow management through optimized patient transports in hospitals
T. Hanne, T. Melo, S. Nickel Bringing robustness to patient flow management through optimized patient transports in hospitals Berichte des Fraunhofer ITWM, Nr. 131 (2007) FraunhoferInstitut für Techno
More informationTowards a Unified Territory Design Approach Applications, Algorithms and GIS Integration
J. Kalcsics, S. Nickel, M. Schröder Towards a Unified Territory Design Approach Applications, Algorithms and GIS Integration Berichte des Fraunhofer ITWM, Nr. 71 (2005) FraunhoferInstitut für Techno
More informationVisualization methods for patent data
Visualization methods for patent data Treparel 2013 Dr. Anton Heijs (CTO & Founder) Delft, The Netherlands Introduction Treparel can provide advanced visualizations for patent data. This document describes
More informationSimulating Human Resources in Software Development Processes
T. Hanne, H. Neu Simulating Human Resources in Software Development Processes Berichte des Fraunhofer ITWM, Nr. 64 (2004) FraunhoferInstitut für Techno und Wirtschaftsmathematik ITWM 2004 ISSN 14349973
More informationAdaptive Business Intelligence
Adaptive Business Intelligence Bearbeitet von Zbigniew Michalewicz, Martin Schmidt, Matthew Michalewicz, Constantin Chiriac 1. Auflage 2006. Buch. xiii, 246 S. Hardcover ISBN 978 3 540 32928 2 Format (B
More informationMulticriteria optimization in intensity modulated radiotherapy planning
K.H. Küfer, M. Monz, A. Scherrer, P. Süss, F. Alonso, A. S. A. Sultan, Th. Bortfeld, D. Craft, Chr. Thieke Multicriteria optimization in intensity modulated radiotherapy planning Berichte des Fraunhofer
More informationContrast enhancement of soft tissues in Computed Tomography images
Contrast enhancement of soft tissues in Computed Tomography images Roman Lerman, Daniela S. Raicu, Jacob D. Furst Intelligent Multimedia Processing Laboratory School of Computer Science, Telecommunications,
More informationThe Olympus stereology system. The Computer Assisted Stereological Toolbox
The Olympus stereology system The Computer Assisted Stereological Toolbox CAST is a Computer Assisted Stereological Toolbox for PCs running Microsoft Windows TM. CAST is an interactive, userfriendly,
More informationS. Hartmann, C. Seiler, R. Dörner and P. Grimm
&DVH6WXG\9LVXDOL]DWLRQRI0HFKDQLFDO3URSHUWLHVDQG 'HIRUPDWLRQVRI/LYLQJ&HOOV S. Hartmann, C. Seiler, R. Dörner and P. Grimm Fraunhofer Anwendungszentrum für Computergraphik in Chemie und Pharmazie Varrentrappstraße
More informationSTRAND: Number and Operations Algebra Geometry Measurement Data Analysis and Probability STANDARD:
how August/September Demonstrate an understanding of the placevalue structure of the baseten number system by; (a) counting with understanding and recognizing how many in sets of objects up to 50, (b)
More informationCOMBINING THE METHODS OF FORECASTING AND DECISIONMAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES
COMBINING THE METHODS OF FORECASTING AND DECISIONMAKING TO OPTIMISE THE FINANCIAL PERFORMANCE OF SMALL ENTERPRISES JULIA IGOREVNA LARIONOVA 1 ANNA NIKOLAEVNA TIKHOMIROVA 2 1, 2 The National Nuclear Research
More informationComparison of Nonlinear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data
CMPE 59H Comparison of Nonlinear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Nonlinear
More informationDevelopment of a HalfAutomated Product Database for Search Engine Optimization. Masterarbeit
Development of a HalfAutomated Product Database for Search Engine Optimization Masterarbeit zur Erlangung des akademischen Grades Master of Science (M.Sc.) im Studiengang Wirtschaftswissenschaft der Wirtschaftswissenschaftlichen
More information3. Interpolation. Closing the Gaps of Discretization... Beyond Polynomials
3. Interpolation Closing the Gaps of Discretization... Beyond Polynomials Closing the Gaps of Discretization... Beyond Polynomials, December 19, 2012 1 3.3. Polynomial Splines Idea of Polynomial Splines
