Open World Target Identification Using the Transferable Belief Model


 Maud Alyson Carr
 2 years ago
 Views:
Transcription
1 Open World Target Identification Using the Transferable Belief Model Matthew Roberts EADS Innovation Works Newport, UK Gavin Powell EADS Innovation Works Newport, UK Dafni Stampouli EADS Innovation Works Newport, UK Abstract The availability of datasets for the comparison of fusion techniques across the board is a key ingredient to the fusion community. Presented in this paper is one such dataset along with a selection of fusion methods applied and evaluated using a set number of prescribed metrics. The dataset is the output of a tracking and classification algorithm that describes a land based vehicle as it traverses across some terrain. This data can be fused over time to obtain a more robust target classification. Evidence theory combination rules are applied to the dataset and an extension to a current combination rule is presented and evaluated. I. INTRODUCTION The Evaluating Techniques for Uncertainty Reasoning Working Group (ETURWG) is an ISIF working group formed to allow a common test bed to be created where fusion algorithms can be tested and evaluated so that a common point of reference can be used for comparison with competing algorithms. As such there is a need for data, scenarios and metrics to be defined and made available. In this paper we describe that dataset and the three scenarios that are encompassed within it. For means of comparison a set of metrics are described and applied to the fusion algorithms that have been used to process that dataset for target classification. Furthermore we propose an extension to the GRP [] combination rule to allow past classifications to have a greater impact on possible future states. This is carried out by eliminating targets from the potential set if it has been shown that the current object is manoeuvring in a manner that is outside of the capable range for a given potential target type. The dataset is based on an open world scenario. The open versus closed world is an important point and has relevance to the fusion rules used and their application. In a closed world we are making the assumption that we uncategorically know every possible outcome, or rather the true state must be within our list of possible outcomes. In some circumstances this is true but often in reality we operate in an open world. In an open world we have a list of possible outcomes but accept that this is not completely exhaustive and that there are some possible states that we do not have in our list, or are even unaware of. The open world is generally more difficult to deal with but as it has more applicability in the real world it has been chosen for this dataset. Section II explains the scenarios and metrics used in this paper. Relevant background material relating to evidential theory is briefly presented in Section III. Section IV describes our proposed solution to the use case. An improvement to the GRP combination rule is given in Section V. Our results are shown in Section VI. Section VII contains our conclusions. II. SCENARIOS The scenarios used in this paper are from a use case for the Evaluating Techniques for Uncertainty Reasoning Working Group (ETURWG). The use case is based on previous work for performing joint tracking and classification with a Wireless Sensor Network (WSN) and the Transferable Belief Model (TBM) [2], [3]. The purpose of the use case is to classify the target for each of the three scenarios. Each scenario consists of a single target moving through an area of interest whilst being monitored by a WSN. Each of the nodes have rangeonly sensors. In the original work, a sensor selection algorithm was used to determine which sensors should be active at each time step, and a particle filter was used to estimate the position and velocity of the target at each step. For the use case, the output of the particle filter is available, and as such, sensor selection and target tracking does not need to be performed. It should be noted that the estimate of the target velocity in all three scenarios is very noisy. There are 5 known target classes: amphibious light tank (); pedestrian; car; light tank (), and bicycle. We have an open world assumption, and as such the simulations also contain an unknown target class a main battle tank (MBT); this is the ground truth target class for Scenario C. The simulated target trajectories used this paper include nonlinear motion such as slowing down at bends and junctions whilst travelling along a road. Terrain is assumed to hinder target classes by varying amounts; this is modelled by assigning each terrain type into a terrain class for a given target class. Three terrain classes are used Go, Slow Go, and No Go. Table I shows these assignments; the letters G, S, and N indicate Go, Slow Go, and No Go, respectively. For example, a pedestrian travelling on the terrain types Road and Grass is considered unhindered and can therefore travel up to and including it s Go speed its maximum possible speed. The maximum speed for each target class and terrain type combination is shown in Table II. This has previously been presented in [2], [3].
2 TABLE I TERRAIN CLASS ASSIGNMENTS. Road Grass Water Buildings Marsh Trees Steep Land Target Class estrian G G N N S G S G G S N S S N G G N N N S N G S N N N S N MBT G G N N N S N G S N N N S N Buildings Start Trees Finish Steep land Road Grass Water TABLE II TARGET CLASSES AND THEIR ASSUMED MAXIMUM SPEEDS FOR EACH TERRAIN CLASS. Maximum Speed (ms  ) Target Class Go Slow Go No Go estrian MBT Fig. 2. Target trajectory and terrain for Scenario B. Annotations have been added to the terrain map. The red line is the target trajectory. As shown in [2]. Steep land Start Steep land Trees Buildings Start Finish Buildings Finish Water Grass Road Marsh Road Fig. 3. Target trajectory and terrain for Scenario C. Annotations have been added to the terrain map. The red line is the target trajectory. As shown in [2]. A. Metrics Fig.. Target trajectory and terrain for Scenario A. Annotations have been added to the terrain map. The red line is the target trajectory. As shown in [2], [3]. Scenario A consists of an travelling along a road, over grass, crossing a section of water, and then travelling over grass again. The terrain map for this scenario can be seen in Figure. The is the only target class capable of travelling over water. In Scenario B, a car travels along a road which includes crossing a roundabout (see Figure 2). The car slows down whilst crossing the roundabout and accelerates away from it. Scenario C consists of a MBT that travels along a road and then goes off road (see Figure 3). Due to the known target classes, this unknown class may appear to be a tank whilst travelling along a road. Two metrics are used to compare the performance of the fusion algorithms, which in this case are different combination rules classification accuracy and decision accuracy. Section VI discusses how these metrics are calculated when the ground truth target class is an unknown target class (i.e. Scenario C). Classification accuracy is the mean classification probability for the ground truth target class across the length of the simulation. In certain circumstances, this metric may favour closed world scenarios as the normalisation at each time step can inflate the classification probabilities. Decision accuracy is calculated as the proportion of the total simulation time where the classification probability of the ground truth is more than any other class. This metric is designed to be less susceptible to the inflation that can occur with normalisation. This metric is used with the assumption that the target class is decided purely on the classification probability.
