Ocean underwater exploration is a part of oceanography that investigates the physical and biological conditions for scientific and commercial purposes. And video technology plays an important role and is extensively a...Ocean underwater exploration is a part of oceanography that investigates the physical and biological conditions for scientific and commercial purposes. And video technology plays an important role and is extensively applied for underwater environment observation. Different from the conventional methods, video technology explores the underwater ecosystem continuously and non-invasively. However, due to the scattering and attenuation of light transport in the water, complex noise distribution and lowlight condition cause challenges for underwater video applications including object detection and recognition. In this paper, we propose a new deep encoding-decoding convolutional architecture for underwater object recognition. It uses the deep encoding-decoding network for extracting the discriminative features from the noisy low-light underwater images. To create the deconvolutional layers for classification, we apply the deconvolution kernel with a matched feature map, instead of full connection, to solve the problem of dimension disaster and low accuracy. Moreover, we introduce data augmentation and transfer learning technologies to solve the problem of data starvation. For experiments, we investigated the public datasets with our proposed method and the state-of-the-art methods. The results show that our work achieves significant accuracy. This work provides new underwater technologies applied for ocean exploration.展开更多
In this paper, based on an adaptive chaos synchronization scheme, two methods of encoding-decoding message for secure communication are proposed. With the first method, message is directly added to the chaotic signal ...In this paper, based on an adaptive chaos synchronization scheme, two methods of encoding-decoding message for secure communication are proposed. With the first method, message is directly added to the chaotic signal with parameter uncertainty. In the second method, multi-parameter modulation is used to simultaneously transmit more than one digital message （i.e., the multichannel digital communication） through just a single signal, which switches among various chaotic attractors that differ only subtly. In theory, such a treatment increases the difficulty for the intruder to directly intercept the information, and meanwhile the implementation cost decreases significantly. In addition, numerical results show the methods are robust against weak noise, which implies their practicability.展开更多
Effective survival analysis is essential for identifying optimal preventive treatments within smart healthcare systems and leveraging digital health advancements;however,existing prediction models face limitations,pri...Effective survival analysis is essential for identifying optimal preventive treatments within smart healthcare systems and leveraging digital health advancements;however,existing prediction models face limitations,primarily relying on ensemble classification techniques with suboptimal performance in both target detection and predictive accuracy.To address these gaps,this paper proposes a multimodal framework that integrates enhanced facial feature detection and temporal predictive modeling.For facial feature extraction,this study developed a lightweight faceregion convolutional neural network(FRegNet)specialized in detecting key facial components,such as eyes and lips in clinical patients that incorporates a residual backbone(Rstem)to enhance feature representation and a facial path aggregated feature pyramid network for multi-resolution feature fusion;comparative experiments reveal that FReg-Net outperforms state-of-the-art target detection algorithms,achieving average precision(AP)of 0.922,average recall of 0.933,mean average precision(mAP)of 0.987,and precision of 0.98–significantly surpassing other mask region-based convolutional neural networks(RCNN)variants,such as mask RCNN-ResNeXt with AP of 0.789 and mAP of 0.957.Based on the extracted facial features and clinical physiological indicators,this study proposes an enhanced temporal encoding-decoding(ETED)model that integrates an adaptive attention mechanism and a gated weighting mechanism to improve predictive performance,with comparative results demonstrating that the ETED variant incorporating facial features(ETEncoding-Decoding-Face)outperforms traditional models,achieving an accuracy of 0.916,precision of 0.850,recall of 0.895,F1 of 0.884,and area under the curve(AUC)of 0.947–outperforming gradient boosting with an accuracy of 0.922,but AUC of 0.669,and other classifiers in comprehensive metrics.The results confirm that the multimodal dataset(facial features+physiological indicators)significantly enhances the prediction accuracy of the seven-day survival conditions of patients.Correlation analysis reveals that chronic health evaluation and mean arterial pressure are positively correlated with survival,while temperature,Glasgow Coma Scale,and fibrinogen are negatively correlated.展开更多
