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

Deep learning or deep neural network is a type of machine learning which exploits deep structures of neural networks with many layers of nonlinear data processing units. Deep neural networks have the ability to automatically extracting abstract data presentation for raw data such as time series signals, texts, and images. Deep learning has been extensively applied in vibration signal-based fault diagnosis for rotary machine since it can learn features from the raw signal of a rotary machine without requiring hand-crafted feature extraction. Batch normalization is a technique proposed to accelerate the training process and convergence of deep neural networks. In this paper, we propose a method for diagnosing faults in a rotary machine based on vibration signal using convolutional neural network with batch normalization technique, which is an advantaged variant of traditional convolutional neural network. The effectiveness of the proposed method is then verified with experiments on bearings and gearboxes fault data.

Rotary Machine Fault Diagnosis Using Scalogram Image and Convolutional Neural Network with Batch Normalization

This paper investigates complete synchronization and stabilization of hyperchaotic systems. A new modified finite-time control method is introduced to guarantee either the stability of the closed-loop system and finite-time complete synchronization or finite-time stabilization. Since the proposed controller possesses simple construction and easy implementation, it could be utilized in synchronization and stabilization of a general class of hyperchaotic systems. Simulation results are provided to demonstrate the feasibility and the efficacy of the proposed method.

Synchronization and Stabilization for Hyperchaotic Systems via a New Modified Finite-time Control

Improving the position accuracy of the closed chain robots needs a global robot kinematic model. Their closed loop constraints should be included in the global model via the specific passive joints of the robot. This paper proposes a simple but effective method to derive a mathematical formula for identification of all parameters of the robots having multiple closed chain mechanisms. An experimental calibration is carried out on a Hyundai HP160 robot to validate the correctness and effectiveness of the proposed method.

Position Accuracy Improvement of Robots having Closed-Chain Mechanisms

For the precision measurement of industrial products, the location of holes inside the products, if they exist, are often selected as feature points. The measurement of hole location would be performed by vision and laser-vision sensor. However, the usage of those sensors is limited in case of big change of light intensity and reflective shiny surface of the products. In order to overcome the difficulties, we have developed a hole displacement measuring device using contact-type displacement sensors (LVDTs). The developed measurement device attached to a robot measures small displacement of a hole by allowing its X-Y movement due to the contact forces between the hole and its own circular cone. The developed device consists of three plates which are connected in series for its own function. The first plate is used for the attachment to an industrial robot with ball-bush joints and springs. The second and third plates allow X-Y direction as LM guides. The bottom of the third plate is designed that various circular cones can be easily attached according to the shape of the hole. The developed system was implemented for its effectiveness that its measurement accuracy is less than 0.05mm.

Development of a Robotic System for Measuring Hole Displacement Using Contact-Type Displacement Sensors

This paper presents a fault detection and isolation scheme for wheeled mobile robots. A nonlinear observer is designed based on the mobile robot dynamic model. The fault is detected when at least one of the residuals exceeds its corresponding threshold. After that, three observers are activated to isolate three types of faults: right wheel fault, left wheel fault, and the other changed dynamic faults.

Hee-Jun Kang

Papers

Rotary Machine Fault Diagnosis Using Scalogram Image and Convolutional Neural Network with Batch Normalization

Synchronization and Stabilization for Hyperchaotic Systems via a New Modified Finite-time Control

Position Accuracy Improvement of Robots having Closed-Chain Mechanisms

Development of a Robotic System for Measuring Hole Displacement Using Contact-Type Displacement Sensors

Fault Detection and Isolation in Wheeled Mobile Robot