The emergence of single-cell multi-omics sequencing technology has enabled the simultaneous profiling of diverse omics data within individual cells. It offers a more comprehensive perspective on cellular phenotypes an...
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With refinement of power equipment, the identification and classification of its operation states is quite important for the stable operation of the system. As the power equipment states are rather complicated, it is ...
With refinement of power equipment, the identification and classification of its operation states is quite important for the stable operation of the system. As the power equipment states are rather complicated, it is difficult to realize general classification due to the limitation of data types and model characteristics when using deep learning. In the light of this, based on the time series data of various power equipment, an equipment state classification method is proposed by combining Recurrent Neural Network (RNN), Long short-term memory (LSTM) and attention mechanism. Firstly, RNN is used as the bottom frame network, and the LSTM core network integrating attention mechanism is added to construct the complex Feature Extraction Module (FEM). Secondly, main feature extraction is implemented in the LSTM network integrated with attention mechanism. Then, the extracted features are input into the complex FEM model and trained to match the evaluation state and obtain the evaluation results. In this paper, confusion matrix, F 1micro and accuracy are used as evaluation indexes. Three cases were selected to verify the method and five groups of comparison models were set for each case. Results show that the proposed method has good accuracy in power equipment state classification and also has strong generalization ability in other data classification.
To achieve coordinated control of power among sources, grid, loads, and storage within the system, a hybrid AC/DC microgrid based on power electronic transformers was constructed in this study. A coordinated control s...
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Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for *** the case of the online shopping business,it’s very important to find tren...
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Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for *** the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the *** this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one *** main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the *** permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the *** performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC *** model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree ***,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying *** research also reveals the importance of page value as a key feature for capturing online purchasing *** may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.
Point-of-interest (POI) group recommendation aims to recommend POIs that can satisfy a group of users through aggregating individual preferences. Recently, POI group recommendation has become a hot topic and some rese...
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Programmable logic controllers(PLCs)play a critical role in many industrial control systems,yet face increasingly serious cyber *** this paper,we propose a novel PLC-compatible software-based defense mechanism,called ...
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Programmable logic controllers(PLCs)play a critical role in many industrial control systems,yet face increasingly serious cyber *** this paper,we propose a novel PLC-compatible software-based defense mechanism,called Heterogeneous Redundant Proactive Defense Framework(HRPDF).We propose a heterogeneous PLC architecture in HRPDF,including multiple heterogeneous,equivalent,and synchronous runtimes,which can thwart multiple types of attacks against PLC without the need of external *** ensure the availability of PLC,we also design an inter-process communication algorithm that minimizes the overhead of *** implement a prototype system of HRPDF and test it in a real-world PLC and an OpenPLC-based device,*** results show that HRPDF can defend against multiple types of attacks with 10.22%additional CPU and 5.56%additional memory overhead,and about 0.6 ms additional time overhead.
Brain tumours may cause neurologic impairment, cognitive and mental disorders, elevated intracranial pressure and convulsions, which may be associated with a serious health hazard. The You Only Look Once (YOLO) system...
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ISBN:
(纸本)9798400713446
Brain tumours may cause neurologic impairment, cognitive and mental disorders, elevated intracranial pressure and convulsions, which may be associated with a serious health hazard. The You Only Look Once (YOLO) system has been demonstrated to be highly accurate when it comes to detecting objects in medical imaging. A new SCC-YOLO structure is proposed, which combines SCConv with YOLOv9. The SCConv module improves the performance of the convolutional neural network, which improves the performance of the system. In this paper, we investigate the impact of YOLOv9 on the Br35H dataset (Brain_Tumor_Dataset) Results indicate that SCC-YOLO improved mAP50 by 0.3% on the Br35H dataset and by 0.5% on our custom dataset compared to YOLOv9. SCC-YOLO achieves state-of-the-art performance in brain tumor detection.
Over the past decade, the rapid advancement of deep learning and big data applications has been driven by vast datasets and high-performance computing systems. However, as we approach the physical limits of semiconduc...
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Federated learning is a promising paradigm that utilizes widely distributed devices to jointly train a machine learning model while maintaining privacy. However, when oriented to distributed resource-constrained edge ...
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The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor *** article is dedicated to review the most recent and interesting smartphones-based indoor navigation syste...
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The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor *** article is dedicated to review the most recent and interesting smartphones-based indoor navigation systems,ranging from electromagnetic to inertia to visible light ones,with an emphasis on their unique challenges and potential realworld applications.A taxonomy of smartphone sensors will be introduced,which serves as the basis to categorise different positioning systems for reviewing.A set of criteria to be used for the evaluation purpose will be *** each sensor category,the most recent,interesting,and practical systems will be examined,with detailed discussion on the open research questions for the academics,and the practicality for the potential clients.
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