Abstract Knowledge is modeled in various knowledge representation formats like semantic network, decision table, decision tree, etc. Such representations are used to express knowledge in natural languages. There is a major difference between programming languages and Natural languages. They both are used for communication purpose. The former is used for communication between human and machine and the latter is used for human communication. Semantic network is one of the knowledge representation technique used for communication between knowledge engineer and user. It lacks logical completeness and exactness. Due to the uncertainty of information about nodes and links in semantic networks problems arise in inferring and queering of knowledge. There is an object oriented layout of knowledge representation known as Frames structures that can be modified with slot filling capability and procedural attachments. In this research paper, a solution is proposed to extract knowledge from semantic networks ontologies in a semi-automatic way of knowledge transformation into an inferring suitable knowledge source so-called frame structure. Our proposed technique readout a standard Owl XML file expressing semantic network ontology designed in Protégé, and transforms into an equivalent frame based knowledge source. The resulted knowledge source is stored in Frames repository providing better inferring capability. Various case examples have been adopted in order to validate the proposed technique by verifying the number and names of the nodes/frames, the number and type of attributes and methods for each node/frame and the type of relationship between any of the two nodes/frames were identical in the input and the corresponding output. Read More Read Less

Abstract Human crowd behavior analysis is a subject of great interest in research now days. Great advantage of investigating dense human crowds in places like mosques and temples to perform automatic surveillance for any unusual activity detection that might be a subject of interest and must be addressed on earliest to avoid accident. We present robust statistical skeleton for modeling a dense crowded scene and then find anomaly in it. Our main intuition is to utilize the true dense motion of the scene to model main motion pattern by using temporal statistical behaviors. After removing the noise we genuinely find anomalies in the scene by using the model. We used Matlab for designing algorithm of motion pattern and anomaly detection. Our test reflect that temporal motion pattern modeling presents hopeful results in actual world scene with dense structured crowded motion. This paper deals with the robust fault detection (FD) identification of discrete-time periodic systems using modelmatching problem. Discrete-time periodic system is first converted into linear time invariant (LTI) system using lifting technique and then conventional fault detection filter (FDF) problem is applied on such discrete LTI system. This problem is multi-objective or optimization problem in which effect of disturbance on residual is minimized and at the same time effect of fault on residual is maximized by solving some linear matrix inequalities (LMI). In the end, a design example is solved and simulation results are provided. Read More Read Less

Abstract This paper presents a new way of modeling the OCC model of emotions. A Bayesian network is employed to represent the structure and the relationship among emotions of the OCC model. In this manner the stochastic behavior, a new characteristic, is inserted in the simulations using the OCC model. Emotions a ect directly the human behavior ff influencing many aspects e.g. decisionmaking, actions and memory. Specially because emotions are not a deterministic variable we propose the use of the Bayesian networks to model them. Bayesian networks are an excellent tool for modeling real problems because they use probabilistic reasoning which differs from the logical reasoning used by the majority of the computational tools. In order to evaluate the proposed model we applied it in a BDI Multi - Agent system. In this system we tested the introduction of emotions in three hypothetical situations and compared how these emotions influence the behavior of the agents. This paper deals with the robust fault detection (FD) identification of discrete-time periodic systems using modelmatching problem. Discrete-time periodic system is first converted into linear time invariant (LTI) system using lifting technique and then conventional fault detection filter (FDF) problem is applied on such discrete LTI system. This problem is multi-objective or optimization problem in which effect of disturbance on residual is minimized and at the same time effect of fault on residual is maximized by solving some linear matrix inequalities (LMI). In the end, a design example is solved and simulation results are provided. Read More Read Less