Recent breakthroughs in video models have achieved remarkable success by integrating vision transformers into the video domain through adaptation. However, prevalent approaches in existing literature often entail a si...
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The ontological aspects of designing the efficient control systems of technological objects, which are operating in uncertain environment have been demonstrated in the research work. Design and monitoring of the contr...
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Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated co...
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Handwritten signature verification (HSV) models are notably recognized for their ability to discern whether a signature is forged in an offline document. Recently, HSV technology has made significant develop...
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The choice of systems for level measurements determined by the properties of the fluid, the design features of the tank, the type of level reading and other factors. This article discusses the results of the experimen...
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Deep neural networks (DNNs) are widely used in fields like computer vision and natural language processing. A key component of DNN training is the optimizer. SGD-Momentum is popular in many DNN methodologies, such as ...
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To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision a...
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We examine semantic patching, an important technique of software evolution, that allows us to automate to a large extent the adaption of external open-source libraries for Digital Twins with a high degree of correctne...
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ISBN:
(数字)9798350394085
ISBN:
(纸本)9798350394092
We examine semantic patching, an important technique of software evolution, that allows us to automate to a large extent the adaption of external open-source libraries for Digital Twins with a high degree of correctness. While this technique is widely known in specialized software engineering circles, we aim to popularize it among the automation community. We evaluate and compare its advantages against other adaptation methods in three practical use cases where the widely-used aas-core Software Development Kit is adapted for different lighthouse projects in Industrie 4.0, written in Python, C#, and Java, respectively. The results highlight the effectiveness of semantic patching in automating the process, minimizing errors and ensuring a seamless transition.
An intelligent livestock monitoring system has become increasingly popular for monitoring and managing animals in smart farms. Various technologies have been developed to localize the animals with wearable sensors. Th...
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As a necessary process of modern drug development,finding a drug compound that can selectively bind to a specific protein is highly challenging and *** drug‐target interaction strength in terms of drug‐target affini...
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As a necessary process of modern drug development,finding a drug compound that can selectively bind to a specific protein is highly challenging and *** drug‐target interaction strength in terms of drug‐target affinity(DTA)is an emerging and effective research approach for drug ***,it is challenging to model drug‐target interactions in a deep learning manner,and few studies provide interpretable analysis of *** paper proposes a DTA prediction method(mutual transformer‐drug target affinity[MT‐DTA])with interactive learning and an autoencoder *** proposed MT‐DTA builds a variational autoencoders system with a cascade structure of the attention model and convolutional neural *** not only enhances the ability to capture the characteristic information of a single molecular sequence but also establishes the characteristic expression relationship for each substructure in a single molecular *** this basis,a molecular information interaction module is constructed,which adds information interaction paths between molecular sequence pairs and complements the expression of correlations between molecular *** performance of the proposed model was verified on two public benchmark datasets,KIBA and Davis,and the results confirm that the proposed model structure is effective in predicting ***,attention transformer models with different configurations can improve the feature expression of drug/protein *** model performs better in correctly predicting interaction strengths compared with state‐of‐the‐art *** addition,the diversity of drug/protein molecules can be better expressed than existing methods such as SeqGAN and Co‐VAE to generate more effective new *** DTA value prediction module fuses the drug‐target pair interaction information to output the predicted value of ***,this paper theoretically proves that the proposed method maximises evidence lower bound
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