More informationCurrent Standard: Mathematical Concepts and Applications Shape, Space, and Measurement Primary
Shape, Space, and Measurement Primary A student shall apply concepts of shape, space, and measurement to solve problems involving two and threedimensional shapes by demonstrating an understanding of:
More informationThe NotFormula Book for C1
Not The NotFormula Book for C1 Everything you need to know for Core 1 that won t be in the formula book Examination Board: AQA Brief This document is intended as an aid for revision. Although it includes
More informationCommon Core Unit Summary Grades 6 to 8
Common Core Unit Summary Grades 6 to 8 Grade 8: Unit 1: Congruence and Similarity 8G18G5 rotations reflections and translations,( RRT=congruence) understand congruence of 2 d figures after RRT Dilations
More informationPA Common Core Standards Standards for Mathematical Practice Grade Level Emphasis*
Habits of Mind of a Productive Thinker Make sense of problems and persevere in solving them. Attend to precision. PA Common Core Standards The Pennsylvania Common Core Standards cannot be viewed and addressed
More informationInteractive Math Glossary Terms and Definitions
Terms and Definitions Absolute Value the magnitude of a number, or the distance from 0 on a real number line Additive Property of Area the process of finding an the area of a shape by totaling the areas
More informationP F  M P C: Particle FilterModel Predictive Control
D. Stahl, J. Hauth P F  M P C: Particle FilterModel Predictive Control Berichte des Fraunhofer ITWM, Nr. 201 (2011) FraunhoferInstitut für Techno und Wirtschaftsmathematik ITWM 2011 ISSN 14349973
More informationA Game of Numbers (Understanding Directivity Specifications)
A Game of Numbers (Understanding Directivity Specifications) José (Joe) Brusi, Brusi Acoustical Consulting Loudspeaker directivity is expressed in many different ways on specification sheets and marketing
More informationInteractive simulation of an ash cloud of the volcano Grímsvötn
Interactive simulation of an ash cloud of the volcano Grímsvötn 1 MATHEMATICAL BACKGROUND Simulating flows in the atmosphere, being part of CFD, is on of the research areas considered in the working group
More informationMARKETING ENGINEERING FOR EXCEL TUTORIAL VERSION
MARKETING ENGINEERING FOR EXCEL TUTORIAL VERSION 141204 Tutorial Positioning Marketing Engineering for Excel is a Microsoft Excel addin. The software runs from within Microsoft Excel and only with data
More informationVisualization of 2D Domains
Visualization of 2D Domains This part of the visualization package is intended to supply a simple graphical interface for 2 dimensional finite element data structures. Furthermore, it is used as the low
More informationData Mining for Manufacturing: Preventive Maintenance, Failure Prediction, Quality Control
Data Mining for Manufacturing: Preventive Maintenance, Failure Prediction, Quality Control Andre BERGMANN Salzgitter Mannesmann Forschung GmbH; Duisburg, Germany Phone: +49 203 9993154, Fax: +49 203 9993234;
More informationInformation Visualization WS 2013/14 11 Visual Analytics
1 11.1 Definitions and Motivation Lot of research and papers in this emerging field: Visual Analytics: Scope and Challenges of Keim et al. Illuminating the path of Thomas and Cook 2 11.1 Definitions and
More informationFor example, estimate the population of the United States as 3 times 10⁸ and the
CCSS: Mathematics The Number System CCSS: Grade 8 8.NS.A. Know that there are numbers that are not rational, and approximate them by rational numbers. 8.NS.A.1. Understand informally that every number
More informationSo today we shall continue our discussion on the search engines and web crawlers. (Refer Slide Time: 01:02)
Internet Technology Prof. Indranil Sengupta Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Lecture No #39 Search Engines and Web Crawler :: Part 2 So today we
More informationIn mathematics, there are four attainment targets: using and applying mathematics; number and algebra; shape, space and measures, and handling data.
