Viscous liquid-gas two-phase flow experiments with liquid viscosity ranging from 1000 - 5000 cP (1.0 - 5.0 Pa.s) and air as the gas phase were compared with the point model simulations obtained from OLGA, a multiphase...
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Body Area Sensor Networks (BASNs) are powered by batteries and hence energy is a scarce resource. Thus communication protocols in BASN need to be energy efficient. This paper addresses this problem by proposing a Mini...
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ISBN:
(纸本)9781424451753
Body Area Sensor Networks (BASNs) are powered by batteries and hence energy is a scarce resource. Thus communication protocols in BASN need to be energy efficient. This paper addresses this problem by proposing a Minimum Energy Packet Forwarding Protocol (MEPF). First, we analyze the trade-off of energy consumption between lower transmission power and packet retransmission. Then we propose the transmission power control part of MEPF. It transmits a packet using the minimum transmission power that can guarantee a high packet reception rate. Unlike the previous work, we not only need to adjust the power of the forward transmission but also the ACKs, which further reduces the consumption. MEPF outstands from the earlier works by also retransmitting lost packets when a link is good enough. The quality of the link is dynamically judged by a machine learning algorithm which also considers the limited buffer space in a sensor node. By MEPF, the energy consumption to forward a packet is minimized without sacrificing the packet reception ratio. Experimental results show quantitatively that MEPF save significant amounts of energy.
Reliability and low power consumption are important design metrics of any critical embedded systems. With the advancements of fabrication technology reaching to the nano levels and complexity of system is increasing, ...
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Contract Net Protocol (CNP) is probably the most widely used task allocation protocol in distributed multi-agent systems (MAS). However it is limited in some issues and has serious drawbacks if it is applied in such r...
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ISBN:
(纸本)9781479939329
Contract Net Protocol (CNP) is probably the most widely used task allocation protocol in distributed multi-agent systems (MAS). However it is limited in some issues and has serious drawbacks if it is applied in such real world applications where temporal interaction aspects are of great importance and fault-tolerance is a crucial issue. Many researchers have proposed various methods to expand and to improve it but those challenges have not been much addressed. To cope with these limitations, this paper proposes a formal model that extends the conventional contract net with real time constraints, often defined as interaction duration and message deadlines, and fault tolerance to handle the agent death exception. In this study we concentrate on the reliability of the awarded contractor which may die while carrying out the assigned task. In the proposed approach a timeout mechanism is modeled to detect the crash failure of the contractor;hence a proper termination of the negotiation process can be timely performed by the manager ensuring the failure recovery. We model the extended CNP with timed colored Petri nets and show that it terminates correctly either in a safety case or in a failure situation. The model analysis by means of CPN tools proves that the protocol meets the key properties namely model correctness, deadline respect, absence of deadlocks and livelocks, absence of dead code, agent terminal states consistency, concurrency and validity.
Neutrino physics is a forefront topic of today's research. Large detectors installed underground study neutrino properties using neutrino beams from muons decaying in flight. DAE?ALUS looks at neutrinos from stopp...
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To substantially enhance robot intelligence, there is a pressing need to develop a large model that enables general-purpose robots to proficiently undertake a broad spectrum of manipulation tasks, akin to the versatil...
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ISBN:
(纸本)9798350377712;9798350377705
To substantially enhance robot intelligence, there is a pressing need to develop a large model that enables general-purpose robots to proficiently undertake a broad spectrum of manipulation tasks, akin to the versatile task-planning ability exhibited by LLMs. The vast diversity in objects, robots, and manipulation tasks presents huge challenges. Our work introduces a comprehensive framework to develop a foundation model for general robotic manipulation that formalizes a manipulation task as contact synthesis. Specifically, our model takes as input object and robot manipulator point clouds, object physical attributes, target motions, and manipulation region masks. It outputs contact points on the object and associated contact forces or post-contact motions for robots to achieve the desired manipulation task. We perform extensive experiments both in the simulation and real-world settings, manipulating articulated rigid objects, rigid objects, and deformable objects that vary in dimensionality, ranging from one-dimensional objects like ropes to two-dimensional objects like cloth and extending to three-dimensional objects such as plasticine. Our model achieves average success rates of around 90%. Supplementary materials and videos are available on our project website at https://***/.
We derive a new algorithm for particle flow with nonzero diffusion corresponding to Bayes' rule, and we report the results of Monte Carlo simulations which show that the new filter is an order of magnitude more ac...
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ISBN:
(纸本)9786058631113
We derive a new algorithm for particle flow with nonzero diffusion corresponding to Bayes' rule, and we report the results of Monte Carlo simulations which show that the new filter is an order of magnitude more accurate than the extended Kalman filter for a difficult nonlinear filter problem. Our new algorithm is simple and fast to compute, and it has an especially nice intuitive formula, which is the same as Newton's method to solve the maximum likelihood estimation (MLE) problem (but for each particle rather than only the MLE), and it is also the same as the extended Kalman filter for the special case of Gaussian densities (but for each particle rather than just the point estimate). All of these particle flows apply to arbitrary multimodal densities with smooth nowhere vanishing nonGaussian densities.
This paper describes a new cognitive model that could be used to build a system that can intelligently realise concepts for itself and then reason over them. The main model has been published previously, but this pape...
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ISBN:
(纸本)9780769538884
This paper describes a new cognitive model that could be used to build a system that can intelligently realise concepts for itself and then reason over them. The main model has been published previously, but this paper proposes a new finer level of processing that would allow the system to learn arbitrarily complex concepts for itself. These can then be clustered into chains that represent higher level concepts and reasoned over. These chains can also trigger each other to generate a certain level of 'thinking'. This model would be suitable for a neural-like system, but also a large distributed network.
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