RUNNING AND DEBUGGING FUZZY EXPERT SYSTEMS I: PARALLEL PROGRAMS in .NET

Produce qr-codes in .NET RUNNING AND DEBUGGING FUZZY EXPERT SYSTEMS I: PARALLEL PROGRAMS

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Once the above steps are complete, planners set an optimal dispatch schedule. This schedule is referred to as the least-cost dispatch or an economic dispatch. The leastcost dispatch is a scheduling of unit outputs that services load and satis es ancillary
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FIGURE 5.30 Equity.
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Kathy is also increasingly disturbed that, directly and explicitly, she has become an agent for the government. She doesn t think this is necessarily a bad thing but, because she is losing control over what she does, she thinks she has cause for concern. Kathy has outlined her apprehension to the District Health Education Advisor, who referred her to the District Medical Of cer, who told her kindly that she must do as she is told by her superiors. This means, in practice, that Kathy is obliged by her employer to ensure that the posters are placed in libraries and other public places, and that Kathy faces a very tricky decision over the content of her lectures to groups of teenagers. She is supposed to follow the of cial line but knows (she is only 24) that this strategy will be ineffective even counterproductive with such groups, many of whom will have tried, and if not will almost certainly know people who have tried, illegal drugs and not experienced the tales of horror she has been told to frighten them with. She also has to deal with a similar problem which she sees ultimately as a question of personal integrity as she decides on the content of a talk to a group of parents, many of whom will expect to have the government hyperbole con rmed.
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Such a comprehensive system, designed to capture data about hundreds if not thousands of types of crimes and the circumstances under which they take place is no small undertaking, even after 70 years of development and evolution. Chilton, Major, and Propheter, in a paper presented at the 1998 annual meeting of the American Society of Criminologists in Washington, D.C., attempt to address this complexity when they note this is an effort to fill an information gap produced by the conversion from the UCR summary statistics system to an incident-based UCR system. . . . We examine some of the difficulties related to the conversion to the new approach, including counting rules, table titles, and the possibilities for confusion among recipients of the information. 9 They go on to recount some of the factors that make the seemingly straightforward task of collecting crime data a larger task than one might imagine:
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10: Grid Networks and Layer 3 Services
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agent where to go to look for pages where it will nd meta tags that it can read. To do this, we must de ne a strategy. Again, the parameters for this are human-set. One possibility would simply be to tell it to go to Altavista and search for whatever the user had put in under ,model. on the agent s on-line form, (in this case, CarverKing ). Altavista will return the URLs of a number of hits. The agent is now programmed to visit each one of these hits in turn and look for evidence of the use of ,manufacturer., ,model., etc. meta tags and, possibly, reference to the DTD. This evidence will tell the agent that it is indeed looking at a page where goods are described in a language and format it can understand. It can therefore reliably extract information from the pages. Now it has to make the purchasing recommendation. Its simple goal is to get the best deal. But how does it de ne best Actually, human intervention is again required. There has to have been a de nition of best supplied to the agent, by the person wanting to buy the goods. The agent is only human , in this regard at least! Best could be de ned as cheapest , in which only the ,price. information would be used. Perhaps a more complex algorithm is preferred: a balance between price and delivery might be de ned by the user. (A weighted sum of the price and delivery date might be calculated, perhaps also with overall limits on either.) This algorithm would be programmed in as a sub-strategy. What we want to make clear in all of this, is the non-magical nature of the process. Firstly, there is no way that the agents can second-guess the user s purchasing goals, even though, as we shall see, they can learn from experience. Secondly, the agent s choice will only be as good as the strat egy allows, and most strategies are relatively naIve. None can be guaranteed to nd the best buy , even given a clear de nition of the goal. Obviously though, agents can have more intelligent strategies than that of the example described above. This did have the advantage that, using the XML speci cations, it was unlikely to get its facts wrong. But, it might well have missed out on better offers that were not programmed in XML. Much agent research is involved in building agents that can make good attempts at reading free text, rather than that which has been deliberately meta tagged according to a set plan. This will produce more answers and, possibly, better bargains, but at the expense of reliability. The issue is similar to the knowledge management discussions in Part 2, Managing eBusiness Knowledge.