MATHEMATICS: THE LEVEL DESCRIPTIONS In mathematics, there are four attainment targets: using and applying mathematics; number and algebra; shape, space and measures, and handling data. Attainment target
More informationOn the advancement and software support of decision making in focused ultrasound therapy
A. Scherrer, S. Jakobsson, K.H. Küfer On the advancement and software support of decision making in focused ultrasound therapy Berichte des Fraunhofer ITWM, Nr. 247 (2015) FraunhoferInstitut für Techno
More informationMOBILITY DATA MODELING AND REPRESENTATION
PART I MOBILITY DATA MODELING AND REPRESENTATION 1 Trajectories and Their Representations Stefano Spaccapietra, Christine Parent, and Laura Spinsanti 1.1 Introduction For a long time, applications have
More informationThe Scientific Data Mining Process
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
More informationOptimising Patient Transportation in Hospitals
Optimising Patient Transportation in Hospitals Thomas Hanne 1 Fraunhofer Institute for Industrial Mathematics (ITWM), FraunhoferPlatz 1, 67663 Kaiserslautern, Germany, hanne@itwm.fhg.de 1 Introduction
More information3D Interactive Information Visualization: Guidelines from experience and analysis of applications
3D Interactive Information Visualization: Guidelines from experience and analysis of applications Richard Brath Visible Decisions Inc., 200 Front St. W. #2203, Toronto, Canada, rbrath@vdi.com 1. EXPERT
More informationan introduction to VISUALIZING DATA by joel laumans
an introduction to VISUALIZING DATA by joel laumans an introduction to VISUALIZING DATA iii AN INTRODUCTION TO VISUALIZING DATA by Joel Laumans Table of Contents 1 Introduction 1 Definition Purpose 2 Data
More informationLearning in Abstract Memory Schemes for Dynamic Optimization
Fourth International Conference on Natural Computation Learning in Abstract Memory Schemes for Dynamic Optimization Hendrik Richter HTWK Leipzig, Fachbereich Elektrotechnik und Informationstechnik, Institut
More informationFUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM
International Journal of Innovative Computing, Information and Control ICIC International c 0 ISSN 3448 Volume 8, Number 8, August 0 pp. 4 FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT
More informationUNCERTAINTY TREATMENT IN STOCK MARKET MODELING
UNCERTAINTY TREATMENT IN STOCK MARKET MODELING Stanislaw Raczynski Universidad Panamericana Augusto Rodin 498, 03910 Mexico City, Mexico email: stanracz@netservice.com.mx Abstract A model of stock market
More informationSoftware Engineering Prof. N.L. Sarda Computer Science & Engineering Indian Institute of Technology, Bombay Lecture4 Overview of Phases (Part  II)
Software Engineering Prof. N.L. Sarda Computer Science & Engineering Indian Institute of Technology, Bombay Lecture4 Overview of Phases (Part  II) We studied the problem definition phase, with which
More informationVirtual Mouse Implementation using Color Pointer Detection
International Journal of Research Studies in Science, Engineering and Technology Volume 1, Issue 5, August 2014, PP 2332 ISSN 23494751 (Print) & ISSN 2349476X (Online) Virtual Mouse Implementation using
More informationTHE LEADERSHIP ASSESSMENT THAT ILLUMINATES LEADER EFFECTIVENESS
THE LEADERSHIP ASSESSMENT THAT ILLUMINATES LEADER EFFECTIVENESS CONNECTING PATTERNS OF ACTION WITH HABITS OF THOUGHT The Leadership Circle Profile (LCP) is a true breakthrough among 360 degree profiles.
