Nuclear power is one of several significant clean energy and its production capacity is increasing rapidly. This paper proposed a novel PID self-tuning method based on closed-loop identification used in nuclear power ...
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This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the availab...
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ISBN:
(数字)9798350357882
ISBN:
(纸本)9798350357899
This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.
Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensio...
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Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensional features,data heterogeneity,and data noise due to the diversity of data dimensions,formats and noise of sensors,controllers and ***,a novel unsupervised learn-ing autoencoder model is proposed for ICS data in this *** traditional methods only capture the linear correlations of ICS features,our deep industrial representation learning model(DIRL)based on a convolutional neural network can mine high-order features,thus solving the problem of high-dimensional and heterogeneous ICS *** addition,an unsupervised denoising autoencoder is introduced for noisy ICS data in *** the denoising autoencoder allows the model to better mitigate the sensor noise *** this way,the represen-tative features learned by DIRL could help to evaluate the safety state of ICSs more *** tested our model with absolute and relative accuracy experi-ments on two large-scale ICS *** with other popular methods,DIRL showed advantages in four common indicators of AI algorithms:accuracy,precision,recall,and *** study contributes to the effective analysis of large-scale ICS data,which promotes the stable operation of ICSs.
At present, blockchain is not only applied in the financial field, but also extended to the medical, insurance, internet of things, and many other fields. Its independence leads to more and more significant problems o...
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In order to improve the real-time and reliability of network communication in intelligent ship system, this paper analyzes the requirements of ship-borne network, and develops a real-time Ethernet protocol stack based...
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Tissue sections can reveal tumour-specific changes, and a tumour diagnosis can be made by analyzing the arrangement and distribution of cells on the surface. Although histopathological diagnosis based on tissue sectio...
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The need for mechanical model simulation platforms is growing as a result of the ongoing advancements in digital technology and the mechanical manufacturing sector. The application scope and efficacy of simulation pla...
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Printed circuit board defect recognition is of great significance to ensuring the quality of electronic products. This paper proposes an improved PCB defect detection method based on YOLOV8. Firstly, the data augmenta...
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ISBN:
(数字)9798331506230
ISBN:
(纸本)9798331506247
Printed circuit board defect recognition is of great significance to ensuring the quality of electronic products. This paper proposes an improved PCB defect detection method based on YOLOV8. Firstly, the data augmentation method PredMix (prediction mix) is adopted to simulate scenarios such as overlapping and occlusion in images, where the displays are incomplete or blurred, thereby enhancing the robustness of the network model. Secondly, BiFPN is used to replace PAN-FPN in the fusion layer to strengthen the feature fusion capability. Finally, the CESE_C2f module is employed in the Backbone part, leveraging a lightweight attention mechanism to improve the detection effect while reducing the number of parameters. Experimental results show that compared with the original YOLOV8, the MAP value of this improved algorithm reaches $\mathbf{92.6}\%$, with a $\mathbf{2.3\%}$ increase in accuracy.
In order to make the robot understand the scene better and the human-robot interaction more friendly, a lightweight semantic SLAM system is proposed in this paper. Our system uses a LIDAR combined with a camera to obt...
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In order to make the robot understand the scene better and the human-robot interaction more friendly, a lightweight semantic SLAM system is proposed in this paper. Our system uses a LIDAR combined with a camera to obtain object location in SLAM and determines object information such as category with a CNN-based object detector. The object identities are represented in the form of joint probability distributions that are updated by Bayes’ theorem. The FPA algorithm is used to obtain parameter valuation with maximum posterior probability so as to determine the modeling feature parameters of objects on the map. The similarity between objects is measured with the Bhattacharyya coefficient to avoid duplicate modeling of identified objects in map updating. The proposed system is simulated in Gazebo and an effective scheme balancing accuracy and computation cost is obtained. Finally, experiments on a small indoor robot demonstrate the effectiveness of our method.
Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The undergro...
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