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monitoring application down to the sensor nodes. Temporarily posed sensor drivers convert service parameters and the XQuery predicate to a bitwise coded format. The coded query is sent to sensors in the payload eld of the set-up packet. In this model, sensor nodes are assumed to be very simple equipment with limited processing and battery power. The set-up packet initiates a tree topology network in the area of interest among the sensors that have accepted the query. DADMA: Data Aggregation and Dilution by Modulus Addressing In DADMA, a sensor network is considered as a distributed relational database composed of a single view that joins virtual local tables named Virtual Local Sensor Node Tables (VLSNT) located at sensor nodes. Figure 4.14 shows the distributed database perception of DADMA. Records in VLSNT are measurements taken upon a query arrival and consist of two elds: task and amplitude. Since a sensor node may have more than one sensor attached to it, the task eld indicates the sensor, e.g. temperature sensor, humidity sensor, etc. that takes the measurement. Since sensor nodes have limited memory capacities, they do not store the results of measurements. Therefore, there can be a single reading for each sensor attached to a node, and the task eld is the key eld in the VLSNT created upon a query arrival. Our perception of WSNs makes relational algebra practical to retrieve the sensed data without much memory requirement, which is different from the scheme explained in [70], where the sensed data for each task are maintained at a different column in a table. Sensor Network Database View (SNDV) can be created temporarily either at the sink, i.e. the node that collects the data from the sensor network, or at an external proxy server. An SNDV record has three elds: task, location and amplitude. While data is being retrieved from a sensor node, the location of the sensor node is also added to the sensed data. Since multiple sensor nodes may have the same type of sensors, i.e. multiple sensors can carry out the same sensing task, task and location become the key elds in an SNDV. In applications where nodes are not location-aware, it is also possible to replace the location eld with the local identi cations of the reporting nodes. The location eld can also be used to identify a group of nodes according to the aggregate and dilute functions explained below. It should be noted that SNDV is a temporary view where the results of a query are collected. For many WSN applications, the sensed data need to be associated with the location data. For example, in target tracking and intrusion detection WSNs, sensed data are almost meaningless without relating them to a location. Therefore, location-awareness of sensor nodes is a requirement imposed by many WSN applications. There are a number of practical location nding techniques for WSNs as reported in [8]. Since each query results in a new SNDV, to keep the aggregated/diluted history of a WSN it may be needed to maintain a permanent External Sensor Network Database Table (ESNDT) in a remote proxy server. In ESNDT, the records obtained from
2.3 GENERAL ALGORITHMS FOR ISOMORPH-FREE EXHAUSTIVE GENERATION In this section, we present general algorithms for generating exactly one representative of each isomorphism class of any kind of combinatorial objects. The reader is referred to the works by Brinkmann [6] and McKay [46] for more information on this type of methods and to the survey by Faulon et al. [26] for a treatment of these methods in the context of enumerating molecules. The algorithms in this section generate combinatorial objects of size n + 1 from objects of size n via backtracking, using a recursive procedure that should be rst called with parameters of an empty object, namely X = [ ] and n = 0. They are presented in a very general form that can be tailored to the problem at hand. In particular, procedures IsComplete(X) and IsExtendible(X) can be set to ensure that all objects of size up to n or exactly n are generated, depending on the application. In addition, properties of the particular problem can be used in order to employ further prunings, which cannot be speci ed in such a general framework but which are of crucial importance. The basic algorithms we consider here (Algorithms BasicGenA and BasicGenB) exhaustively generate all objects using backtracking and only keep one representative from each isomorphism class. They both require a method for checking whether the current object generated is the one to be kept in its isomorphism class. In Algorithm BasicGenA, this is done by remembering previously generated objects, which are always checked for isomorphism against the current object. Algorithm BasicGenA (X = [x1 , x2 , . . . , xn ], n) redundancyFound = false if (IsComplete(X)) then if (for all Y GenList: AreIsomorphic(X, Y )) then GenList = GenList {X} process X else redundancyFound = true if (( redundancyFound) and (IsExtendible(X))) then for all extensions of X: X = [x1 , x2 , . . . , xn , x ] if (IsFeasible(X )) then BasicGenA(X , n + 1) The third line of Algorithm BasicGenA is quite expensive in terms of time, since an isomorphism test AreIsomorphic(X, Y ) between X and each element Y in GenList must be computed; see the works by Kocay [43] and McKay [44] for more information on isomorphism testing and by McKay [45] for an ef cient software package for graph isomorphism. In addition, memory requirements for this algorithm become a serious issue as all the previously generated objects must be kept. In Algorithm BasicGenB, deciding whether the current object is kept is done by a rule specifying who is the canonical representative of each isomorphism class. Based on this rule, the current object is only kept if it is canonical within its isomorphism class. A commonly used rule is that the canonical object be the lexicographically
objective. In the IST Secure project, which aims at trust management in uncertain environments (such as the cooperation between newly discovered PDAs), a general trust model based on the notion of reputation is proposed. Each possible action involved in the cooperation is associated with a set of possible outcomes. Each node maintains an evidence store to log the interactions with other nodes and interesting events. Based on the history of interactions and observed outcomes, nodes are able to dynamically build a notion of reputation. Essentially, positive outcomes lead to a good reputation. If an action is required in an interaction, a bene t/risk analysis is performed and a decision is taken based on the active policy for the node. For example, if the risk is beyond a given threshold or if the bene t is below a given value, the action may be rejected. Similar mechanisms are investigated in a French study called Mosaic, which aims at providing system support for collaborative backup of vulnerable personal devices (PDA, phones, digital camera, etc.). Each device uses other devices to save part of their data, and provides spare space to backup other devices. Fair use of the resources (space and energy) is considered critical for the success of this kind of application, and market-like regulation is a promising approach to ensure this goal. The idea is to associate a currency to the resources and account for their use when a device provides backup to another. Backing up data gives credits to a device, while saving data to another device uses credits. 5.3.2.4. Virtual machines The concept of virtual machines has been well-known since the early 1960s and is used to indicate a piece of computer software able to shield applications from the details of an underlying hardware or software platform. A virtual machine offers to applications a suite of virtual instructions and maps them to the real instruction set actually provided by the underlying real machine. In this way, the virtual machine abstraction can mask differences in the hardware and software lying below the virtual machine itself, thus facilitating code and data mobility. In a system of complex COs, the interchange of data and other information may be very cumbersome since single devices may show a broad range of different internal architectures and protocols. This problem may be overcome by providing each different device with an adequate version of the virtual machine, which will hide the single device s peculiarities and provide a unique virtual hardware and software setting to applications. In this way, applications can easily be written to run on the virtual machine itself instead of having to create separate application versions for each different platform. The virtual machine approach has already been deeply investigated for different applications ([21, 24, 25, 47]), but only a few approaches have been proposed and designed explicitly for COs. A tiny virtual machine speci cally designed for sensor networks is Mat , developed at the Intel Research Laboratory at Berkeley, in conjunction with UC Berkeley. For further implementation details the reader may refer to section 5.4.1.3.
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