More informationEngineering Problem Solving and Excel. EGN 1006 Introduction to Engineering
Engineering Problem Solving and Excel EGN 1006 Introduction to Engineering Mathematical Solution Procedures Commonly Used in Engineering Analysis Data Analysis Techniques (Statistics) Curve Fitting techniques
More informationVisualizing the Teaching / Learning Process through Computer Graphics. Visualizing, technology, computer graphics, education
Visualizing the Teaching / Learning Process through Computer Graphics 1 Aghware F. O.; 2 Egbuna E. O.; 3 Aghware A. and 4 Ojugo Arnold 1, 2, 3 Computer Science Department, College of Education, Agbor 4
More informationNEW VERSION OF DECISION SUPPORT SYSTEM FOR EVALUATING TAKEOVER BIDS IN PRIVATIZATION OF THE PUBLIC ENTERPRISES AND SERVICES
NEW VERSION OF DECISION SUPPORT SYSTEM FOR EVALUATING TAKEOVER BIDS IN PRIVATIZATION OF THE PUBLIC ENTERPRISES AND SERVICES Silvija Vlah Kristina Soric Visnja Vojvodic Rosenzweig Department of Mathematics
More informationUnit 6 Geometry: Constructing Triangles and Scale Drawings
Unit 6 Geometry: Constructing Triangles and Scale Drawings Introduction In this unit, students will construct triangles from three measures of sides and/or angles, and will decide whether given conditions
More informationBusiness Intelligence and Decision Support Systems
Chapter 12 Business Intelligence and Decision Support Systems Information Technology For Management 7 th Edition Turban & Volonino Based on lecture slides by L. Beaubien, Providence College John Wiley
More informationInternational Year of Light 2015 TechTalks BREGENZ: Mehmet Arik WellBeing in Office Applications Light Measurement & Quality Parameters
www.ledprofessional.com ISSN 1993890X Trends & Technologies for Future Lighting Solutions ReviewJan/Feb 2015 Issue LpR 47 International Year of Light 2015 TechTalks BREGENZ: Mehmet Arik WellBeing in
More informationChapter 15 Introduction to Linear Programming
Chapter 15 Introduction to Linear Programming An Introduction to Optimization Spring, 2014 WeiTa Chu 1 Brief History of Linear Programming The goal of linear programming is to determine the values of
More informationMeasures to increase accuracy and precision of softwarebased noise prediction
Measures to increase accuracy and precision of softwarebased noise prediction Dr. Wolfgang Probst, DataKustik GmbH Gewerbering 5, 86926 Greifenberg, Germany ABSTRACT Following some similar national activities
More informationHYBRID GENETIC ALGORITHMS FOR SCHEDULING ADVERTISEMENTS ON A WEB PAGE
HYBRID GENETIC ALGORITHMS FOR SCHEDULING ADVERTISEMENTS ON A WEB PAGE Subodha Kumar University of Washington subodha@u.washington.edu Varghese S. Jacob University of Texas at Dallas vjacob@utdallas.edu
More informationAdaptive information source selection during hypothesis testing
Adaptive information source selection during hypothesis testing Andrew T. Hendrickson (drew.hendrickson@adelaide.edu.au) Amy F. Perfors (amy.perfors@adelaide.edu.au) Daniel J. Navarro (daniel.navarro@adelaide.edu.au)
More informationREVISED GCSE Scheme of Work Mathematics Higher Unit 6. For First Teaching September 2010 For First Examination Summer 2011 This Unit Summer 2012
REVISED GCSE Scheme of Work Mathematics Higher Unit 6 For First Teaching September 2010 For First Examination Summer 2011 This Unit Summer 2012 Version 1: 28 April 10 Version 1: 28 April 10 Unit T6 Unit
More informationLayout Based Visualization Techniques for Multi Dimensional Data
Layout Based Visualization Techniques for Multi Dimensional Data Wim de Leeuw Robert van Liere Center for Mathematics and Computer Science, CWI Amsterdam, the Netherlands wimc,robertl @cwi.nl October 27,
More informationApplying the Chronographical Approach to the Modelling of Multistorey Building Projects
Applying the Chronographical Approach to the Modelling of Multistorey Building Projects A. Francis, E. T. Miresco Department of Construction Engineering, École de technologie supérieure, University of
More informationone Introduction chapter OVERVIEW CHAPTER
one Introduction CHAPTER chapter OVERVIEW 1.1 Introduction to Decision Support Systems 1.2 Defining a Decision Support System 1.3 Decision Support Systems Applications 1.4 Textbook Overview 1.5 Summary
More informationBASIC ELECTRONICS PROF. T.S. NATARAJAN DEPT OF PHYSICS IIT MADRAS LECTURE4 SOME USEFUL LAWS IN BASIC ELECTRONICS
BASIC ELECTRONICS PROF. T.S. NATARAJAN DEPT OF PHYSICS IIT MADRAS LECTURE4 SOME USEFUL LAWS IN BASIC ELECTRONICS Hello everybody! In a series of lecture on basic electronics, learning by doing, we now
More informationA Comparison of System Dynamics (SD) and Discrete Event Simulation (DES) Al Sweetser Overview.