In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggreg...In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggregation U-shaped attention network structure of MAAUNet(MultiRes aggregation attention UNet)is proposed based on MultiResUNet.Firstly,aggregate connection is introduced from the original feature aggregation at the same level.Skip connection is redesigned to aggregate features of different semantic scales at the decoder subnet,and the problem of semantic gaps is further solved that may exist between skip connections.Secondly,after the multi-scale convolution module,a convolution block attention module is added to focus and integrate features in the two attention directions of channel and space to adaptively optimize the intermediate feature map.Finally,the original convolution block is improved.The convolution channels are expanded with a series convolution structure to complement each other and extract richer spatial features.Residual connections are retained and the convolution block is turned into a multi-channel convolution block.The model is made to extract multi-scale spatial features.The experimental results show that MAAUNet has strong competitiveness in challenging datasets,and shows good segmentation performance and stability in dealing with multi-scale input and noise interference.展开更多
With the expanding applications of multiple unmanned systems in various fields,more and more research attention has been paid to their security.The aim is to enhance the anti-interference ability,ensure their reliabil...With the expanding applications of multiple unmanned systems in various fields,more and more research attention has been paid to their security.The aim is to enhance the anti-interference ability,ensure their reliability and stability,and better serve human society.This article conducts adaptive cooperative secure tracking consensus of networked multiple unmanned systems subjected to false data injection attacks.From a practical perspective,each unmanned system is modeled using high-order unknown nonlinear discrete-time systems.To reduce the communication bandwidth between agents,a quantizer-based codec mechanism is constructed.This quantizer uses a uniform logarithmic quantizer,combining the advantages of both quantizers.Because the transmission information attached to the false data can affect the accuracy of the decoder,a new adaptive law is added to the decoder to overcome this difficulty.A distributed controller is devised in the backstepping framework.Rigorous mathematical analysis shows that our proposed control algorithms ensure that all signals of the resultant systems remain bounded.Finally,simulation examples reveal the practical utility of the theoretical analysis.展开更多
基金supported by the Jilin Science and Technology Development Plan Project (Nos. 20160209006GX, 20170309001GX and 20180201043GX)
文摘Ocean underwater exploration is a part of oceanography that investigates the physical and biological conditions for scientific and commercial purposes. And video technology plays an important role and is extensively applied for underwater environment observation. Different from the conventional methods, video technology explores the underwater ecosystem continuously and non-invasively. However, due to the scattering and attenuation of light transport in the water, complex noise distribution and lowlight condition cause challenges for underwater video applications including object detection and recognition. In this paper, we propose a new deep encoding-decoding convolutional architecture for underwater object recognition. It uses the deep encoding-decoding network for extracting the discriminative features from the noisy low-light underwater images. To create the deconvolutional layers for classification, we apply the deconvolution kernel with a matched feature map, instead of full connection, to solve the problem of dimension disaster and low accuracy. Moreover, we introduce data augmentation and transfer learning technologies to solve the problem of data starvation. For experiments, we investigated the public datasets with our proposed method and the state-of-the-art methods. The results show that our work achieves significant accuracy. This work provides new underwater technologies applied for ocean exploration.
基金Project supported by the National Natural Science Foundation of China （Grant No. 10572080）, Shanghai Rising-Star Program （Grant No.05QMX1422）, and Dawn Project of the Science Foundation of Shanghai Municipal Commission of Education （Grant No.05SG41 04YQHB089）
文摘In this paper, based on an adaptive chaos synchronization scheme, two methods of encoding-decoding message for secure communication are proposed. With the first method, message is directly added to the chaotic signal with parameter uncertainty. In the second method, multi-parameter modulation is used to simultaneously transmit more than one digital message （i.e., the multichannel digital communication） through just a single signal, which switches among various chaotic attractors that differ only subtly. In theory, such a treatment increases the difficulty for the intruder to directly intercept the information, and meanwhile the implementation cost decreases significantly. In addition, numerical results show the methods are robust against weak noise, which implies their practicability.
基金supported by the National Key Research and Development Program,No.2022YFB4703500Shenzhen High-tech Zone Development Special Plan Innovation Platform Construction Project+3 种基金the Proof of Concept Center for High Precision and High Resolution 4D Imagingthe National Key Research and Development Program,No.2022YFB4703500Beijing Natural Science Foundation,No.L243005National Natural Science Foundation of China,No.82372218.