3 III. BACKGROUND Evidence theory was born some years ago through the initial work of Arthur Dempster and Glenn Shafer [4], [5]. There are various forms based upon that original methodology that exist to date. It is this field of techniques that are used to determine the target in the 3 scenarios of the ETURWG use case, and of which we choose to compare and contrast using a prescribed set of metrics. The methods that we apply to the data set fall into various subsections of evidence theory and those being DempsterShafer Theory (DST), DezertSmarandache Theory (DSmT) [6] and the TBM [7]. The underpinnings of these are well described in their respective literature and as such we suggest the reader follow those references if unfamiliar with any of the approaches. This example utilises the TBM as a means of applying the various combination rules. The TBM relies on working in the space where you have a set that contains all possible outcomes that are to be considered, and also an empty set, which denotes anything that is not included in that set. A basic belief assignment (bba) is created at each time step, and this is where mass, m, is placed on each of the possible subsets of the world and denotes the belief we have that the real true state lies somewhere within that set as shown by: A. Combination rules m : 2 Ω [, ] with A Ω m(a) =. () At the core of any evidential theory technique for the fusion of 2 or more pieces of information is the combination rule. This allows information to be combined in a manner that will enable the system to make a robust and informed decision about the world that is being sensed. The combination rules vary in complexity, usability, and applicability to the problem space. These rules are generally well known and their strengths and weaknesses well documented. We do propose an extension to previous work in defining combination rules and show the new rule s comparative results when applied to the data set. ) Conjunctive combination: The conjunctive rule of combination is generally accepted to be the original rule for combination used within DST. It is capable of working in both the open and closed worlds and its simplicity ensures it is often used to conquer problems. It tends to work best in a closed world, issues relating to this have previously being discussed by the authors [8], [9]. When utilised in an open world this rule will tend toward empty set domination and an unresponsive system. The unnormalised open world conjunctive rule of combination is given by: m 2 (X) = m (A) m 2 (B), (2) A,B 2 Ω A B = X where m 2 is decisive intersection of the two pieces of evidence given by m and m 2. The normalised closed world is given by: m DS (X) = k 2 A,B 2 Ω A B = X where k 2 is the normalisation factor: k 2 = A,B 2 Ω A B = m (A) m 2 (B), (3) m (A) m 2 (B). (4) 2) Disjunctive combination: The disjunctive rule of combination [] is the counterpart to the conjunctive rule of combination and provides a cautious combination of information, as opposed to the decisiveness of the conjunctive. This rule will tend toward vagueness and the placement of mass on the ignorant set as more combinations are performed. We only apply this rule using an open world assumption within this paper. The disjunctive rule is given by: m 2 (X) = A,B 2 Ω A B = X m (A) m 2 (B). (5) where m 2 is the cautious union between the two pieces of evidence given by m and m 2. 3) PCR5: PCR5 [] is a combination rule that uses a more intelligent means of redistributing the conflict when used in a closed world. To give this a means of comparison to the other rules here we have used it in an open world manner. This rule was intended for use within the DSmT framework where the open world is treated in a slightly different manner. The PCR5 rule that we have utilised for this comparison is given by: m P CR5 (X) = m 2 (X) + [ Y 2 Ω \{X} X Y = m (X) 2 m 2 (Y ) m (X) + m 2 (Y ) + m 2(X) 2 m (Y ) m 2 (X) + m (Y ) ], (6) This is generally seen as one of the more impressive combination rules that can be applied to evidence theory. 4) RCRS: RCRS [2] is a robust combination rule with symmetric coefficients. This rule is a weighted combination of both the conjunctive and disjunctive combination rules. The weighting factor is given by: α (k) = k k + k 2 and β (k) = k k + k 2 (7) and are applied to the robust combination rule by: m RCR S (A) = k k + k 2 m (A)+ k k + k 2 m (A) (8) and m RCR S ( ) =. RCRS is the closest neighbour to the GRP rule in terms of its underlying methodology.
4 5) GRP: GRP [] was created to combat the shortcomings of some of the other combination rules that are in circulation. This rule was required to be able to effectively combine information in a temporal manner, where measurements may be received for an infinite amount of time, while still retaining stability, flexibility and an element of memory of the past to effect the way in which current information is combined. This is specifically designed for use where some estimate of state is given and constantly updated with new information. GRP is the arithmetic mean of the conjunctive and disjunctive combination rules, after they have been discounted by some factor. The discounting takes place using: { m α α m(a) if A Ω; (A) = (9) α m(a) + ( α) if A = Ω, to discount the conjunctive rule, and: { m α α m(a) if A Ω, A ; (A) = α m(a) + ( α) if A =, () to discount the disjunctive rule. The amount that we discount by is governed by the precision (lack of vagueness) that the current state/system has. There are various precision metrics [], but the one used within this paper is given by: p (m) = Ω A Ω m(a) A, A 2Ω () The GRP combination rule is: m GRP (A) = m fd(a) + m fc (A), (2) 2 m fd (A) = m f md (A), (3) m fc (A) = m f mc (A), (4) where A 2 Ω, m md = m α m (using Equation ), m mc = m α m (using Equation 9), both discounting equations use α = p (m f ), m f is the bba that represents the existing information, and m m is the bba that represents the incoming information. IV. SOLUTION The solution for the ETURWG use case that is presented here is based on previous work for performing tracking and classification with a WSN and the TBM. The frame of discernment is the set of known target classes. Each simulation consists of a number of discrete time steps. At each of these steps a bba is created and is fused with the bba that represents the existing knowledge the pignistic transform is then used to provide a classification probability for each target class. The conditional bba created at each time step, m C, is created calculated using the Generalised Bayesian Theory (GBT) [3]: m C (A x) = pl(x c i ) [ pl(x c i )], (5) c i A c i Ā where pl(x c i ) is the plausibility of target class c i given the speed of the target, x, and A 2 Ω. The plausibility of a target for a given speed and given terrain class can be calculated using: ( pl(x) = (x x) Betf(x) + dā ) Betf(a) da, x da (6) where Betf is a bell shaped pignistic density function, Betf(x) = Betf( x), and x v x; v is the mode of Betf. x is taken as the mean of the W N most recent speed estimates from the particle filter. The Betf for each target and terrain class combination can be found in Section VI. Equation 6 is calculated for each of the three terrain classes and combined using a weighted sum; the weightings are calculated from the coverage of an ellipse whose size and shape are defined the number of standard deviations, N σ, and the covariance of the current particle filter