The energy transition represents an essential process necessary to drive a shift towards a sustainable and balanced human-environment interaction. Within the residential building sector, this obj ective can be pursued...
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Surface roughness is considered to be a critical parameter when evaluating the machinability of a material, as well as the surface quality of a machined part. Thus, a number of studies exist in the literature, that de...
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For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pa...
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For a 5G wireless communication system,a convolutional deep neural network(CNN)is employed to synthesize a robust channel state estimator(CSE).The proposed CSE extracts channel information from transmit-and-receive pairs through offline training to estimate the channel state ***,it utilizes pilots to offer more helpful information about the communication *** proposedCNN-CSE performance is compared with previously published results for Bidirectional/long short-term memory(BiLSTM/LSTM)NNs-based *** CNN-CSE achieves outstanding performance using sufficient pilots only and loses its functionality at limited pilots compared with BiLSTM and LSTM-based *** three different loss function-based classification layers and the Adam optimization algorithm,a comparative study was conducted to assess the performance of the presented DNNs-based *** BiLSTM-CSE outperforms LSTM,CNN,conventional least squares(LS),and minimum mean square error(MMSE)*** addition,the computational and learning time complexities for DNN-CSEs are *** estimators are promising for 5G and future communication systems because they can analyze large amounts of data,discover statistical dependencies,learn correlations between features,and generalize the gotten knowledge.
This study aims to investigate whether or not just-in-time (JIT) and supply chain finance (SCF) have a synergistic impact on overall organisation performance. The purpose of this study is to help address this knowledg...
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Communication is a critical process that facilitates team members’ coordination, expertise exchange, decision-making, and, consequently, team effectiveness. A focus on effective communication is critical in virtual t...
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By describing images of tourist attractions from different angles and generating descriptions, it is expected to make them more easily searchable by users. In this study, a system, which automatically generates attrac...
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This study uses a conjoint analysis approach to determine the key factors influencing subscriber preferences for subscribing to Netflix plan. In a rapidly evolving industry, understanding the drivers of subscription c...
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As Knowledge management (KM) offers significant benefits for organizations, its implementation is crucial for them to make sure they take advantage of those benefits. However, risks and uncertainties are undeniable wh...
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Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to est...
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Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.
Artificial intelligence (AI) is emerging as an alternative solution in the healthcare sector, offering opportunities to enhance efficiency and optimize the utilization of precious resources. The nascent stage of AI ap...
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