A Comparison of System Dynamics (SD) and Discrete Event Simulation (DES) Al Sweetser Andersen Consultng 1600 K Street, N.W., Washington, DC 200062873 (202) 8628080 (voice), (202) 7854689 (fax) albert.sweetser@ac.com
More informationIntroduction to Engineering System Dynamics
CHAPTER 0 Introduction to Engineering System Dynamics 0.1 INTRODUCTION The objective of an engineering analysis of a dynamic system is prediction of its behaviour or performance. Real dynamic systems are
More informationEL5223. Basic Concepts of Robot Sensors, Actuators, Localization, Navigation, and1 Mappin / 12
Basic Concepts of Robot Sensors, Actuators, Localization, Navigation, and Mapping Basic Concepts of Robot Sensors, Actuators, Localization, Navigation, and1 Mappin / 12 Sensors and Actuators Robotic systems
More informationSimulation of the fiber spinning process
T. Götz, H. Rave, D. ReinelBitzer, K. Steiner, H. Tiemeier Simulation of the fiber spinning process Berichte des Fraunhofer ITWM, Nr. 26 (2001) FraunhoferInstitut für Techno und Wirtschaftsmathematik
More informationCertificate SAP INTEGRATION CERTIFICATION
Certificate SAP INTEGRATION CERTIFICATION SAP SE hereby confirms that the interface software MCC SR2015 for the product MCC SR2015 of the MEIERHOFER AG. has been certified for integration with SAP ECC
More informationSTATISTICA. Clustering Techniques. Case Study: Defining Clusters of Shopping Center Patrons. and
Clustering Techniques and STATISTICA Case Study: Defining Clusters of Shopping Center Patrons STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Webbased Analytics Table
More informationInterpreting Data in Normal Distributions
Interpreting Data in Normal Distributions This curve is kind of a big deal. It shows the distribution of a set of test scores, the results of rolling a die a million times, the heights of people on Earth,
More informationPrentice Hall Connected Mathematics 2, 7th Grade Units 2009
Prentice Hall Connected Mathematics 2, 7th Grade Units 2009 Grade 7 C O R R E L A T E D T O from March 2009 Grade 7 Problem Solving Build new mathematical knowledge through problem solving. Solve problems
More informationBig Data: Rethinking Text Visualization
Big Data: Rethinking Text Visualization Dr. Anton Heijs anton.heijs@treparel.com Treparel April 8, 2013 Abstract In this white paper we discuss text visualization approaches and how these are important
More informationManual for simulation of EB processing. Software ModeRTL
1 Manual for simulation of EB processing Software ModeRTL How to get results. Software ModeRTL. Software ModeRTL consists of five thematic modules and service blocks. (See Fig.1). Analytic module is intended
More informationA Short Introduction to Computer Graphics
A Short Introduction to Computer Graphics Frédo Durand MIT Laboratory for Computer Science 1 Introduction Chapter I: Basics Although computer graphics is a vast field that encompasses almost any graphical
More informationHolistic Development of Knowledge Management with KMMM
1 Karsten Ehms, Dr. Manfred Langen Holistic Development of Knowledge Management with KMMM Siemens AG / Corporate Technology Knowledge Management & Business Transformation If knowledge management is to
More informationPSS 27.2 The Electric Field of a Continuous Distribution of Charge
Chapter 27 Solutions PSS 27.2 The Electric Field of a Continuous Distribution of Charge Description: Knight ProblemSolving Strategy 27.2 The Electric Field of a Continuous Distribution of Charge is illustrated.