文摘Effective survival analysis is essential for identifying optimal preventive treatments within smart healthcare systems and leveraging digital health advancements;however,existing prediction models face limitations,primarily relying on ensemble classification techniques with suboptimal performance in both target detection and predictive accuracy.To address these gaps,this paper proposes a multimodal framework that integrates enhanced facial feature detection and temporal predictive modeling.For facial feature extraction,this study developed a lightweight faceregion convolutional neural network(FRegNet)specialized in detecting key facial components,such as eyes and lips in clinical patients that incorporates a residual backbone(Rstem)to enhance feature representation and a facial path aggregated feature pyramid network for multi-resolution feature fusion;comparative experiments reveal that FReg-Net outperforms state-of-the-art target detection algorithms,achieving average precision(AP)of 0.922,average recall of 0.933,mean average precision(mAP)of 0.987,and precision of 0.98–significantly surpassing other mask region-based convolutional neural networks(RCNN)variants,such as mask RCNN-ResNeXt with AP of 0.789 and mAP of 0.957.Based on the extracted facial features and clinical physiological indicators,this study proposes an enhanced temporal encoding-decoding(ETED)model that integrates an adaptive attention mechanism and a gated weighting mechanism to improve predictive performance,with comparative results demonstrating that the ETED variant incorporating facial features(ETEncoding-Decoding-Face)outperforms traditional models,achieving an accuracy of 0.916,precision of 0.850,recall of 0.895,F1 of 0.884,and area under the curve(AUC)of 0.947–outperforming gradient boosting with an accuracy of 0.922,but AUC of 0.669,and other classifiers in comprehensive metrics.The results confirm that the multimodal dataset(facial features+physiological indicators)significantly enhances the prediction accuracy of the seven-day survival conditions of patients.Correlation analysis reveals that chronic health evaluation and mean arterial pressure are positively correlated with survival,while temperature,Glasgow Coma Scale,and fibrinogen are negatively correlated.
基金National Natural Science Foundation of China(No.61806006)Jiangsu University Superior Discipline Construction Project。
文摘In view of the problems of multi-scale changes of segmentation targets,noise interference,rough segmentation results and slow training process faced by medical image semantic segmentation,a multi-scale residual aggregation U-shaped attention network structure of MAAUNet(MultiRes aggregation attention UNet)is proposed based on MultiResUNet.Firstly,aggregate connection is introduced from the original feature aggregation at the same level.Skip connection is redesigned to aggregate features of different semantic scales at the decoder subnet,and the problem of semantic gaps is further solved that may exist between skip connections.Secondly,after the multi-scale convolution module,a convolution block attention module is added to focus and integrate features in the two attention directions of channel and space to adaptively optimize the intermediate feature map.Finally,the original convolution block is improved.The convolution channels are expanded with a series convolution structure to complement each other and extract richer spatial features.Residual connections are retained and the convolution block is turned into a multi-channel convolution block.The model is made to extract multi-scale spatial features.The experimental results show that MAAUNet has strong competitiveness in challenging datasets,and shows good segmentation performance and stability in dealing with multi-scale input and noise interference.
基金supported in part by the National Natural Science Foundation of China under Grant U20B2073,Grant 62103047Beijing Institute of Technology Research Fund Program for Young ScholarsYoung Elite Scientists Sponsorship Program by BAST(Grant No.BYESS2023365)
文摘With the expanding applications of multiple unmanned systems in various fields,more and more research attention has been paid to their security.The aim is to enhance the anti-interference ability,ensure their reliability and stability,and better serve human society.This article conducts adaptive cooperative secure tracking consensus of networked multiple unmanned systems subjected to false data injection attacks.From a practical perspective,each unmanned system is modeled using high-order unknown nonlinear discrete-time systems.To reduce the communication bandwidth between agents,a quantizer-based codec mechanism is constructed.This quantizer uses a uniform logarithmic quantizer,combining the advantages of both quantizers.Because the transmission information attached to the false data can affect the accuracy of the decoder,a new adaptive law is added to the decoder to overcome this difficulty.A distributed controller is devised in the backstepping framework.Rigorous mathematical analysis shows that our proposed control algorithms ensure that all signals of the resultant systems remain bounded.Finally,simulation examples reveal the practical utility of the theoretical analysis.
