Energy systems, either based on electricity or natural gas, are omnipresent regardless of the financial status of a country or an area. Due to a variety of causes (such as depletion of resources, natural calamities or...
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Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more ...
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The labor market is constantly affected by global events and techno-logical advancements, leading to a need for the workforce to continuously ac-quire new skills. Technical and Vocational Education and Training (TVET)...
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In the recent years, computing systems and applications are ubiquitous, the security and privacy are components which cannot be ignored anymore. Even if security is not a functional component of applications the conce...
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The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distributi...
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This paper discusses various applications of fractals in neurosciences and presents a methodology for their investigation and modeling with appropriate software. It is presented how to use multifractal analysis for ch...
This paper discusses various applications of fractals in neurosciences and presents a methodology for their investigation and modeling with appropriate software. It is presented how to use multifractal analysis for characterizing neural signals, with an emphasis on qualia fractality highlighted by the mechanisms proposed for its evaluation.
Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and ...
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Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.
The paper presents a solution for an irrigation controller based on the fuzzy-logic methodology. First, it describes the general problem of irrigation. Then, it discusses the physical control model. The precious irrig...
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Timely pest detection and identification is critical as part of modern agriculture. Halyomorpha Halys is a prevalent pest with proven harmful impacts on numerous crops and agricultural regions. The paper proposes an e...
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The education world has moved from analogue mediums to digital. This offers teachers the opportunity to take advantage of tools and features that can decrease the load. By using new technologies, we increase the avail...
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