More informationUsing Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data
Using Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data Introduction In several upcoming labs, a primary goal will be to determine the mathematical relationship between two variable
More information1. Classification problems
Neural and Evolutionary Computing. Lab 1: Classification problems Machine Learning test data repository Weka data mining platform Introduction Scilab 1. Classification problems The main aim of a classification
More informationAlgebra 1 2008. Academic Content Standards Grade Eight and Grade Nine Ohio. Grade Eight. Number, Number Sense and Operations Standard
Academic Content Standards Grade Eight and Grade Nine Ohio Algebra 1 2008 Grade Eight STANDARDS Number, Number Sense and Operations Standard Number and Number Systems 1. Use scientific notation to express
More informationSound Power Measurement
Sound Power Measurement A sound source will radiate different sound powers in different environments, especially at low frequencies when the wavelength is comparable to the size of the room 1. Fortunately
More informationMathematics on the Soccer Field
Mathematics on the Soccer Field Katie Purdy Abstract: This paper takes the everyday activity of soccer and uncovers the mathematics that can be used to help optimize goal scoring. The four situations that
More informationCHAPTER 1 INTRODUCTION
1 CHAPTER 1 INTRODUCTION Exploration is a process of discovery. In the database exploration process, an analyst executes a sequence of transformations over a collection of data structures to discover useful
More informationCARTOGRAPHIC INFORMATION ARCHITECTURE  DESIGNING ONLINE PRESENTATION OF CARTOGRAPHIC INFORMATION TO FACILITATE USER UNDERSTANDING
CARTOGRAPHIC INFORMATION ARCHITECTURE  DESIGNING ONLINE PRESENTATION OF CARTOGRAPHIC INFORMATION TO FACILITATE USER UNDERSTANDING Alexander Pucher University Assistant, Department of Geography and Regional
More informationAn Overview of the Finite Element Analysis
CHAPTER 1 An Overview of the Finite Element Analysis 1.1 Introduction Finite element analysis (FEA) involves solution of engineering problems using computers. Engineering structures that have complex geometry
More informationInteractive Voting System. www.ivsystem.nl. IVSBasic IVSProfessional 4.4
Interactive Voting System www.ivsystem.nl IVSBasic IVSProfessional 4.4 Manual IVSBasic 4.4 IVSProfessional 4.4 1213 Interactive Voting System The Interactive Voting System (IVS ) is an interactive
More informationWarning! Construction Zone: Building Solids from Nets
Brief Overview: Warning! Construction Zone: Building Solids from Nets In this unit the students will be examining and defining attributes of solids and their nets. The students will be expected to have
More informationGo to contents 18 3D Visualization of Building Services in Virtual Environment
3D Visualization of Building Services in Virtual Environment GRÖHN, Matti Gröhn; MANTERE, Markku; SAVIOJA, Lauri; TAKALA, Tapio Telecommunications Software and Multimedia Laboratory Department of Computer
More informationMonitoring of Natural Hazards With the ImpactSentinel Alarming System An Intelligent Solution
Monitoring of Natural Hazards With the ImpactSentinel Alarming System An Intelligent Solution ImpactSentinel Natural Hazard Sensors und Systems hazard signalization in protection fences overstress, shock
More informationFast and Easy Delivery of Data Mining Insights to Reporting Systems
Fast and Easy Delivery of Data Mining Insights to Reporting Systems Ruben Pulido, Christoph Sieb rpulido@de.ibm.com, christoph.sieb@de.ibm.com Abstract: During the last decade data mining and predictive
