Bio-hybrid systems in which living organisms interact and self-organize with multi-robot systems are a novel approach in engineering and biology. We show here how a group of honeybees and robots can interact in collec...
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Bio-hybrid systems in which living organisms interact and self-organize with multi-robot systems are a novel approach in engineering and biology. We show here how a group of honeybees and robots can interact in collective decisionmaking and how computer code that adds feedback loops to the system may affect the global system properties. This study contains a series of experiments with living honeybees and robots as well as a cellular-automaton inspired model that is simple, yet still in good agreement with the empirical findings presented here. Using this model, we explore the most likely candidates for local parameters in the proximate mechanisms of the animals, thus further the understanding of this natural system in a context that is relevant for such bio-hybrid manifestations. We identify positive feedback based on bee-to-bee collision and temperature as an important factor governing collective decisionmaking and found the stopping probability after close-encounters among bees as a crucial local parameter. This study is the first step towards using computer and robotic technology to monitor and control complex animal societies like honeybees.
An attacker can very possibly make significant damage for the power grid with a proper sequence of timing and attacks. Existing approaches neglect the power system generation loss and also identification of critical a...
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An attacker can very possibly make significant damage for the power grid with a proper sequence of timing and attacks. Existing approaches neglect the power system generation loss and also identification of critical attack sequences. In this paper, we investigate a reinforcement learning approach to identify the minimum number of attacks/actions to reach blackout threshold. The attacker will only have limited topological information of the power systems. Proper state vectors, action vectors and also reward are designed in this smart grid security environment. The proposed method is evaluated on a W & W 6 bus system and an ieee 30 bus system. The attack performance is tested for different percentages of line outage. The amount of load shedding is also considered as an attack objective and demonstrated on W & W 6 bus system. The optimal attack sequence is identified through a trial-and-error learning process and is then validated on a power system simulator.
The arrangement of fuzzy number, either in ascending or descending order, is utmost significant in optimization modeling, decisionmaking and also in ranking fuzzy numbers. As a generalization of the fuzzy number, ran...
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ISBN:
(纸本)9781538676394;9781538676387
The arrangement of fuzzy number, either in ascending or descending order, is utmost significant in optimization modeling, decisionmaking and also in ranking fuzzy numbers. As a generalization of the fuzzy number, ranking is more difficult due to the fact that Trapezoidal Intuitionistic Fuzzy Number (TrIFNs) is a popular and specific intuitionistic fuzzy set, which is defined on the set of real number. So far, several methods to rank simple fuzzy numbers, have already been proposed by different researchers, there is a dearth of having effectual methods to rank Intuitionistic Fuzzy Numbers (IFNs) in the literature. The paper focusses on developing a new methodology for ranking TrIFNs based on the thought of a ratio of the value index to the ambiguity index. The superiority and effectiveness of the ranking method, proposed in this paper, is assessed by illustrating a criteria based numerical problem and also by comparing it with an existing similar method.
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