Marine Internet of Things (Marine IoT) has garnered increasing interest in monitoring oceanic environments. But establishing a Marine IoT system for observing and processing marine environmental data presents various ...
Marine Internet of Things (Marine IoT) has garnered increasing interest in monitoring oceanic environments. But establishing a Marine IoT system for observing and processing marine environmental data presents various challenges. These include limited underwater communication bandwidth, and uneven distribution of tasks among data-sensing devices. This paper proposes an Edge Computing-RESTful (ECR) architecture to efficiently integrate and process the substantial volume of data gathered through marine observations. 1) The ECR method is introduced for task collaborative migration, aiming to schedule and manage the tasks within the Marine IoT system. 2) Through unified scheduling and management of tasks, the edge gateway can facilitate migration, scheduling, and interaction of task loads among various edge sensing devices. The proposed dynamic and adaptive approach matches computing tasks with edge sensing devices, minimizing energy consumption and migration delay within the Marine IoT system. Simulation results validate the superiority of the proposed method for Marine IoT, showcasing improved performance metrics such as reduced time delays and lower energy consumption during task collaborative migration.
This paper applies the proposed hybrid force and position control method to the physical robot system with interaction tasks to further improve our previous study. In the control scheme, the variable stiffness based o...
详细信息
ISBN:
(数字)9798331517519
ISBN:
(纸本)9798331517526
This paper applies the proposed hybrid force and position control method to the physical robot system with interaction tasks to further improve our previous study. In the control scheme, the variable stiffness based on proportional integral derivative(PID) admittance control is adopted for interaction force tracking and the radial basis function neural network(RBFNN) based fixed-time control is designed to ensure position tracking. We have performed interaction tasks based on a Baxter robot for drawing on the plane and slope plane with different expected interaction forces and position trajectories. The experiment results indicate that the method performs well in terms of interaction force and trajectory tracking.
Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature...
Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud. Most existing methods focus on designing efficient local feature extractors while ignoring global connection, or vice versa. In this paper, we design a new Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point relations, which considers both local and global attentions. Specifically, considering local spatial coherence, local feature learning is performed through Relative Position Encoding and Attentive Feature Pooling. We incorporate the learned locality into the Transformer module. The local feature affects value component in Transformer to modulate the relationship between channels of each point, which can enhance self-attention mechanism with locality based channel interaction. We demonstrate its superiority experimentally on classification and segmentation tasks. The code is available at: https://***/jiamang/IBT
The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesi...
详细信息
ISBN:
(数字)9798350364293
ISBN:
(纸本)9798350364309
The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.
The evolution of web technologies has brought to the fore new solutions for content management and distribution. The development of these new technologies has managed to lay the foundations of a strong web industry an...
详细信息
Waves seriously impact port construction, worldwide route planning, military activities, and wave power generation. To improve the accuracy of significant wave height prediction, we proposed a novel prediction method,...
详细信息
Continuous subgraph matching (CSM) is a critical task for analyzing dynamic graphs and has a wide range of applications, such as merchant fraud detection, cyber-attack hunting, and rumor detection. Although many effic...
详细信息
Broad learning system(BLS)is an emerging neural network characterized by its rapid processing and robust generalization ***,determining the appropriate structure for broad learning system is also a *** addition,broad ...
详细信息
Broad learning system(BLS)is an emerging neural network characterized by its rapid processing and robust generalization ***,determining the appropriate structure for broad learning system is also a *** addition,broad learning system may perform overfitting due to the dependence between nodes in processing fully connected *** deal with these problems,an efficient ensemble broad learning system based on Dropout and Dropconnect is proposed in this *** proposed Dropout ensemble broad learning system randomly discards hidden nodes to improve diversity between individuals and reduce the synergy between nodes to improve prediction *** Dropconnect ensemble broad learning system randomly drops connection weights to generate more complementary models by adding input attribute *** experimental results on the UCI datasets confirm that the method proposed in this paper can solve the problem of model overfitting caused by the strong dependence between the nodes of ensemble broad learning *** proposed algorithm outperforms the original BLS in terms of prediction stability and classification accuracy.
With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary ...
详细信息
In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,ex...
详细信息
In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time *** proposed system is based on Commodity WiFi and is easy to *** WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel *** feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.
暂无评论