position estimate weighted covariance, respectively. The centre of the ellipse is positioned at the weighted mean of the particle filter position estimate. For a more detailed explanation of this please see [2], [3]. If a closed world assumption is used, m C is then normalised to remove the mass assigned to the empty set. If this is not possible, i.e. m C ( ) =, then an assignment of m C (Ω) = is used. Once m C has been calculated for the current time step, it is combined with the existing bba that represents the existing knowledge built up over all of the previous time steps, m k. If this is the first time step than the existing bba is taken as m k (Ω) =. The two bbas are fused using a combination rule resulting in m k. The performance of different combination rules is compared in Section VI. At each step the pignistic transform is used to calculate a classification probability (including the for the empty set) [7]: (B) = A Ω m k (A) A B A A Ω. (7) This is then used to calculate the metrics discussed in Section IIA. V. IMPROVED COMBINATION RULE For the ETURWG use case, we have modified an existing combination rule, GRP. The modification was made to allow GRP to classify a target whilst retaining a memory of target classes that are no longer possible. This prevents mass from being assigned to targets that have been previously been determined as having little or no classification probability. The modification should allow this improved rule to classify a target in an open world scenario especially when a scenario consists of different segments with different target behaviours. The remainder of this section describes the improved combination rule, GRP+. At the start of using the GRP+ combination rule, the conditional bba created in the current time step, m C, is conjunctively combined, using an open world assumption, with m F (F) =, where F is the set of all feasible and known target classes; this transfers all of the belief assigned
5 to targets which have been deemed no longer possible onto the remaining masses: m CF (A) = m C F (A) A 2 Ω. (8) The GRP combination rule is then used, but instead of discounting to the ignorant set, mass is moved to the most ignorant but still feasible set F. To achieve this, Equation 9 is modified to be: { m α α m(a) if A F; (A) = (9) α m(a) + ( α) if A = F, In the notation used in Section IIIA5, m f = m k and m m = m CF. After an initialisation period, I, a target class, A, is deemed to no longer be possible if (A) is smaller than or equal to a threshold, T. The set of feasible target classes is determined using: F = {A Ω : (A) > T }. (2) If at any time F =, then the set of feasible classes is reset to F = Ω. The set of feasible classes is determined at the end of using the GRP+ combination rule for the next time the GRP+ is used. VI. RESUS Simulations were run for each combination of the 3 scenarios and 7 combination rules: GRP; GRP+; PCR5; RCR S; conjunctive combination (closed world assumption); conjunctive combination (open world assumption); and disjunctive combination (open world assumption). At the end of each simulation, the classification accuracy and decision accuracy metrics where calculated. When calculating the metrics, the unknown target was assumed to be classified as the empty set. Therefore, for the purposes of the metrics, the empty set was treated like any other target class. This section For all three scenarios, N σ = 5, W = 7, and T =.. The plausibilities calculated at each time step, using Equation 5, are calculated using the pignistic density functions in Figure 4. The classification accuracy calculated for each combination rule and scenario can be found in Table III; Table IV contains the decision accuracy results. Where Table III and IV state DNF for a particular combination of scenario and combination rule, the simulation failed to finish; this was always due to an attempt to normalise a bba during the fusion step where k =. An extra row has been added to both tables to show an average for the 3 scenarios, this provides an overall view of the performance of each combination rule. It can been seen that GRP+ has the highest scoring overall classification and decision accuracy; this is due to the poor performance of the combination rules that use a closed world assumption when trying to identify the target in Scenario C. Betf(x) Betf(x) Betf(x).5.5 tbmterrain Go Speed (ms ) x (a) 4 2 tbmterrain Slow Go PT76 ZiL 44 T62 Challenger Speed (ms ) x (b) 2 tbmterrain No Go Speed (ms ) x (c) Fig. 4. The assumed Betf for the terrain classes Go (a), Slow Go (b), and No Go (c). Previously shown in [3]. Scenario GRP TABLE III CLASSIFICATION ACCURACY GRP+ Combination rule PCR5 RCRS Conj. (Closed) Conj.(Open) Disj. A DNF.283 B C.2.32 DNF.979 Mean N/A A. Scenario A GRP creates a robust and flexible classification as shown in Figure 5. The issue of this approach is clearly highlighted where its flexibility means that the target is incorrectly classified. More importantly this misclassification is with an object type that should be impossible given the objects previous motion. This is shown after time step 2 where the object is classified as a bicycle even though previously the target was moving at a velocity that is highly unlikely for a bicycle. GRP+ accounts for this by correctly removing the bicycle from the list of possible outcomes, once it understands that the object motion dictates it is not a possible class, as shown
6 Scenario GRP TABLE IV DECISION ACCURACY GRP+ Combination rule PCR5 RCRS Conj. (Closed) Conj. (Open) Disj Scenario A GRP+ A DNF B C DNF.977 Mean N/A Scenario A GRP Fig. 5. The fused bba for each time step of Scenario A when using the GRP combination rule. in Figure 6. It can be clearly seen that at time step 2 there is now a low, but equal, probability of each target class showing ambiguity. This is as expected as the motion of the object is not pertaining toward any particular class. This improved behaviour results in a better classification and decision accuracy for GRP+ compared to GRP. In Table III, it can be seen that PCR5 and RCRS score higher than the other rules. This is because these rules are designed to work with a closed world assumption (within the TBM and the redistribution of mass that takes place inflates the values for when compare to GRP and GRP+. This is a safe behaviour to have in this Scenario, but not in open world scenarios. This effect is reduced for the decision accuracy metric because the absolute value of is not important. The use of conjunctive combination with a closed world assumption fails as it cannot cope with when the conflict k =. The conjunctive combination with an open world assumption scores badly due to the convergence toward the empty set; similarly the scores for the disjunctive rule of combination are poor due to the convergence toward the ignorant set Fig. 6. The fused bba for each time step of Scenario A when using the GRP+ combination rule Scenario B GRP Fig. 7. The fused bba for each time step of Scenario B when using the GRP combination rule. B. Scenario B Similar properties are shown in Scenario B as were displayed in Scenario A. The vehicle is moving quite rapidly initially and heavily suggests that it is a car. Because of this other objects that cannot travel at that speed should be removed from the list of possible outcomes. Figures 7 and 8 depict these attributes of the algorithms well and provide a very different result. The notable different between GRP and GRP+ is between time steps where GRP+ is more decided. Notably GRP+ eventually classifies correctly but the flexibility of GRP means that an incorrect classification ensues. Both metrics show a similar performance for the open world approaches discussed above and the sophisticated closed world approaches of PCR5 and RCRS. This is to be expected as the
