Point cloud completion aims to infer complete point clouds based on partial 3D point cloud *** previous methods apply coarseto-fine strategy networks for generating complete point ***,such methods are not only relativ...
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Point cloud completion aims to infer complete point clouds based on partial 3D point cloud *** previous methods apply coarseto-fine strategy networks for generating complete point ***,such methods are not only relatively time-consuming but also cannot provide representative complete shape features based on partial *** this paper,a novel feature alignment fast point cloud completion network(FACNet)is proposed to directly and efficiently generate the detailed shapes of *** aligns high-dimensional feature distributions of both partial and complete point clouds to maintain global information about the complete *** its decoding process,the local features from the partial point cloud are incorporated along with the maintained global information to ensure complete and time-saving generation of the complete point *** results show that FACNet outperforms the state-of-theart on PCN,Completion3D,and MVP datasets,and achieves competitive performance on ShapeNet-55 and KITTI ***,FACNet and a simplified version,FACNet-slight,achieve a significant speedup of 3–10 times over other state-of-the-art methods.
In the trend of continuously advancing urban intelligent transport construction, traditional traffic signal control (TSC) struggles to make effective decisions with complex traffic conditions. Although multi-agent dee...
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The responses of soil CO2 emission to global changes are crucial for predicting the future terrestrial carbon cycle. However, the effects of warming and nitrogen addition on soil CO2 emissions during different seasons...
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Bluetooth-low-energy (BLE)-based low-power cameras have expanded the applications of battery-powered low-power Internet of Things (IoT) vision systems. The adaptive tuning of connection and image encoding settings pla...
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This research combines the NAO robot platform to develop three single scene modes (visual line-following navigation, object location and grabbing, and moving object tracking and obstacle avoidance), and finally throug...
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This research combines the NAO robot platform to develop three single scene modes (visual line-following navigation, object location and grabbing, and moving object tracking and obstacle avoidance), and finally through the frame based expert system to form a knowledge base of each mode, and uses the inference engine to achieve multi-mode fusion. In the development of visual line-following navigation, the proposed fast path extraction processing method can not only accurately extract the path information in the case of noisy interference, but also improve the running speed to ensure the real-time performance of the robot. A slope compensation PID controller is proposed to control the parameters of the robot during walking, which ensures low error and high stability when the robot follows the navigation line. In the development of object positioning and grabbing, a monocular localization algorithm is proposed using the characteristics of QR code, then the kinematics of the robot arm is modeled. Finally, the end effector of the robot is moved to the specified position according to the three-dimensional coordinates of the QR code to grasp the target object. In the development of moving object tracking and obstacle avoidance, the NaoMark marker is used to simplify the characteristics of the moving target, and the parameters of the robot walking are controlled according to the central position of the marker in the image. In order to avoid the collision of the robot with the obstacle during the movement, the obstacle avoidance is realized by combining the ultrasonic sensor measurement and the artificial potential field algorithm. Finally, the implementation methods of each part of the above three modes are formed into a knowledge base in the form of a framework based expert system, and the knowledge is combined by the inference engine to realize the fusion of multiple modes to complete tasks that require multiple modes. This research has presented an original solution, as
Current motion detection and evaluation technologies face challenges such as limited scalability, imprecise feedback, and lack of personalized guidance. To address these challenges, this research integrated efficient ...
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Nowadays, the proliferation of open Internet of Things (IoT) devices has made IoT systems increasingly vulnerable to cyber attacks. It is of great practical significance to solve the security issues of IoT systems. Dr...
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This paper investigates the input-to-state stabilization of discrete-time Markov jump systems. A quantized control scheme that includes coding and decoding procedures is proposed. The relationship between the error in...
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As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering ...
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As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering *** literature studies have proposed numerousmodels for the classification of security ***,adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning ***,most of the researchers focus only on the classification of requirements with security *** did not consider other nonfunctional requirements(NFR)directly or indirectly related to *** has been identified as a significant research gap in security requirements *** major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security *** use PROMISE_exp and DOSSPRE,the two most commonly used datasets in the software engineering *** proposed methodology consists of two *** the first step,we analyze all the nonfunctional requirements and their relation with security *** found 10 NFRs that have a strong relationship with security *** the second step,we categorize those NFRs in the security requirements *** proposedmethodology is a hybridmodel based on the ConvolutionalNeural Network(CNN)and Extreme Gradient Boosting(XGBoost)***,we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security *** performance is evaluated using four metrics:recall,precision,accuracy,and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3%and 85.3%accuracy,*** proposed study shows an enhancement in metrics
The scaler and scheduler of serverless system are the two cornerstones that ensure service quality and efficiency. However, existing scalers and schedulers are constrained by static thresholds, scaling latency, and si...
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