Realtime analyzing the feeding behavior of fish is the premise and key to accurate guidance on *** identification of fish behavior using a single information is susceptible to various *** overcome the problems,this pa...
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Realtime analyzing the feeding behavior of fish is the premise and key to accurate guidance on *** identification of fish behavior using a single information is susceptible to various *** overcome the problems,this paper proposes an adaptive deep modular co-attention unified multi-modal transformers(DMCA-UMT).By fusing the video,audio and water quality parameters,the whole process of fish feeding behavior could be ***,for the input video,audio and water quality parameter information,features are extracted to obtain feature vectors of different ***,deep modular co-attention(DMCA)is introduced on the basis of the original cross-modal encoder,and the adaptive learnable weights are *** feature vector of video and audio joint representation is obtained by automatic learning based on fusion ***,the information of visual-audio modality fusion and text features are used to generate clip-level moment *** query decoder decodes the input features and uses the prediction head to obtain the final joint moment retrieval,which is the start-end time of feeding the *** results show that the mAP Avg of the proposed algorithm reaches 75.3%,which is37.8%higher than that of unified multi-modal transformers(UMT)algorithm.
Atmospheric data assimilation is essential for numerical weather prediction. Ensemble data assimilation connects multiple instances of an atmospheric model through a Kalman filter-based algorithm, which is regarded as...
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Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produce...
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Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.
Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity...
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Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years *** information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the *** are also significant security risks,concerns,and difficulties associated with cloud *** address these challenges,we propose merging BC and SDN into a cloud computing platform for the *** paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT *** proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and *** in the industrial sector benefit from the dispersed or decentralized,and efficient environment of ***,we described an SDN method to improve the durability,stability,and load balancing of cloud *** efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.
Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multip...
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The right lane and platoon assignment to a vehicle significantly impacts achieving the desired driving goals to improve occupants’ safety or reduce pollution. Preparing an intelligent assistant to address this issue ...
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Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring loca...
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This paper proposes a composite design of fuzzy adaptive control scheme based on TMD RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in LMI then ...
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Maritime transportation,a cornerstone of global trade,faces increasing safety challenges due to growing sea traffic *** study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identificatio...
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Maritime transportation,a cornerstone of global trade,faces increasing safety challenges due to growing sea traffic *** study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System(AIS)data and advanced deep learning models,including Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Bidirectional LSTM(DBLSTM),Simple Recurrent Neural Network(SimpleRNN),and Kalman *** research implemented rigorous AIS data preprocessing,encompassing record deduplication,noise elimination,stationary simplification,and removal of insignificant *** were trained using key navigational parameters:latitude,longitude,speed,and *** aware processing through trajectory segmentation and topological data analysis(TDA)was employed to capture dynamic *** using a three-month AIS dataset demonstrated significant improvements in prediction *** GRU model exhibited superior performance,achieving training losses of 0.0020(Mean Squared Error,MSE)and 0.0334(Mean Absolute Error,MAE),with validation losses of 0.0708(MSE)and 0.1720(MAE).The LSTM model showed comparable efficacy,with training losses of 0.0011(MSE)and 0.0258(MAE),and validation losses of 0.2290(MSE)and 0.2652(MAE).Both models demonstrated reductions in training and validation losses,measured by MAE,MSE,Average Displacement Error(ADE),and Final Displacement Error(FDE).This research underscores the potential of advanced deep learning models in enhancing maritime safety through more accurate trajectory predictions,contributing significantly to the development of robust,intelligent navigation systems for the maritime industry.
Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains...
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