7 Scenario B GRP Scenario C GRP Fig. 8. The fused bba for each time step of Scenario B when using the GRP+ combination rule Fig. 9. The fused bba for each time step of Scenario C when using the GRP combination rule. ground truth target is within the set of known target classes. The conjunctive rule of combination with a closed world assumption has achieve a perfect score as it initially believes the target class is a car with % certainty and is unable to change its mind later in the simulation. The conjunctive rule of combination with an open world assumption scores badly due to the convergence discussed in Section VIA. The disjunctive rule of combination scores better in this scenario compared to Scenario A because during the first part of the scenario, where the target travels along the road quickly, the target s behaviour is very different from the other known target classes. C. Scenario C In Scenario C, the object is moving and then slows down considerably to make a turn and traverses across grass. While traversing across grass this should show that it is very unlikely to be a car, as is shown below by the low probability from time step 2 to 25 as shown in Figure 9. After this period, the car becomes the most likely target class even though the last period of information has suggested that it can t be a car. GRP+ overcomes this issue, as shown in Figure, by retaining some memory of what has previously happened. It removes car from the list of possible outcomes once the object travels across the grass in a manner that is highly unlikely for a car. GRP+ provides a correct classification while GRP incorrectly classifies. Unlike Scenarios A and B, this scenario requires an open world assumption in order to perform well; this can been seen in Tables III and IV where the closed world combination rules do not receive a score of more than for both metrics. It should be noted that a better score for both metrics may have been seen if the PCR5 combination rule had been used within the DSmT framework instead of the TBM. The open world conjunctive combination rule appears to perform very well, Scenario C GRP Fig.. The fused bba for each time step of Scenario C when using the GRP+ combination rule. but it is not possible to determine how much of this is due to noise and the overall metrics for this rule are not as good as GRP and GRP+. VII. CONCLUSION We have presented a vehicle identification use case for the Evaluating Techniques for Uncertainty Reasoning Working Group (ETURWG) and proposed a solution to this that uses the TBM. The use case is based on a previous work, a closed world tracking and classification problem, which has been modified to be an open world classification problem. The use case consists of three scenarios. The solution proposed is not limited to a particular combination rule. A new rule, based on GRP, called GRP+ has been proposed; this rule adds a stronger memory element to move mass away from targets
8 that have previously been eliminated from the classification exercise. The results presented in this paper have shown a good performance for the GRP+ combination rule in comparison to other rules. PCR5 and RCRS has performed well in the closed world scenarios, but are unable to perform adequately in the open world scenario (within a TBM implmentation). As is to expected, the conjunctive combination rule with a closed world assumption and the disjunctive rule of combination with an open world assumption do not perform well in the scenarios presented in this paper. Although the results for GRP+ combination rule are better than other rules for the use case presented in this paper, there is still room for improvement. Future work should improve upon this combination rule to perform better when the ground truth target class is not within the set of known classes. ACKNOWLEDGMENT The authors would like to thank David Marshall, diff University and Pete TalbotJones, EADS both of whom helped to develop the previous work on which the ETURWG use case is based. REFERENCES [] G. Powell and M. Roberts, GRP. A recursive fusion operator for the transferable belief model, in Proceedings International Conference on Information Fusion 2, Jul 2. [2] M. Roberts, Tracking and classification with wireless sensor networks and the transferable belief model, Ph.D. dissertation, diff School of Computer Science & Informatics, diff University, 2. [3] M. Roberts, D. Marshall, and G. Powell, Improving joint tracking and classification with the Transferable Belief Model and terrain information, in Proceedings International Conference on Information Fusion 2, Jul 2. [4] A. P. Dempster, A generalization of bayesian inference, Journal of the Royal Statistical Society, vol. 3, no. 2, pp , 968. [5] G. Shafer, A Mathematical Theory of Evidence. Princeton, NJ: Princeton University Press, 976. [6] J. Dezert and F. Smarandache, An introduction to DSmT, CoRR, vol. abs/93.279, 29. [7] P. Smets and R. Kennes, The Transferable Belief Model, Artificial Intelligence, vol. 66, no. 2, pp , 994. [8] G. Powell, M. Roberts, and D. Marshall, Pitfalls for recursive iteration in set based fusion, in Workshop on the Theory of Belief Functions, Apr 2. [9], biasing issues in the Transferable Belief Model for fusing and decision making, in Proceedings International Conference on Information Fusion 2, Jul 2. [] D. Dubois and H. Prade, Representation and combination of uncertainty with belief functions and possibility measures, Computational Intelligence, vol. 4, no. 3, pp , Sept 988. [] F. Smarandache and J. Dezert, Information fusion based on new proportional conflict redistribution rules, in Proceedings International Conference on Information Fusion 25, vol. 2, Jul 25, pp [2] M. Florea, A.L. Jousselme, E. Bosséb, and D. Grenier, Robust combination rules for evidence theory, Information Fusion, vol., no. 2, pp , April 29. [3] B. Ristic and P. Smets, Belief function theory on the continuous space with an application to model based classification, Proceedings of Information Processing and Management of Uncertainty in Knowledge Based Systems, IPMU, pp. 4 9, 24.
Contradiction measures and specificity degrees of basic belief assignments
Contradiction measures and specificity degrees of basic belief assignments Florentin Smarandache Math. & Sciences Dept. University of New Mexico, 200 College Road, Gallup, NM 87301, U.S.A. Email: smarand@unm.edu
More informationCredal classification of uncertain data using belief functions
23 IEEE International Conference on Systems, Man, and Cybernetics Credal classification of uncertain data using belief functions Zhunga Liu a,c,quanpan a, Jean Dezert b, Gregoire Mercier c a School of
More informationClassical Combination Rules Generalized to DSm Hyperpower Sets and their Comparison with the Hybrid DSm Rule
Milan Daniel 1 3 Institute of Computer Science, Academy of Sciences, Prague, Czech Republic Classical Combination Rules Generalized to DSm Hyperpower Sets and their Comparison with the Hybrid DSm Rule
More informationHow to preserve the conflict as an alarm in the combination of belief functions?
