From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

비만아 부모의 돌봄 경험: q 방법론

Applications of machine learning to machine fault diagnosis: A review and roadmap

1D convolutional neural networks and applications: A survey

During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.

1D Convolutional Neural Networks and Applications: A Survey

Robot kinematic calibration used to be carried out with the partial pose measurements (position only) of dimension 3 in industry, while full pose measurements (orientation and position) of dimensio...

https://journals.sagepub.com/doi/pdf/10.1177/16878140211047754

A performance comparison of the full pose- and partial pose-based robot calibration for various types of robot manipulators

This paper presents a method of improving performance of visual servoing system by integrating inertial sensors to the system. The method is applied to a roll and pitch rotated platform stabilization in high control frequency. For the purpose, an inertial measurement unit is attached to the platform to provide its dynamics information. A new inertial information based feedforward control is used along with the conventional visual feedback control. Two contributions are realized: first, it helps solve the remaining limitation of static-object assumption in conventional visual servoing. Second, it helps drastically improve the response rate of the servoing system due to the utilization of a high-speed inertial measurement unit. Stability of the control system is analyzed such that the error of the system is proved to be bounded. Control algorithm was simulated using Matlab Aerospace Toolbox as well as Robotics Toolbox. Then, experiments were implemented to verify the feasibility of the proposed methodology.

A platform stabilization algorithm based on feedforward visual-inertial servoing

This paper investigates an algorithm for robust fault detection and isolation(FDI) in robot manipulator using an neural network(NN) based observer. The proposed FDI algorithm uses both an nonlinear estimation process and an neural network based learning algorithm. This online monitoring algorithm is used to detect and isolate dynamic changes of a robot manipulator due to both incipient and abrupt faults. Another neural network is used for estimate the uncertainties in robot dynamics. A computer simulation for a two link robot manipulator shows the effectiveness of the proposed algorithm in the fault detection and isolation process.

http://ieeexplore.ieee.org/iel5/6011517/6020946/06021030.pdf

A robust detection and isolation scheme for incipient and abrupt faults in robot manipulator using neural network

In this paper, a novel fault-tolerant control tactic for robot manipulator systems using only position measurements is proposed. The proposed algorithm is constructed based on a combination of a nonsingular fast terminal sliding mode control (NFTSMC) and a novel high-speed third-order sliding mode observer (TOSMO). In the first step, the high-speed TOSMO is proposed for the first time to approximate both the system velocity and the lumped unknown input with a faster convergence time compared to the TOSMO. The faster convergence speed is obtained thanks to the linear characteristic of the added elements. In the second step, the NFTSMC is designed based on a nonsingular fast terminal sliding (NFTS) surface and the information obtained from the proposed high-speed TOSMO. Thanks to the combination, the proposed controller–observer tactic provides excellent features, such as a fast convergence time, high tracking precision, chattering phenomenon reduction, robustness against the effects of the lumped unknown input and velocity requirement elimination. Especially, the proposed observer does not only improve the convergence speed of the estimated signals, but also increases the system dynamic response. The system’s finite-time stability is proved using the Lyapunov theory. Finally, to validate the efficiency of the proposed strategy, simulations on a PUMA560 robot manipulator are performed.

/pdf/a-novel-high-speed-third-order-sliding-mode-observer-for-c0z7fhr6.pdf

A Novel High-Speed Third-Order Sliding Mode Observer for Fault-Tolerant Control Problem of Robot Manipulators

In this paper, we propose the estimation of contact force in a collaborative robot without explicit force-sensing based on the adaptive third-order sliding mode observer. An adaptive third-order sliding mode observer was designed to estimate the contact force based only on the position measurement. The information from the observer was used for admittance control and emergent stop control when the robot interacts with a human. The experimental results with the emergent stop control and admittance control, using the proposed contact force estimation for the 3-DOF AT2-FARA robot manipulator, illustrate the capability of the proposed system in real-world application.

https://www.tandfonline.com/doi/pdf/10.1080/21642583.2022.2063201?needAccess=true

Hee-Jun Kang

Papers

A performance comparison of the full pose- and partial pose-based robot calibration for various types of robot manipulators

A platform stabilization algorithm based on feedforward visual-inertial servoing

A robust detection and isolation scheme for incipient and abrupt faults in robot manipulator using neural network

A Novel High-Speed Third-Order Sliding Mode Observer for Fault-Tolerant Control Problem of Robot Manipulators

Implementation of sensorless contact force estimation in collaborative robot based on adaptive third-order sliding mode observer