Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify t...
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This review studies recent developments towards the physical design and control of self-assembling multi-robot systems. A wide range of novel robotic systems have been developed lately, for potential applications in t...
This review studies recent developments towards the physical design and control of self-assembling multi-robot systems. A wide range of novel robotic systems have been developed lately, for potential applications in terrestrial, aquatic, and aerospace environments. They increasingly make use of connectors which enable modules to join with each other at arbitrary points instead of discrete locations. Although the majority of contemporary algorithms are shape-driven, an increased focus on task-driven algorithms is observed. Self-assembling multi-robot systems allow the same set of robots to adopt specific morphologies for different tasks. The requirements for robots to be able to connect to each other, locomote, and communicate have led to a wide range of physical designs realising different trade-offs. While algorithms are validated extensively in simulation, only a small portion are yet tested on real robotic platforms. Future research should investigate the real-world application of these systems, possibly aided by the introduction of standardised and open hardware.
The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux ...
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This paper aims at developing new techniques that study vital functions of the heart by analysing biological signals. ECG prediction is important for many current medical applications. Currently, there are many machin...
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This work proposes a novel solution to the problem of covering a bounded grid world using a swarm of robotic agents. The controller requires no run-time memory and only few, discrete sensory inputs. Two variants of th...
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Simultaneous localisation and mapping (SLAM) relies on low-cost on-board sensors such as cameras and inertial measurement units. It is crucial that the surroundings are visible to the cameras to maximise the accuracy ...
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This paper addresses the multi-robot barrier coverage problem. It presents a group of memory-less robots that encircle a group of herd agents, by moving along a polygonal barrier. The results, produced from simulation...
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The present paper introduces a mathematical model for the cross-talking between microRNA and Protein. Studying the qualitative properties of the proposed model, we infer that the microRNA is an inhibitor for the Prote...
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The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the par...
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ISBN:
(数字)9798350366778
ISBN:
(纸本)9798350366785
The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the paradigms of quantum machine learning, but the training of quantum kernel is time consuming. Therefore, this work makes the first attempt to introduce a consensus-based distributed approach to quantum kernel learning - named CDQKL - that only requires to exchange model parameter information between adjacent nodes while avoiding the need of sharing local training data. Through comparative experimental studies, the advantages of CDQKL in classification accuracy and convergence speed are verified. Considering the popularization of quantum computing cloud service and miniaturization of quantum terminals, the CDQKL adapting to this trend is able to play a vital role in data security, which implies the far-reaching significance of this work. Our code is available at https://***/Leisurivan/CDOKL.
The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain;with proper path planning,it can also minimize the potential dam...
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The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain;with proper path planning,it can also minimize the potential damage by setting constraints and optimizing the insertion ***,reinforcement learning(RL)-based path planning algorithm has shown promising results in neurosurgery,but because of the trial and error mechanism,it can be computationally expensive and insecure with low training *** this paper,we propose a heuristically accelerated deep Q network(DQN)algorithm to safely preoperatively plan a needle insertion path in a neurosurgical ***,a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL *** are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN *** showed promising results of our algorithm in saving over 50 training episodes,calculating path lengths of 0.35 after normalization,which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm,***,the maximum curvature during planning is reduced to 0.046 from 0.139 mm−1 using the proposed algorithm compared to DQN.
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