Advanced powerful tools are sought for in order to conduct power system studies, especially for large scale systems. In this paper, an efficient PC-based environment for power system studies is presented. It is based ...
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Advanced powerful tools are sought for in order to conduct power system studies, especially for large scale systems. In this paper, an efficient PC-based environment for power system studies is presented. It is based on both analytical and artificial intelligence techniques. To demonstrate its effectiveness, this environment has been used for restoration studies following a recent blackout in the Hellenic power system, with very promising results.
This paper investigates the implementation of a hybrid methodology, which combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for the modeling of the supervisor of Large Scale systems. The description...
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This paper investigates the implementation of a hybrid methodology, which combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for the modeling of the supervisor of Large Scale systems. The description and the construction of Fuzzy Cognitive Map will be extensively examined and it will be proposed a model for the supervisor. There is an oncoming need for more autonomous and intelligent systems, especially in Large Scale systems and the application of Fuzzy Cognitive Map for the modeling of the Supervisor may contribute in the development of more autonomous systems.
This paper proposes an approach for the design of discrete-time decentralized control systems with m-step delay sharing information pattern, employing the modelbased predictive control (MBPC) scheme combined with fuzz...
This paper proposes an approach for the design of discrete-time decentralized control systems with m-step delay sharing information pattern, employing the modelbased predictive control (MBPC) scheme combined with fuzzy prediction for the interconnections among the subsystems. A state-space model is used at each control station to predict the corresponding subsystem output over a long-range time period. The interaction trajectories are considered to be non-linear functions of the states of the subsystems. For all cases the interconnections and the necessary predictions for them are estimated by an appropriate adaptive fuzzy identifier based on the generation of linguistic IF-THEN rules and the on-line construction of a common fuzzy rule base. Representative computer simulation results are provided and compared for nontrivial example systems.
This paper proposes an approach for the design of discrete-time decentralized control systems covering not only the case of m-step delay sharing information pattern, but also any general nonclassical information patte...
This paper proposes an approach for the design of discrete-time decentralized control systems covering not only the case of m-step delay sharing information pattern, but also any general nonclassical information pattern where the non-local information is not spread among the subsystems. It employs the model-based predictive control (MBPC) scheme combined with fuzzy prediction for the interconnections among the subsystems. A state space model is used at each control station to predict the corresponding subsystem output over a long-range time period. The interaction trajectories are considered to be non-linear functions of the states of the subsystems. In all cases, the interconnections and the necessary predictions for them are estimated by an appropriate neuro-fuzzy identifier trained on-line using the back-propagation training algorithm. Representative computer simulation results are provided and compared for nontrivial example systems.
Intelligent Manufacturing systems is the major challenge that must be met by scientific community in order to develop the next generation sophisticated manufacturing systems. This paper presents an overview of new pro...
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Intelligent Manufacturing systems is the major challenge that must be met by scientific community in order to develop the next generation sophisticated manufacturing systems. This paper presents an overview of new promising domains, such as fuzzy logic, neural networks and hybrid intelligent control systems, that could be utilised to achieve human intention for more advanced manufacturing systems. The modelling of the supervisor of manufacturing systems using fuzzy cognitive map is proposed and system functionalization architecture is presented. Finally, conclusions of an international symposium on issues and challenges of Manufacturing and Control Education for the 21 st Century are presented.
In this paper the application of Fuzzy Cognitive Map (FCM) in controlling a process problem and its use in modelling Supervisory Manufacturing systems is presented. The description, construction and the mathematical m...
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In this paper the application of Fuzzy Cognitive Map (FCM) in controlling a process problem and its use in modelling Supervisory Manufacturing systems is presented. The description, construction and the mathematical model of Fuzzy Cognitive Map are examined. Then, a chemical process is modelled with FCM and its behaviour is simulated and the application of Fuzzy Cognitive Maps in the mode ling of the Supervisor of Manufacturing systems is discussed.
Extends the kinematic manipulability concept commonly used for serial manipulators to general constrained rigid multibody systems. Examples of such systems include multiple cooperating manipulators, multiple fingers h...
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Extends the kinematic manipulability concept commonly used for serial manipulators to general constrained rigid multibody systems. Examples of such systems include multiple cooperating manipulators, multiple fingers holding a payload, multi-leg walking robots, and variable geometry trusses. Explicit formulas for velocity and force manipulability ellipsoids are derived and their duality explained. The concept of unstable grasp and manipulable grasp are also extended and illustrated with examples.
Presents an algorithm for a simulator to train mobile robot operators in the use of deictic commands. It shows that robots can be deictically controlled in any reasonable environment. Deictic control is a technique wh...
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Presents an algorithm for a simulator to train mobile robot operators in the use of deictic commands. It shows that robots can be deictically controlled in any reasonable environment. Deictic control is a technique which directs the robot by visually servoing on targets pointed out to the robot by its operator. This provides a direct, intuitive control method which any sighted operator can use. Previous work has formally defined canonical targets required for deictic control, and has described a training system for prospective users. This paper extends that work by providing a specific algorithm for the construction of deictically controlled paths. The algorithm is described in detail and results are presented. This algorithm shows that deictic commands and their targets can be simply determined by an agent able to sense the surrounding environment.
This paper discusses the swing-up control of a two link robot mechanism known as the Acrobot. Previously, a technique using meta-rules was proposed to compensate for external disturbances when using a dynamic fuzzy co...
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This paper discusses the swing-up control of a two link robot mechanism known as the Acrobot. Previously, a technique using meta-rules was proposed to compensate for external disturbances when using a dynamic fuzzy controller. In this paper, stability of the meta-rule system is analyzed from a global asymptotic stability viewpoint based on fuzzy characteristic matrices. These matrices are generated from the first phase of a three phase learning process involving the combination of genetic algorithms, dynamic switching fuzzy systems, and meta-rule techniques with the purpose of developing a better performing fuzzy controller.
The paper is concerned with the implementation of cryptographic hash functions on the regular array of simple cellular neural network (CNN) cells with periodic boundary conditions. Cryptographic hash functions enable ...
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The paper is concerned with the implementation of cryptographic hash functions on the regular array of simple cellular neural network (CNN) cells with periodic boundary conditions. Cryptographic hash functions enable message origin authentication and validation of message content integrity. A class of cryptographic hash functions-termed Cartesian authentication codes-provide provable (unconditional) security for message authentication between two mutually trustful parties sharing a secret key. We succeeded in implementing existing constructions of Cartesian authentication codes on today's CNN Universal Machine (CNN-UM) chips. Here we prove that rather complex (binary) arithmetic can be performed on a simple CNN chip, by providing an algorithm to implement a specific Cartesian authentication code based on the computation of a polynomial expression over a finite field. The bitrate of the computation is in the 100 Mbit/sec range with existing chips.
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