How to preserve the conflict as an alarm in the combination of belief functions? Eric Lefèvre a, Zied Elouedi b a Univ. Lille Nord de France, UArtois, EA 3926 LGI2A, France b University of Tunis, Institut
More informationTopic 4. 4.3 DempsterShafer Theory
Topic 4 Representation and Reasoning with Uncertainty Contents 4.0 Representing Uncertainty 4.1 Probabilistic methods 4.2 Certainty Factors (CFs) 4.3 DempsterShafer theory 4.4 Fuzzy Logic DempsterShafer
More informationA GENERALIZATION OF THE CLASSIC COMBINATION RULES TO DSm HYPERPOWER SETS
A GENERALIZATION OF THE CLASSIC COBINATION RULES TO DSm HYPERPOWER SETS ilan DANIEL Abstract: Dempster s rule, Yager s rule and DuboisPrade s rule for belief functions combination are generalized to
More informationMultiultrasonic sensor fusion for autonomous mobile robots
Multiultrasonic sensor fusion for autonomous mobile robots Zou Yi *, Ho Yeong Khing, Chua Chin Seng, and Zhou Xiao Wei School of Electrical and Electronic Engineering Nanyang Technological University
More informationDesign of an Intelligent Housing System Using Sensor Data Fusion Approaches
11 Design of an Intelligent ousing System Using Sensor Data Fusion Approaches Arezou_Moussavi Khalkhali, Behzad_ Moshiri, amid Reza_ Momeni Telecommunication Infrastructure Company of Iran, Ministry of
More informationVEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS
VEHICLE TRACKING USING ACOUSTIC AND VIDEO SENSORS Aswin C Sankaranayanan, Qinfen Zheng, Rama Chellappa University of Maryland College Park, MD  277 {aswch, qinfen, rama}@cfar.umd.edu Volkan Cevher, James
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 informationA Generalization of the minc Combination to DSm Hyperpower Sets
A Generalization of the minc Combination to DSm Hyperpower Sets ilan Daniel Institute of Computer Science Academy of Sciences of the Czech Republic milan.daniel@cs.cas.cz Abstract Formulas for minc combination
More informationPrediction of Stock Performance Using Analytical Techniques
136 JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 5, NO. 2, MAY 2013 Prediction of Stock Performance Using Analytical Techniques Carol Hargreaves Institute of Systems Science National University
More informationLecture 11: Graphical Models for Inference
Lecture 11: Graphical Models for Inference So far we have seen two graphical models that are used for inference  the Bayesian network and the Join tree. These two both represent the same joint probability
More informationCOMPUTING CLOUD MOTION USING A CORRELATION RELAXATION ALGORITHM Improving Estimation by Exploiting Problem Knowledge Q. X. WU
COMPUTING CLOUD MOTION USING A CORRELATION RELAXATION ALGORITHM Improving Estimation by Exploiting Problem Knowledge Q. X. WU Image Processing Group, Landcare Research New Zealand P.O. Box 38491, Wellington
More informationGENERALIZATION OF THE CLASSIC COMBINATION RULES TO DSm HYPERPOWER SETS
GENERALIZATION OF THE CLASSIC COMBINATION RULES TO DSm HYPERPOWER SETS Milan DANIEL Abstract: In this article, the author generalizes Dempster s rule, Yager s rule, and DuboisPrade s rule for belief
More informationComparison of Kmeans and Backpropagation Data Mining Algorithms
Comparison of Kmeans and Backpropagation Data Mining Algorithms Nitu Mathuriya, Dr. Ashish Bansal Abstract Data mining has got more and more mature as a field of basic research in computer science and
More informationSupport Vector Machines for Dynamic Biometric Handwriting Classification
Support Vector Machines for Dynamic Biometric Handwriting Classification Tobias Scheidat, Marcus Leich, Mark Alexander, and Claus Vielhauer Abstract Biometric user authentication is a recent topic in the
More informationSocial Media Mining. Data Mining Essentials
Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers
More informationDeterministic Samplingbased Switching Kalman Filtering for Vehicle Tracking
Proceedings of the IEEE ITSC 2006 2006 IEEE Intelligent Transportation Systems Conference Toronto, Canada, September 1720, 2006 WA4.1 Deterministic Samplingbased Switching Kalman Filtering for Vehicle
More informationApplication of Adaptive Probing for Fault Diagnosis in Computer Networks 1
Application of Adaptive Probing for Fault Diagnosis in Computer Networks 1 Maitreya Natu Dept. of Computer and Information Sciences University of Delaware, Newark, DE, USA, 19716 Email: natu@cis.udel.edu
More informationCollision Probability Forecasting using a Monte Carlo Simulation. Matthew Duncan SpaceNav. Joshua Wysack SpaceNav
Collision Probability Forecasting using a Monte Carlo Simulation Matthew Duncan SpaceNav Joshua Wysack SpaceNav Joseph Frisbee United Space Alliance Space Situational Awareness is defined as the knowledge
More informationA Review of Anomaly Detection Techniques in Network Intrusion Detection System
A Review of Anomaly Detection Techniques in Network Intrusion Detection System Dr.D.V.S.S.Subrahmanyam Professor, Dept. of CSE, Sreyas Institute of Engineering & Technology, Hyderabad, India ABSTRACT:In
More informationMaking Sense of the Mayhem: Machine Learning and March Madness
Making Sense of the Mayhem: Machine Learning and March Madness Alex Tran and Adam Ginzberg Stanford University atran3@stanford.edu ginzberg@stanford.edu I. Introduction III. Model The goal of our research
More informationIntersection Cost Comparison Spreadsheet User Manual ROUNDABOUT GUIDANCE VIRGINIA DEPARTMENT OF TRANSPORTATION
Intersection Cost Comparison Spreadsheet User Manual ROUNDABOUT GUIDANCE VIRGINIA DEPARTMENT OF TRANSPORTATION Version 2.5 i Virginia Department of Transportation Intersection Cost Comparison Spreadsheet
More informationInternational Journal of Electronics and Computer Science Engineering 1449
International Journal of Electronics and Computer Science Engineering 1449 Available Online at www.ijecse.org ISSN 22771956 Neural Networks in Data Mining Priyanka Gaur Department of Information and
More informationMarketing Mix Modelling and Big Data P. M Cain
1) Introduction Marketing Mix Modelling and Big Data P. M Cain Big data is generally defined in terms of the volume and variety of structured and unstructured information. Whereas structured data is stored
More informationA CLASSIFIER FUSIONBASED APPROACH TO IMPROVE BIOLOGICAL THREAT DETECTION. Palaiseau cedex, France; 2 FFI, P.O. Box 25, N2027 Kjeller, Norway.
A CLASSIFIER FUSIONBASED APPROACH TO IMPROVE BIOLOGICAL THREAT DETECTION Frédéric Pichon 1, Florence Aligne 1, Gilles Feugnet 1 and Janet Martha Blatny 2 1 Thales Research & Technology, Campus Polytechnique,
More informationExtraction of Satellite Image using Particle Swarm Optimization
Extraction of Satellite Image using Particle Swarm Optimization Er.Harish Kundra Assistant Professor & Head Rayat Institute of Engineering & IT, Railmajra, Punjab,India. Dr. V.K.Panchal Director, DTRL,DRDO,
More informationAP Physics 1 and 2 Lab Investigations
AP Physics 1 and 2 Lab Investigations Student Guide to Data Analysis New York, NY. College Board, Advanced Placement, Advanced Placement Program, AP, AP Central, and the acorn logo are registered trademarks
More informationMS Excel as Tool for Modeling, Dynamic Simulation and Visualization ofmechanical Motion
MS Excel as Tool for Modeling, Dynamic Simulation and Visualization ofmechanical Motion MARIE HUBALOVSKA, STEPAN HUBALOVSKY, MARCELA FRYBOVA Department of Informatics Faculty of Science University of Hradec
More informationKalman Filter Applications
Kalman Filter Applications The Kalman filter (see Subject MI37) is a very powerful tool when it comes to controlling noisy systems. The basic idea of a Kalman filter is: Noisy data in hopefully less noisy
More informationAuthor: Hamid A.E. AlJameel (Research Institute: Engineering Research Centre)
SPARC 2010 Evaluation of Carfollowing Models Using Field Data Author: Hamid A.E. AlJameel (Research Institute: Engineering Research Centre) Abstract Traffic congestion problems have been recognised as
More informationEvolutionary denoising based on an estimation of Hölder exponents with oscillations.
