In this paper, a multi-feature extraction-based image identification method for rock debris in the drilling process is proposed, involving three main parts (trainable feature extractor, strong feature extraction, and ...
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In this paper, a multi-feature extraction-based image identification method for rock debris in the drilling process is proposed, involving three main parts (trainable feature extractor, strong feature extraction, and classification). In trainable feature extractor, abstract features are obtained by extracting the full connection layer of Convolutional Neural Network (CNN). In strong feature extraction, the method uses Gray-Level Co-occurrence Matrix (GLCM) and Color Coherence Vector (CCV) to get the strong feature. In classification, the extracted abstract features and strong features are concatenated and fed into the Support Vector Machine (SVM). Comparison results with two well-known methods indicated the effectiveness of the proposed method.
Ethiopian Airlines’ Boeing 737-8 MAX nosedived and crashed shortly after takeoff on March 10, 2019, at Ejere Town, south of Addis Ababa. A faulty angle of attack (AOA) sensor was the cause of the crash. Many airplane...
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ISBN:
(纸本)9781665478977
Ethiopian Airlines’ Boeing 737-8 MAX nosedived and crashed shortly after takeoff on March 10, 2019, at Ejere Town, south of Addis Ababa. A faulty angle of attack (AOA) sensor was the cause of the crash. Many airplane accidents have been linked to faulty AOA sensors in the past. The majority of the AOA sensor fault detection, isolation, and accommodation (SFDIA) literature relied on linear model-driven techniques, which are not suitable when the system’s model is uncertain, complex, or nonlinear. Traditional multilayer perceptron (MLP) models have been employed in data-driven models in the literature and the effectiveness of deep learning-based data-driven models has not been investigated. In this work, a data collection and processing method that ensures the collected data is not monotonous and a data-driven model for AOA SFDIA is proposed. The proposed model uses a deep learning-based recurrent neural network (RNN) to accommodate for faulty AOA measurement under flight conditions with faulty AOA measurement, faulty total velocity measurement, and faulty pitch rate measurement. Conventional residual analysis with a fixed threshold is used to detect and isolate faulty AOA sensors. The proposed and benchmark models are trained with the adaptive momentum estimation (Adam) algorithm. We show that the proposed model effectively detects, isolates, and accommodates faulty AOA measurements when compared to other data-driven benchmark models. The method is able to detect and isolate faulty AOA sensors with a detection delay of 0.5 seconds for ramp failure and 0.1 seconds for step failure.
Autonomous tracking control is one of the fundamental challenges in the field of robotic autonomous navigation,especially for future intelligent *** this paper,an improved pure pursuit control method is proposed for t...
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Autonomous tracking control is one of the fundamental challenges in the field of robotic autonomous navigation,especially for future intelligent *** this paper,an improved pure pursuit control method is proposed for the path tracking control problem of a four-wheel independent steering *** on the analysis of the four-wheel independent steering model,the kinematic model and the steering geometry model of the robot are *** the path tracking control is realized by considering the correlation between the look-ahead distance and the velocity,as well as the lateral error between the robot and the reference *** experimental results demonstrate that the improved pure pursuit control method has the advantages of small steady-state error,fast response and strong robustness,which can effectively improve the accuracy of path tracking.
Smart wheelchair plays an important role in rehabilitation training and daily movement of people with high mobility *** ensure that the smart wheelchair can operate stably in an unfamiliar environment,it is necessary ...
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Smart wheelchair plays an important role in rehabilitation training and daily movement of people with high mobility *** ensure that the smart wheelchair can operate stably in an unfamiliar environment,it is necessary to solve the problems of locating,mapping and motion *** the complex indoor environment of rehabilitation training and the wheelchair model,we propose a new smart wheelchair system *** on Simultaneous Localization and Mapping(SLAM) technology,location and mapping can be obtained through the Rao-Blackwellized Particle Filters(RBPF) *** integrate the sensor data into the proposal distribution to solve the particle degradation problem of RBPF and use selective resampling to improve the algorithm *** to the wheelchair’s own location and the surrounding environment map,global path planning and local real-time path planning are used to complete the motion *** show that the smart wheelchair system we proposed can operate robustly and quickly in a complex indoor environment.
A promising effective human-robot interaction in assistive robotic systems is gaze-based control. However, current gaze-based assistive systems mainly help users with basic grasping actions, offering limited support. ...
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作者:
Xu, MengXin, BinDou, LihuaGao, GuanqiangSchool of Automation
State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China
This paper proposes an intelligent "Cell Potential and Motion Pattern driven Coverage (CPMPC)" algorithm to solve a cooperative coverage path planning problem for multiple robots in two-dimensional target en...
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Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively lear...
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Objectif: La prévalence de la goutte augmente, mais les études analysant ses caractéristiques cliniques dans de grands échantillons sont insuffisantes. Cette étude s'attache à clarifi...
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Crowdsourcing is a critical technology in social manufacturing, which leverages an extensive and boundless reservoir of human resources to handle a wide array of complex tasks. The successful execution of these comple...
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Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System ...
Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance.
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