We study the problem of regret minimization in a multi-armed bandit setup where the agent is allowed to play multiple arms at each round by spreading the resources usually allocated to only one arm. At each iteration ...
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This paper showcases a real-world example of a system that achieves collaborative localization and mapping of multiple agents within a building. The proposed system processes the odometry and 3D point cloud data colle...
This paper showcases a real-world example of a system that achieves collaborative localization and mapping of multiple agents within a building. The proposed system processes the odometry and 3D point cloud data collected by the agents moving around the building to automatically generate the building’s floorplan on which the agent trajectories are overlaid. The wearable hardware consists of a low-cost passive integrated sensor that includes both a camera and an IMU (Inertial Measurement Unit) and an embedded compute unit. The system’s capabilities are shown through real-world experiments.
Edge computing paradigm enables moving Internet of Things (IoT) applications from the Cloud to the edge of the network. Modern software engineering approaches are adhering to microservices to enable the deployment of ...
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Rotating machinery is an integral part of many industrial systems. Domain adaptation technique provides a powerful tool to detect faults under different working conditions. However, there is still a challenge: convent...
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Rotating machinery is an integral part of many industrial systems. Domain adaptation technique provides a powerful tool to detect faults under different working conditions. However, there is still a challenge: conventional domain adaptation approach only works under the ‘closed set’ assumption that all test classes are known at training time. In practice, a more realistic situation is ‘open set’, i.e., knowledge is incomplete in the training process, resulting in unknown classes during the testing. In this paper, a sparse autoencoder based adversarial open set domain adaptation (SAOSDA) model is proposed for rotating machinery fault diagnosis under open set scenarios, which can recognize the unknown faults and detect the known faults under different working conditions. This model utilizes adversarial learning to reduce the discrepancies between source samples and known target samples and reject the unknown target samples simultaneously. Experimental results of the actual bearing dataset verify the superiority and effectiveness of this method.
Adaptive control architectures often make use of Lyapunov functions to design adaptive laws. We are specifically interested in adaptive control methods, such as the well-known L1 adaptive architecture, which employ a ...
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Aiming at a large number of ambiguous,imprecise and incomplete data in the real world,fuzzy time series has come into being and developed into an effective forecasting *** the process of modeling and forecasting of fu...
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Aiming at a large number of ambiguous,imprecise and incomplete data in the real world,fuzzy time series has come into being and developed into an effective forecasting *** the process of modeling and forecasting of fuzzy time series,the prediction performance of fuzzy time series can be effectively improved by partitioning the universe of discourse into different *** this paper,a forecasting approach for fuzzy time series,which introduces the granularity mechanism into interval division and employs differential data for incremental forecasting,is proposed to solve the problem of time series forecasting with high forecasting *** the proposed approach,in order to describe the fuzzy logic relationship and fuzzy trend of historical data,we first do differential processing on the historical ***,Fuzzy C-means(FCM) clustering algorithm is used to generate several partition intervals *** the sequel,we use the principle of justifiable granularity to constantly adjust the width of all the intervals,so that these information granules associated with corresponding intervals become the most"informative" information ***,the boundary of information granules is used as the basis of interval division to complete the forecasting *** illustrative example is provided to demonstrate the essence of the proposed *** comparative experiment with other representative approaches shows that the proposed approach can significantly improve the prediction accuracy of time series.
This paper investigates the high-precision path tracking control of tracked paver combined with global satellite navigation *** the paver is performing paving operations,it requires high path tracking accuracy and goo...
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This paper investigates the high-precision path tracking control of tracked paver combined with global satellite navigation *** the paver is performing paving operations,it requires high path tracking accuracy and good vehicle ***,considering the influence of road curvature on path tracking accuracy and vehicle stability,and the situation that the vehicle can not move quickly to the expectation path when the lateral position of the vehicle deviates from the expectation path,this paper proposes a lateral path tracking control method based on improved Pure Pursuit *** control method is verified through *** experimental results show that the maximum lateral tracking error of the improved algorithm is 0.04 m,which is 55.56% lower than that of the original algorithm,and the average lateral tracking error is 0.02 m,which is60% lower than that of the original *** purpose of high-precision path tracking of the paver is realized.
Aiming at the problem of how to realize the reasonable distribution of goods and the resource scheduling of route planning under dynamic conditions, a phased refresh method is proposed to deal with the requirements in...
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Aiming at the problem of how to realize the reasonable distribution of goods and the resource scheduling of route planning under dynamic conditions, a phased refresh method is proposed to deal with the requirements in sections. At the same time, the clustering method is introduced to generate the initial population. And the cross and mutation rate is determined by a step-by-step reduction method, which is used to solve the problem of the traditional genetic algorithm falling into the local optimum caused by the uneven distribution of searchable solutions. Finally, the effectiveness of the improved method is verified by simulation experiments.
In the modern manufacturing environment, the production systems develop towards a flexible direction due to the market demands of multiple varieties and small batch-based customized products. To make better use of the...
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In the modern manufacturing environment, the production systems develop towards a flexible direction due to the market demands of multiple varieties and small batch-based customized products. To make better use of the existing resources and to improve production efficiency, distributed manufacturing mode begins to appear. This paper concentrates on a multi-type production scheduling problem for distributed serial lines. Specifically, unreliable machines and finite buffers are considered,which are more realistic in the practical production. The mathematical model for the production system with two Bernoulli machines is formulated first. Then, under the hypothetical model mentioned above, the main work of this article is to propose the exact and approximate analyses to calculate the performance indicators. Based on the analysis of the model, the genetic algorithm is applied to optimize the maximum completion time on the distributed serial lines for multi-batch-based production tasks. Finally, the feasibility and effectiveness of the mathematical model and the proposed algorithm are verified through numerical experiments.
The Koopman operator framework allows to embed a nonlinear system into a linear one. This enables the analysis, estimation, and control of nonlinear dynamics with linear methods. controllers based on the Koopman opera...
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The Koopman operator framework allows to embed a nonlinear system into a linear one. This enables the analysis, estimation, and control of nonlinear dynamics with linear methods. controllers based on the Koopman operator (KO) are often model predictive control (MPC) schemes. The performance of an MPC depends on the prediction accuracy of its model. Hence, it is meaningful to update the model online if the predictions are not sufficiently accurate. In this work, we approach this problem by using a recursive least squares (RLS) algorithm with forgetting factor. Furthermore, we show in an empirical case study that combining the KO with an online update and the recently proposed quasi-linear parameter-varying model predictive control (qLMPC) algorithm results in an efficient control scheme.
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