Evolutionary denoising based on an estimation of Hölder exponents with oscillations. Pierrick Legrand,, Evelyne Lutton and Gustavo Olague CICESE, Research Center, Applied Physics Division Centro de Investigación
More informationConcept Learning. Machine Learning 1
Concept Learning Inducing general functions from specific training examples is a main issue of machine learning. Concept Learning: Acquiring the definition of a general category from given sample positive
More informationSENSITIVITY ANALYSIS AND INFERENCE. Lecture 12
This work is licensed under a Creative Commons AttributionNonCommercialShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
More informationA MultiModel Filter for Mobile Terminal Location Tracking
A MultiModel Filter for Mobile Terminal Location Tracking M. McGuire, K.N. Plataniotis The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 1 King s College
More informationUnderstanding Proactive vs. Reactive Methods for Fighting Spam. June 2003
Understanding Proactive vs. Reactive Methods for Fighting Spam June 2003 Introduction IntentBased Filtering represents a true technological breakthrough in the proper identification of unwanted junk email,
More informationWireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm
, pp. 99108 http://dx.doi.org/10.1457/ijfgcn.015.8.1.11 Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm Wang DaWei and Wang Changliang Zhejiang Industry Polytechnic College
More informationA Secure Online Reputation Defense System from Unfair Ratings using Anomaly Detections
A Secure Online Reputation Defense System from Unfair Ratings using Anomaly Detections Asha baby PG Scholar,Department of CSE A. Kumaresan Professor, Department of CSE K. Vijayakumar Professor, Department
More informationLogLikelihood Ratiobased Relay Selection Algorithm in Wireless Network
Recent Advances in Electrical Engineering and Electronic Devices LogLikelihood Ratiobased Relay Selection Algorithm in Wireless Network Ahmed ElMahdy and Ahmed Walid Faculty of Information Engineering
More informationChapter 4. Probability and Probability Distributions
Chapter 4. robability and robability Distributions Importance of Knowing robability To know whether a sample is not identical to the population from which it was selected, it is necessary to assess the
More informationInfluence Discovery in Semantic Networks: An Initial Approach
2014 UKSimAMSS 16th International Conference on Computer Modelling and Simulation Influence Discovery in Semantic Networks: An Initial Approach Marcello Trovati and Ovidiu Bagdasar School of Computing
More informationCOMPUTATIONAL METHODS FOR A MATHEMATICAL THEORY OF EVIDENCE
COMPUTATIONAL METHODS FOR A MATHEMATICAL THEORY OF EVIDENCE Jeffrey A. Barnett USC/lnformation Sciences Institute ABSTRACT: Many knowledgebased expert systems employ numerical schemes to represent evidence,
More informationNeovision2 Performance Evaluation Protocol
Neovision2 Performance Evaluation Protocol Version 3.0 4/16/2012 Public Release Prepared by Rajmadhan Ekambaram rajmadhan@mail.usf.edu Dmitry Goldgof, Ph.D. goldgof@cse.usf.edu Rangachar Kasturi, Ph.D.
More informationA New Quantitative Behavioral Model for Financial Prediction
2011 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (2011) (2011) IACSIT Press, Singapore A New Quantitative Behavioral Model for Financial Prediction Thimmaraya Ramesh
More informationPerformance of Probability Transformations Using Simulated Human Opinions
Performance of Probability Transformations Using Simulated Human Opinions Donald J. Bucci, Sayandeep Acharya, Timothy J. Pleskac, and Moshe Kam Department of Electrical and Computer Engineering, Drexel
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 informationFlorida International University  University of Miami TRECVID 2014
Florida International University  University of Miami TRECVID 2014 Miguel Gavidia 3, Tarek Sayed 1, Yilin Yan 1, Quisha Zhu 1, MeiLing Shyu 1, ShuChing Chen 2, HsinYu Ha 2, Ming Ma 1, Winnie Chen 4,
More informationBootstrapping Big Data
Bootstrapping Big Data Ariel Kleiner Ameet Talwalkar Purnamrita Sarkar Michael I. Jordan Computer Science Division University of California, Berkeley {akleiner, ameet, psarkar, jordan}@eecs.berkeley.edu
More informationChapter 6. The stacking ensemble approach
82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described
More informationField Evaluation of a Behavioral Test Battery for DWI
September 1983 NHTSA Technical Note DOT HS806475 U.S. Department of Transportation National Highway Traffic Safety Administration Field Evaluation of a Behavioral Test Battery for DWI Research and Development
More informationA Reliability Point and Kalman Filterbased Vehicle Tracking Technique
A Reliability Point and Kalman Filterbased Vehicle Tracing Technique Soo Siang Teoh and Thomas Bräunl Abstract This paper introduces a technique for tracing the movement of vehicles in consecutive video
More informationUnion/intersection vs. alternative/conjunction  defining posterior hypotheses in C2 systems
nion/intersection vs. alternative/conjunction  defining posterior hypotheses in C2 systems Ksawery Krenc RSSD R&D Marine Technology Centre Gdynia, Poland ksawery.krenc@ctm.gdynia.pl Abstract This paper
More informationEnsemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 20150305
Ensemble Methods Knowledge Discovery and Data Mining 2 (VU) (707004) Roman Kern KTI, TU Graz 20150305 Roman Kern (KTI, TU Graz) Ensemble Methods 20150305 1 / 38 Outline 1 Introduction 2 Classification
More informationRegional Transport in Canterbury Health Impact Analysis Dynamic Simulation Model
Regional Transport in Canterbury Health Impact Analysis Dynamic Simulation Model Final Report for Environment Canterbury David Rees and Adrian Field 11 June 2010 CONTENTS 1. Background 2. Model Structure
More informationSetBased Design: A DecisionTheoretic Perspective
SetBased Design: A DecisionTheoretic Perspective Chris Paredis, Jason Aughenbaugh, Rich Malak, Steve Rekuc Product and Systems Lifecycle Management Center G.W. Woodruff School of Mechanical Engineering
More informationDiscrete FrobeniusPerron Tracking
Discrete FrobeniusPerron Tracing Barend J. van Wy and Michaël A. van Wy French SouthAfrican Technical Institute in Electronics at the Tshwane University of Technology Staatsartillerie Road, Pretoria,
