In this paper we deal with a companion that is capable of migration to variety of embodiments in order to fulfil its tasks (including the social one) and to achieve its goals. It is important that identity of a compan...
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In this paper we deal with a companion that is capable of migration to variety of embodiments in order to fulfil its tasks (including the social one) and to achieve its goals. It is important that identity of a companion in each utilised embodiment can be recognised by the user regardless technological constraints and own capabilities of a robotic platform. We are focused on the case when one companion uses two robots, embodied in different degrees, whereby one robot is poorly embodied. In this context the concept of unfalsified identity is introduced. In video-based user studies the less embodied robot companion reveals a certain set of characteristic behaviours relevant the context and the currently carried out task. These behaviours are consistent with behaviours observed by the user earlier, in similar contexts, when the companion was more embodied. It follows from the study that the user can properly recognise the identity of this companion with a certain level of confidence. A number of observations relevant to behaviours as identity cues are presented and discussed.
A variant of spiking neural P systems was recently investigated by the authors, using astrocytes that have excitatory and inhibitory influence on synapses. In this work, we consider this system in the non-synchronized...
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In this paper, we propose a mapping and navigation method for mobile robots with low computational resources and limited memory capacity. The proposed navigation method is based on topological mapping of visual featur...
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In the paper we show a new three-stage algorithm identifying the Hammerstein system nonlinearity. The algorithm is designed to work when a poor a priori knowledge is available and when the measurement data set is smal...
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In the paper we show a new three-stage algorithm identifying the Hammerstein system nonlinearity. The algorithm is designed to work when a poor a priori knowledge is available and when the measurement data set is small. In the first stage, a deconvolution routine is applied to output signal in order to diminish a (usually) harmful influence of the linear dynamic component. Such filtered output is then used in a standard nonparametric estimate (be it kernel or orthogonal series one) to recover the unknown characteristics. Finally, the results of nonparametric estimation are used in the algorithm selecting the best parametric model. The entire proposed approach is illustrated by the simulation example.
In this paper a survey of selected topics concerning development of wireless sensor network systems formed by implantable medical devices (IMDs) located on the patient's body are presented and discussed. The focus...
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ISBN:
(纸本)9781467307086
In this paper a survey of selected topics concerning development of wireless sensor network systems formed by implantable medical devices (IMDs) located on the patient's body are presented and discussed. The focus is on security aspects and effective management of power resources available in implanted sensors. Implantable medical devices are very sensitive to the energy constraints. Moreover, most applications of IMDs require protection against injection or modification of disseminated measurements. Therefore, the transmission between IMDs and external devices, which aim is to monitor and control of IMDs has to be protected. The approaches to provide a security and ensure a privacy during monitoring, controlling, drug disposition and implant identification are discussed. The concept of novel energy-efficient security system for wearable devices to monitor the patient's health is presented in the final part of this paper.
In the paper we present a new method for solving optimal control problems of a class of hybrid systems. We describe the new effective algorithm based on memetic algorithm (MA) for optimal control of switched systems. ...
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In the paper we present a new method for solving optimal control problems of a class of hybrid systems. We describe the new effective algorithm based on memetic algorithm (MA) for optimal control of switched systems. We concentrate on a class of problems in which a pre-specified sequence of active subsystems is known. Our aim is to find both the optimal switching instants and the optimal continuous inputs. The new approach, which we propose, decomposes the cost functional of the basic optimal control problem in Bolza form in two terms. The first term depends explicitly on a value of state variables at the final time. The second term depends on state and control trajectories. In order to solve those two tasks we used MA as the multi-objective optimization algorithm. In this paper we considered a fundamental bi-criteria case with two mentioned before functions: the value of state variables at the final time and the state and control trajectories. In order to find an approximation of Pareto frontier, we proposed new effective method based on genetic algorithm (GA) and local search (LS). Problems properties were taken into consideration in the design of our new approach of solving it. They were used to construct new algorithm inspired by the LS NSGA-II, which performed rather well in multi-criteria scheduling problem. Since simple genetic algorithms are efficient heuristics in searching for optimal solutions, but lack the accuracy of some more computational complex algorithms, a hybrid algorithm was constructed. It uses fast non-dominated sorting, in order to evaluate child population and allocate solutions to corresponding Pareto frontiers. I addition a local search method was used, in order to find more differentiated and better solutions. Clustering solutions from Pareto frontiers also improved diversity of solutions in child population. This approach can be used as a start point for searching for algorithm for solving optimal control problems of switched systems wit
Model Checking is a method for verification. The model will be checked until the specification of it is proved or disproved. With the rising complexity of big models, there are non-checkable cases, in which cases the ...
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Model Checking is a method for verification. The model will be checked until the specification of it is proved or disproved. With the rising complexity of big models, there are non-checkable cases, in which cases the problem can be analyzed by some models, for example, bounded Model Checking means to analyze the model until a defined time or depth. The verification happens automatically. The programs for doing this are called Model Checking Tools or Model Checker. Model Checking are used in both software and hardware verification. It is an inherent part of hardware verification, whereas it is less used in the software verification.
In this paper, we present an approach towards autonomous grasping of objects according to their category and a given task. Recent advances in the field of object segmentation and categorization as well as task-based g...
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Nowadays, online and real-time pattern classification applications are required in many areas. Most classification algorithms are suitable only for off-line applications. Using the concept of evolving intelligent syst...
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To enhance classification performance by making use of easily available unlabelled data to overcome the scarcity of labelled data, this paper proposes an Embedded Co-Adaboost algorithm that integrates multi-view learn...
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