18154847
Dynamic domination in fuzzy causal networks
This paper presents a dynamic domination theory for fuzzy causal networks (FCN). There are three major contributions. First, we propose a new inference procedure based on dominating sets. Second, we introduce the concepts of dynamic and minimal dynamic dominating sets (DDS and MDDS) in an FCN. To reflect changes of dominance with time, we also introduce the concept of a dynamic dominating process (DDP) that has significant implications in many real-world problems. We pay a special attention to the minimal dynamic dominating process (MDDP) and develop rules for generating DDP and MDDP. Third, we investigate dynamic dominating sets with extended feedback, which we call effective dynamic dominating sets (EDDS), and related effective dynamic dominating process (EDDP). This study unveils a very important phenomenon in FCN: At any time t, either an EDDS exists or there is a dramatic change of the states of vertices. In the latter case we also identify the special structure of the sub-FCN induced by active vertices.
10848842
Vision Guided Circumnavigating Autonomous Robots
In this paper, we propose a system for vision guided autonomous circumnavigation, allowing robots to navigate around objects of arbitrary pose. The system performs knowledge-based object recognition from an intensity image using a canonical viewer-centred model. A path planned from a geometric model then guides the robot in circum-navigating the object. This system can be used in many applications where robots have to recognize and manipulate objects of unknown pose and placement. Such applications occur in a variety of contexts such as factory automation, underwater and space exploration, and nuclear power station maintenance. We also define a canonical-view graph to model objects, which is a viewer-centred representation.
14780024
Fuzzy causal networks: general model, inference, and convergence
In this paper, we first propose a general framework for fuzzy causal networks (FCNs). Then, we study the dynamics and convergence of such general FCNs. We prove that any general FCN with constant weight matrix converges to a limit cycle or a static state, or the trajectory of the FCN is not repetitive. We also prove that under certain conditions a discrete state general FCN converges to its limit cycle or static state in O(n) steps, where n is the number of vertices of the FCN. This is in striking contrast with the exponential running time 2/sup n/, which is accepted widely for classic FCNs.
45372122
Interrogating the structure of fuzzy cognitive maps
Abstract Causal algebra in fuzzy cognitive maps (FCMs) plays a critical role in the analysis and design of FCMs. Improving causal algebra in FCMs to model complicated situations has been one of the major research topics in this area. In this paper we propose a dynamic causal algebra in FCMs which can improve FCMs' inference and representation capability. The dynamic causal algebra shows that the indirect, strongest, weakest and total effects a vertex influences another in the FCM not only depend on the weights along all directed paths between the two vertices but also the states of the vertices on the directed paths. Therefore, these effects are nonlinear dynamic processes determined by initial conditions and propagated in the FCM to reach a static or cyclic pattern. We test our theory with a simple example.
207320245
Hidden Markov models with factored Gaussian mixtures densities
We present a factorial representation of Gaussian mixture models for observation densities in hidden Markov models (HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized parameters by the Expectation Maximization (EM) algorithm and propose a novel method for initializing them. To compare the performances of the proposed models with that of the factorial hidden Markov models and HMMs, we have carried out extensive experiments which show that this modelling approach is effective and robust.
7502395
Dynamic image sequence analysis using fuzzy measures
In this paper, we present an image understanding system using fuzzy sets and fuzzy measures. This system is based on a symbolic object-oriented image interpretation system. We apply a simple, powerful three-dimensional (3-D) recursive filter to tracking moving objects in a dynamic image sequence. This filter has a time-varying 3-D frequency-planar passband that is adapted in a feedback system to automatically track moving objects. However, as objects in the image sequence are not well-defined and are engaged in dynamic activities, their shapes and trajectories in most cases can be described only vaguely. In order to handle these uncertainties, we use fuzzy measures to capture subtle variations and manage the uncertainties involved. This enables us to develop an image understanding system that produces a very natural output. We demonstrate the effectiveness of our system with complex real traffic scenes.
3166137
Integrating Temporal Logics
In this paper, we study the predicative semantics of different temporal logics and the relationships between them. We use a notation called generic composition to simplify the manipulation of predicates. The modalities of possibility and necessity become generic composition and its inverse of converse respectively. The relationships between different temporal logics are also characterised as such modalities. Formal reasoning is carried out at the level of predicative semantics and supported by the higher-level laws of generic composition and its inverse. Various temporal domains are unified under a notion called resource cumulation. Temporal logics based on these temporal domains can be readily defined, and their axioms identified. The formalism provides a framework in which human experience about system development can be formalised as refinement laws. The approach is demonstrated in the transformation from Duration Calculus to Temporal Logic of Actions. A number of common design patterns are studied. The refinement laws identified are then applied to the case study of water pump controlling.
16274304
Face detection and tracking in video using dynamic programming
Face detection and tracking are important in video content analysis since the most important objects in most video are human beings. This paper proposes a new approach for combined face detection and tracking in video. The face detection algorithm is a fast template matching procedure using iterative dynamic programming (DP). Although the face detection algorithm is designed for frontal face, the same mechanism can also be applied to track non-frontal faces with online adapted face models. Due to the essence of template matching, the algorithm is capable of comparing the similarity among different faces, which makes it suitable for tracking the same face that occur at disjointed temporal locations in video. While the proposed face detection method provides comparable accuracy as the neural network based approach, it is much faster.
12789585
A Secure Agent Architecture for Sensor Networks
Advances in sensor and wireless technologies will enable thousands, or even millions of small but smart sensors to be deployed for a wide range of monitoring applications. The sensor networks must be dynamically configurable and adaptive in order to response actively to events. Security must also be built into sensor networks at the very initial design stage to prevent any potential threats. This paper presents a design of a generic software architecture based on tiny active agents for sensor networks. The architecture has built-in security components. This architecture will significantly benefit the dynamic and secure deployment and configuration tasks of large sensor networks for various applications.
6592348
Scheduling UET-UCT Series-Parallel Graphs on Two Processors
Abstract The scheduling of task graphs on two identical processors is considered. It is assumed that tasks have unit-execution-time, and arcs are associated with unit-communication-time delays. The problem is to assign the tasks to the two processors and schedule their execution in order to minimize the makespan. A quadratic algorithm is proposed to compute an optimal schedule for a class of series-parallel graphs, called SP1 graphs, which includes in particular in-forests and out-forests.
13865796
Virtual Screening of Novel Noncovalent Inhibitors for SARS-CoV 3C-like Proteinase
The SARS coronavirus 3C-like proteinase is considered as a potential drug design target for the treatment of severe acute respiratory syndrome (SARS). Owing to the lack of available drugs for the treatment of SARS, the discovery of inhibitors for SARS coronavirus 3C-like proteinase that can potentially be optimized as drugs appears to be highly desirable. We have built a "flexible" three-dimensional model for SARS 3C-like proteinase by homology modeling and multicanonical molecular dynamics method and used the model for virtual screening of chemical databases. After Dock procedures, strategies including pharmocophore model, consensus scoring, and "drug-like" filters were applied in order to accelerate the process and improve the success rate of virtual docking screening hit lists. Forty compounds were purchased and tested by HPLC and colorimetric assay against SARS 3C-like proteinase. Three of them including calmidazolium, a well-known antagonist of calmodulin, were found to inhibit the enzyme with an apparent K(i) from 61 to 178 microM. These active compounds and their binding modes provide useful information for understanding the binding sites and for further selective drug design against SARS and other coronavirus.