More informationAn EnergyBased Vehicle Tracking System using Principal Component Analysis and Unsupervised ART Network
Proceedings of the 8th WSEAS Int. Conf. on ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING & DATA BASES (AIKED '9) ISSN: 179519 435 ISBN: 97896474512 An EnergyBased Vehicle Tracking System using Principal
More informationCRM Forum Resources http://www.crmforum.com
CRM Forum Resources http://www.crmforum.com BEHAVIOURAL SEGMENTATION SYSTEMS  A Perspective Author: Brian Birkhead Copyright Brian Birkhead January 1999 Copyright Brian Birkhead, 1999. Supplied by The
More informationTracking Groups of Pedestrians in Video Sequences
Tracking Groups of Pedestrians in Video Sequences Jorge S. Marques Pedro M. Jorge Arnaldo J. Abrantes J. M. Lemos IST / ISR ISEL / IST ISEL INESCID / IST Lisbon, Portugal Lisbon, Portugal Lisbon, Portugal
More informationAutomated Process for Generating Digitised Maps through GPS Data Compression
Automated Process for Generating Digitised Maps through GPS Data Compression Stewart Worrall and Eduardo Nebot University of Sydney, Australia {s.worrall, e.nebot}@acfr.usyd.edu.au Abstract This paper
More informationPredicting the Stock Market with News Articles
Predicting the Stock Market with News Articles Kari Lee and Ryan Timmons CS224N Final Project Introduction Stock market prediction is an area of extreme importance to an entire industry. Stock price is
More informationBbleaching: Agile Overtraining Avoidance in the WiSARD Weightless Neural Classifier
Bbleaching: Agile Overtraining Avoidance in the WiSARD Weightless Neural Classifier Danilo S. Carvalho 1,HugoC.C.Carneiro 1,FelipeM.G.França 1, Priscila M. V. Lima 2 1 Universidade Federal do Rio de
More informationVision Based Traffic Light Triggering for Motorbikes
Vision Based Traffic Light Triggering for Motorbikes Tommy Chheng Department of Computer Science and Engineering University of California, San Diego tcchheng@ucsd.edu Abstract Current traffic light triggering
More informationChapter 14 Managing Operational Risks with Bayesian Networks
Chapter 14 Managing Operational Risks with Bayesian Networks Carol Alexander This chapter introduces Bayesian belief and decision networks as quantitative management tools for operational risks. Bayesian
More informationEM Clustering Approach for MultiDimensional Analysis of Big Data Set
EM Clustering Approach for MultiDimensional Analysis of Big Data Set Amhmed A. Bhih School of Electrical and Electronic Engineering Princy Johnson School of Electrical and Electronic Engineering Martin
More informationPath Tracking for a Miniature Robot
Path Tracking for a Miniature Robot By Martin Lundgren Excerpt from Master s thesis 003 Supervisor: Thomas Hellström Department of Computing Science Umeå University Sweden 1 Path Tracking Path tracking
More informationProblems often have a certain amount of uncertainty, possibly due to: Incompleteness of information about the environment,
Uncertainty Problems often have a certain amount of uncertainty, possibly due to: Incompleteness of information about the environment, E.g., loss of sensory information such as vision Incorrectness in
More informationEFFICIENT DATA PREPROCESSING FOR DATA MINING
EFFICIENT DATA PREPROCESSING FOR DATA MINING USING NEURAL NETWORKS JothiKumar.R 1, Sivabalan.R.V 2 1 Research scholar, Noorul Islam University, Nagercoil, India Assistant Professor, Adhiparasakthi College
More informationWORLD TRADE ORGANIZATION
WORLD TRADE ORGANIZATION Council for Trade in Services Special Session TN/S/W/51 23 September 2005 (054227) Original: English COMMUNICATION FROM SWITZERLAND Methodology to assess Schedules of commitments
More informationINTRUSION PREVENTION AND EXPERT SYSTEMS
INTRUSION PREVENTION AND EXPERT SYSTEMS By Avi Chesla avic@vsecure.com Introduction Over the past few years, the market has developed new expectations from the security industry, especially from the intrusion
More informationT O B C A T C A S E G E O V I S A T DETECTIE E N B L U R R I N G V A N P E R S O N E N IN P A N O R A MISCHE BEELDEN
T O B C A T C A S E G E O V I S A T DETECTIE E N B L U R R I N G V A N P E R S O N E N IN P A N O R A MISCHE BEELDEN Goal is to process 360 degree images and detect two object categories 1. Pedestrians,
More informationSimulation of Nonlinear Stochastic Equation Systems
Simulation of Nonlinear Stochastic Equation Systems Veit Köppen 1, HansJ. Lenz 2 (Freie Universität Berlin) Abstract In this paper, we combine data generated by MonteCarlo simulation, which is based
More informationModels for Product Demand Forecasting with the Use of Judgmental Adjustments to Statistical Forecasts
Page 1 of 20 ISF 2008 Models for Product Demand Forecasting with the Use of Judgmental Adjustments to Statistical Forecasts Andrey Davydenko, Professor Robert Fildes a.davydenko@lancaster.ac.uk Lancaster
More informationSeismic Risk Assessment Procedures for a System consisting of Distributed Facilities Part three Insurance Portfolio Analysis
Seismic Risk Assessment Procedures for a System consisting of Distributed Facilities Part three Insurance Portfolio Analysis M. Achiwa & M. Sato Yasuda Risk Engineering Co., Ltd., Tokyo, Japan M. Mizutani
More informationVilnius University. Faculty of Mathematics and Informatics. Gintautas Bareikis
Vilnius University Faculty of Mathematics and Informatics Gintautas Bareikis CONTENT Chapter 1. SIMPLE AND COMPOUND INTEREST 1.1 Simple interest......................................................................
More informationComponent Ordering in Independent Component Analysis Based on Data Power
Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals
More informationAnother Look at Sensitivity of Bayesian Networks to Imprecise Probabilities
Another Look at Sensitivity of Bayesian Networks to Imprecise Probabilities Oscar Kipersztok Mathematics and Computing Technology Phantom Works, The Boeing Company P.O.Box 3707, MC: 7L44 Seattle, WA 98124
More informationUnderstanding and Applying Kalman Filtering
Understanding and Applying Kalman Filtering Lindsay Kleeman Department of Electrical and Computer Systems Engineering Monash University, Clayton 1 Introduction Objectives: 1. Provide a basic understanding
More informationPRACTICE BOOK COMPUTER SCIENCE TEST. Graduate Record Examinations. This practice book contains. Become familiar with. Visit GRE Online at www.gre.
