One of the most popular methods for reducing the complexity of assemblies of finite element models in the field of structural dynamics is component mode synthesis. A main challenge of component mode synthesis is balan...
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Magnetic-field simultaneous localization and mapping (SLAM) using consumer-grade inertial and magnetometer sensors offers a scalable, cost-effective solution for indoor localization. However, the rapid error accumulat...
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With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services...
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Open source software for robot audition called HARK aims to make “OpenCV” in audio signal processing, providing comprehensive functions from multichannel audio input to sound localization, sound source separation, a...
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Open source software for robot audition called HARK aims to make “OpenCV” in audio signal processing, providing comprehensive functions from multichannel audio input to sound localization, sound source separation, and au-tomatic speech recognition. Since each of these HARK modules takes considerable energy when executed on PC, we propose to implement each module on an FPGA board called M-KUBOS connected. Here, we focus on the most computationally expensive function of HARK; the sound source separation, and implement it on a Zynq Ultrascale+ board. More than twice a performance improvement was achieved by using the sound frequency level parallelization in the HLS description compared to the software execution on the Ryzen 3990X64-core server. Power evaluation of the real board showed that the energy consumption is only 1/23.4 of the server.
Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively lear...
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This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optim...
In this paper the exogenous control of gene reg-ulatory networks is investigated through the semi-discretized partial integro-differential equation (PIDE) describing the time-evolution of the network's probability...
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ISBN:
(数字)9783907144107
ISBN:
(纸本)9798331540920
In this paper the exogenous control of gene reg-ulatory networks is investigated through the semi-discretized partial integro-differential equation (PIDE) describing the time-evolution of the network's probability density function. With an appropriate finite volume method the semi-discretized system is a mass-conservative linear compartmental model, and thus it preserves most qualitative properties of the solution of the PIDE, namely, it is nonnegative and mass conservative. These advantages combined with the newly investigated mesh-invariance of control allows us to efficiently determine the reachability set. The possibilities of this framework are demon-strated through an illustrative example from literature.
We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS) for computing operator fixed points, with applications to asynchronous optimization. DEGAS includes novel asynchronous variants of ADMM and ...
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Stock deviation of casted parts needs to be handled by adapting the CNC machining code to every new batch. Usually, human experts deal with this workpiece referencing task, but the demand for automation is expressed b...
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Stock deviation of casted parts needs to be handled by adapting the CNC machining code to every new batch. Usually, human experts deal with this workpiece referencing task, but the demand for automation is expressed by the industry. This paper introduces a Digital Twin (DT) supported workpiece referencing method, implemented in the following steps: building the DT of the CNC machining cell, loading measurements of the casted part into the DT, solving the workpiece referencing problem as a convex optimization problem, and generating the compensated CNC code. The proposed approach is illustrated in a case study from the automotive industry.
We study community detection based on state observations from gossip opinion dynamics over stochastic block models (SBM). It is assumed that a network is generated from a two-community SBM where each agent has a commu...
We study community detection based on state observations from gossip opinion dynamics over stochastic block models (SBM). It is assumed that a network is generated from a two-community SBM where each agent has a community label and each edge exists with probability depending on its endpoints' labels. A gossip process then evolves over the sampled network. We propose two algorithms to detect the communities out of a single trajectory of the process. It is shown that, when the influence of stubborn agents is small and the link probability within communities is large, an algorithm based on clustering transient agent states can achieve almost exact recovery of the communities. That is, the algorithm can recover all but a vanishing part of community labels with high probability. In contrast, when the influence of stubborn agents is large, another algorithm based on clustering time average of agent states can achieve almost exact recovery. Numerical experiments are given for illustration of the two algorithms and the theoretical results of the paper.
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