Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend ...
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作者:
Grzegorz BaronUrszula StańczykDepartment of Graphics
Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland
Cross-validation is a popularly used approach to evaluation of performance for classifiers. It relies on random selection of independent samples for training and testing, and assumes that if any similarities among sam...
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Cross-validation is a popularly used approach to evaluation of performance for classifiers. It relies on random selection of independent samples for training and testing, and assumes that if any similarities among samples exist, they do not lead to known grouping of datapoints in the input space. If these conditions are violated, as it may happen for datasets with some structure of samples included, standard cross-validation can return biased results even for many folds. In the paper the research on cross-validation was reported for application to stylometric datasets, describing a task of authorship attribution. The comparison of standard and non-standard processing was presented. In the latter case, selected subsets of examples were swapped over between training and test sets several times. The experiments with three popular classifiers showed that standard cross-validation tended to give over-optimistic results, whereas non-standard processing was more guarded, and by that more reliable. To avoid high computational costs involved, evaluation based on averaged predictions for limited numbers of test sets can be considered as a reasonable compromise.
作者:
Urszula StańczykDepartment of Graphics
Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland
Estimation of attribute importance can be obtained by a mechanism that allows to assign some weight to variables. Weighting attributes can lead to their ordering, which, in turn, can be exploited for feature selection...
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Estimation of attribute importance can be obtained by a mechanism that allows to assign some weight to variables. Weighting attributes can lead to their ordering, which, in turn, can be exploited for feature selection and reduction. Decision reducts constitute an example of a mechanism aiming at dimensionality reduction, embedded in rough set approach to data mining. The paper presents research works, where the process of weighting was driven by the proposed factor based on reducts, with varying their sets, and the results were analysed through the perspective of reduct cardinality, since it is typically considered as the most significant indicator of reduct quality. Constructed rankings of variables were used for inferring sets of decision rules from gradually decreasing numbers of features, and then the performance of the rule classifiers was tested. The experiments show that for the weighting factor to be useful for feature reduction, not only reduct cardinalities, but also the numbers of reducts found need to be taken into account.
Assuming agents possess sensing capabilities and dynamics relative to their body coordinate frames, this paper addresses a displacement-based strategy for discrete-time formation control through attitude synchronizati...
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Lithium-ion batteries are widely applied in sustainable energy conversion system. Consequently, it is of great research significance to accurately estimate the state of health (SOH) of batteries. To effectively model ...
Lithium-ion batteries are widely applied in sustainable energy conversion system. Consequently, it is of great research significance to accurately estimate the state of health (SOH) of batteries. To effectively model the input features at the spatial level, this article proposes a self-attention graph pooling convolutional network (SAGPCN) to estimate the SOH. The advantages of SAGPCN proposed in this paper can be reflected as follows: (1) The SAGPCN can consider node characteristics and graph topology, which focuses the attention on key parts of the graph. (2) The SAGPCN designs a self-attention mechanism to reserve significant nodes and delete secondary nodes, so as to optimize the network structure. A real-world dataset is adopted to evaluate the proposed battery SOH estimation approach in this paper. Experimental results represent that the estimation performance of the proposed SAGPCN is better than some data-driven SOH prediction approaches.
An existing challenge in power systems is the implementation of optimal demand management through dynamic pricing. This paper encompasses the design, analysis and implementation of a novel on-line pricing scheme based...
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In this paper, the problems related to cooperative control for the multiple mobile robot system (MMRS) is presented. The LIDAR sensor is employed to obtain the 2D map of the indoor space. The formation control and the...
In this paper, the problems related to cooperative control for the multiple mobile robot system (MMRS) is presented. The LIDAR sensor is employed to obtain the 2D map of the indoor space. The formation control and the leader-following algorithm are applied to control the MMRS moving in a specific formation. Additionally, this paper considers the scenario that the MMRS has to move to several targets in the indoor space with many obstacles. Therefore, the indoor travelling salesman problem-based Ant Colony Optimization (ACO) is studied in this work to determine the optimal moving path of the MMRS. To prove the effectiveness and merit of the proposed methods, the simulation results are provided in this article.
The micro RNA-messenger RNA dynamics is a topic of constant interest. In this paper we analyze the stability of equilibria of a mathematical model associated to this interaction. After proving the existence and unique...
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Negative pressure has been utilized in medical practice, exemplified by methods like cupping therapy. This study aimed to determine the effect of pressure and time duration of cupping therapy on the stratum corneum (S...
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ISBN:
(数字)9798350386844
ISBN:
(纸本)9798350386851
Negative pressure has been utilized in medical practice, exemplified by methods like cupping therapy. This study aimed to determine the effect of pressure and time duration of cupping therapy on the stratum corneum (SC) and deeper epidermis layer (ED) thickness. Three volunteer participants with healthy palms and no scars or wounds were allocated. Optical Coherence Tomography (OCT) records the thickness of skin layers using 105 mmHg and 145 mmHg negative pressure with 5 and 10 minutes duration. We analyzed the paired T-test for the SC thickness changes before and after cupping therapy. We found that there is a significant increase in SC with negative pressure 105 mmHg and 5 minutes duration (0.032 ± 0.005 to 0.036 ± 0.005, P < 0.05), and with negative pressure 145 mmHg and 10 minutes duration (0.037 ± 0.005 to 0.038 ± 0.005, P < 0.05). This indicates that cupping therapy has a discernible impact on the thickness of SC.
The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, th...
The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target.
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