This book is provided FREE with test registration by the Graduate Record Examinations Board. Graduate Record Examinations This practice book contains one actual fulllength GRE Computer Science Test testtaking
More informationBayesian sample size determination of vibration signals in machine learning approach to fault diagnosis of roller bearings
Bayesian sample size determination of vibration signals in machine learning approach to fault diagnosis of roller bearings Siddhant Sahu *, V. Sugumaran ** * School of Mechanical and Building Sciences,
More informationH13187 INVESTIGATING THE BENEFITS OF INFORMATION MANAGEMENT SYSTEMS FOR HAZARD MANAGEMENT
H13187 INVESTIGATING THE BENEFITS OF INFORMATION MANAGEMENT SYSTEMS FOR HAZARD MANAGEMENT Ian Griffiths, Martyn Bull, Ian Bush, Luke Carrivick, Richard Jones and Matthew Burns RiskAware Ltd, Bristol,
More informationVEHICLE LOCALISATION AND CLASSIFICATION IN URBAN CCTV STREAMS
VEHICLE LOCALISATION AND CLASSIFICATION IN URBAN CCTV STREAMS Norbert Buch 1, Mark Cracknell 2, James Orwell 1 and Sergio A. Velastin 1 1. Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE,
More informationMCTA: Target Tracking Algorithm based on
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE INFOCOM 27 proceedings. MCTA: Target Tracking Algorithm based on
More informationA CONTROLLER FOR MERGING TRAFFIC ONTO A HIGHWAY
A CONTROLLER FOR MERGING TRAFFIC ONTO A HIGHWAY Omar Ahmad Research and Development Project Leader & Huidi Tang Senior Systems Analyst National Advanced Driving Simulator The University of Iowa Abstract
More informationThree types of messages: A, B, C. Assume A is the oldest type, and C is the most recent type.
Chronological Sampling for Email Filtering ChingLung Fu 2, Daniel Silver 1, and James Blustein 2 1 Acadia University, Wolfville, Nova Scotia, Canada 2 Dalhousie University, Halifax, Nova Scotia, Canada
More informationIntent Based Filtering: A Proactive Approach Towards Fighting Spam
Intent Based Filtering: A Proactive Approach Towards Fighting Spam By Priyanka Agrawal AbstractThe cyber security landscape has changed considerably over the past few years. Since 2003, while traditional
More informationA Network Flow Approach in Cloud Computing
1 A Network Flow Approach in Cloud Computing Soheil Feizi, Amy Zhang, Muriel Médard RLE at MIT Abstract In this paper, by using network flow principles, we propose algorithms to address various challenges
More informationOpen Access Research on Application of Neural Network in Computer Network Security Evaluation. Shujuan Jin *
Send Orders for Reprints to reprints@benthamscience.ae 766 The Open Electrical & Electronic Engineering Journal, 2014, 8, 766771 Open Access Research on Application of Neural Network in Computer Network
More informationBENEFIT OF DYNAMIC USE CASES TO EARLY DESIGN A DRIVING ASSISTANCE SYSTEM FOR PEDESTRIAN/TRUCK COLLISION AVOIDANCE
BENEFIT OF DYNAMIC USE CASES TO EARLY DESIGN A DRIVING ASSISTANCE SYSTEM FOR PEDESTRIAN/TRUCK COLLISION AVOIDANCE Hélène Tattegrain, Arnaud Bonnard, Benoit Mathern, LESCOT, INRETS France Paper Number 090489
More informationSpeed Performance Improvement of Vehicle Blob Tracking System
Speed Performance Improvement of Vehicle Blob Tracking System Sung Chun Lee and Ram Nevatia University of Southern California, Los Angeles, CA 90089, USA sungchun@usc.edu, nevatia@usc.edu Abstract. A speed
More informationMONITORING AND DIAGNOSIS OF A MULTISTAGE MANUFACTURING PROCESS USING BAYESIAN NETWORKS
MONITORING AND DIAGNOSIS OF A MULTISTAGE MANUFACTURING PROCESS USING BAYESIAN NETWORKS Eric Wolbrecht Bruce D Ambrosio Bob Paasch Oregon State University, Corvallis, OR Doug Kirby Hewlett Packard, Corvallis,
More informationFall Detection System based on Kinect Sensor using Novel Detection and Posture Recognition Algorithm
Fall Detection System based on Kinect Sensor using Novel Detection and Posture Recognition Algorithm Choon Kiat Lee 1, Vwen Yen Lee 2 1 Hwa Chong Institution, Singapore choonkiat.lee@gmail.com 2 Institute
More informationANNEX 2: Assessment of the 7 points agreed by WATCH as meriting attention (cover paper, paragraph 9, bullet points) by Andy Darnton, HSE
ANNEX 2: Assessment of the 7 points agreed by WATCH as meriting attention (cover paper, paragraph 9, bullet points) by Andy Darnton, HSE The 7 issues to be addressed outlined in paragraph 9 of the cover
More informationAnalysis of Micromouse Maze Solving Algorithms
1 Analysis of Micromouse Maze Solving Algorithms David M. Willardson ECE 557: Learning from Data, Spring 2001 Abstract This project involves a simulation of a mouse that is to find its way through a maze.
More informationCredit Card Market Study Interim Report: Annex 4 Switching Analysis
MS14/6.2: Annex 4 Market Study Interim Report: Annex 4 November 2015 This annex describes data analysis we carried out to improve our understanding of switching and shopping around behaviour in the UK
More informationInformatics 2D Reasoning and Agents Semester 2, 201516
Informatics 2D Reasoning and Agents Semester 2, 201516 Alex Lascarides alex@inf.ed.ac.uk Lecture 30 Markov Decision Processes 25th March 2016 Informatics UoE Informatics 2D 1 Sequential decision problems
More informationInternational Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, MayJun 2014
RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer
More informationExperiments in Web Page Classification for Semantic Web
Experiments in Web Page Classification for Semantic Web Asad Satti, Nick Cercone, Vlado Kešelj Faculty of Computer Science, Dalhousie University Email: {rashid,nick,vlado}@cs.dal.ca Abstract We address
More informationSpam Filtering based on Naive Bayes Classification. Tianhao Sun
Spam Filtering based on Naive Bayes Classification Tianhao Sun May 1, 2009 Abstract This project discusses about the popular statistical spam filtering process: naive Bayes classification. A fairly famous
More information