More informationData, Measurements, Features
Data, Measurements, Features Middle East Technical University Dep. of Computer Engineering 2009 compiled by V. Atalay What do you think of when someone says Data? We might abstract the idea that data are
More informationPhysical to Functional Mapping with Mindmap Software
2006013493 Physical to Functional Mapping with Mindmap Software Copyright 2006 SAE International Michael R Sevcovic International Truck and Engine Corporation ABSTRACT This paper describes how mind mapping
More informationCONNECTING LESSONS NGSS STANDARD
CONNECTING LESSONS TO NGSS STANDARDS 1 This chart provides an overview of the NGSS Standards that can be met by, or extended to meet, specific STEAM Student Set challenges. Information on how to fulfill
More informationSustainable construction criteria to evaluate the thermal indoor climate  further development of the BNB assessment tool and practical implementation
Fraunhofer Institute for Building Physics IBP Directors Univ. Prof. Dr. Ing. Gerd Hauser Univ. Prof. Dr. Ing. Klaus Sedlbauer Holzkirchen Branch Fraunhoferstrasse 10 83626 Valley / Germany IBPAbstract
More information15.062 Data Mining: Algorithms and Applications Matrix Math Review
.6 Data Mining: Algorithms and Applications Matrix Math Review The purpose of this document is to give a brief review of selected linear algebra concepts that will be useful for the course and to develop
More informationJava Modules for Time Series Analysis
Java Modules for Time Series Analysis Agenda Clustering Nonnormal distributions Multifactor modeling Implied ratings Time series prediction 1. Clustering + Cluster 1 Synthetic Clustering + Time series
More informationAppendix A. Comparison. Number Concepts and Operations. Math knowledge learned not matched by chess
Appendix A Comparison Number Concepts and Operations s s K to 1 s 2 to 3 Recognize, describe, and use numbers from 0 to 100 in a variety of familiar settings. Demonstrate and use a variety of methods to
More informationUnit 7 Quadratic Relations of the Form y = ax 2 + bx + c
Unit 7 Quadratic Relations of the Form y = ax 2 + bx + c Lesson Outline BIG PICTURE Students will: manipulate algebraic expressions, as needed to understand quadratic relations; identify characteristics
More informationIFS8000 V2.0 INFORMATION FUSION SYSTEM
IFS8000 V2.0 INFORMATION FUSION SYSTEM IFS8000 V2.0 Overview IFS8000 v2.0 is a flexible, scalable and modular IT system to support the processes of aggregation of information from intercepts to intelligence
More informationA FUZZY LOGIC APPROACH FOR SALES FORECASTING
A FUZZY LOGIC APPROACH FOR SALES FORECASTING ABSTRACT Sales forecasting proved to be very important in marketing where managers need to learn from historical data. Many methods have become available for
More informationA HYBRID APPROACH FOR AUTOMATED AREA AGGREGATION
A HYBRID APPROACH FOR AUTOMATED AREA AGGREGATION Zeshen Wang ESRI 380 NewYork Street Redlands CA 92373 Zwang@esri.com ABSTRACT Automated area aggregation, which is widely needed for mapping both natural
More informationTips and Tricks SAGE ACCPAC INTELLIGENCE
Tips and Tricks SAGE ACCPAC INTELLIGENCE 1 Table of Contents Auto emailing reports... 4 Automatically Running Macros... 7 Creating new Macros from Excel... 8 Compact Metadata Functionality... 9 Copying,
More informationFace Recognition using Principle Component Analysis
Face Recognition using Principle Component Analysis Kyungnam Kim Department of Computer Science University of Maryland, College Park MD 20742, USA Summary This is the summary of the basic idea about PCA
More information