Model adaptive apparatus and model adaptive method, recording medium, and pattern recognition apparatus

2006 ◽  
Vol 120 (5) ◽  
pp. 2415
Author(s):  
Hongchang Pao
Keyword(s):  
Pattern Recognition ◽  
Adaptive Method ◽  
Recording Medium

scholarly journals An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7404
Author(s):  
Veronika Spieker ◽  
Amartya Ganguly ◽  
Sami Haddadin ◽  
Cristina Piazza
Keyword(s):  
Pattern Recognition ◽  
Position Effect ◽  
Adaptive Method ◽  
Completion Rate ◽  
Hand Motion ◽  
Limb Position ◽  
Upper Limb Prostheses ◽  
New Information ◽  
Classical Pattern ◽  
External Loads

Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset—i.e., representing variations in limb position or external loads—to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development.

scholarly journals Extreme Interval Entropy Based on Symbolic Analysis and a Self-Adaptive Method

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 238
Author(s):  
Zhuofei Xu ◽  
Yuxia Shi ◽  
Qinghai Zhao ◽  
Wei Li ◽  
Kai Liu
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Extreme Values ◽  
Rolling Bearing ◽  
Adaptive Methods ◽  
Adaptive Method ◽  
Printing Press ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Self Adaptive

Self-adaptive methods are recognized as important tools in signal process and analysis. A signal can be decomposed into a serious of new components with these mentioned methods, thus the amount of information is also increased. In order to use these components effectively, a feature set is used to describe them. With the development of pattern recognition, the analysis of self-adaptive components is becoming more intelligent and depend on feature sets. Thus, a new feature is proposed to express the signal based on the hidden property between extreme values. In this investigation, the components are first simplified through a symbolization method. The entropy analysis is incorporated into the establishment of the characteristics to describe those self-adaptive decomposition components according to the relationship between extreme values. Subsequently, Extreme Interval Entropy is proposed and used to realize the pattern recognition, with two typical self-adaptive methods, based on both Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). Later, extreme interval entropy is applied in two fault diagnosis experiments. One experiment is the fault diagnosis for rolling bearings with both different faults and damage degrees, the other experiment is about rolling bearing in a printing press. The effectiveness of the proposed method is evaluated in both experiments with K-means cluster. The accuracy rate of the fault diagnosis in rolling bearing is in the range of 75% through 100% using EMD, 95% through 100% using EWT. In the printing press experiment, the proposed method can reach 100% using EWT to distinguish the normal bearing (but cannot distinguish normal samples at different speeds), with fault bearing in 4 r/s and in 8 r/s. The fault samples are identified only according to a single proposed feature with EMD and EWT. Therefore, the extreme interval entropy is proved to be a reliable and effective tool for fault diagnosis and other similar applications.

Human Action Modeling and Application to a Control System Using the Mahalanobis-Taguchi System

2016 ◽  
Author(s):  
Yusuke Asaka ◽  
Keiichi Watanuki ◽  
Shuichi Fukuda ◽  
Keiichi Muramatsu ◽  
Kazunori Kaede
Keyword(s):  
Pattern Recognition ◽  
Control System ◽  
Knowledge Acquisition ◽  
Explicit Knowledge ◽  
Adaptive Method ◽  
Human Action ◽  
Embodied Knowledge ◽  
Set Up ◽  
Intuitive Sense ◽  
Recognition Technique

This paper discusses human action modeling and its application to a control system that uses the Mahalanobis – Taguchi System (MTS). In this study, we define embodied knowledge as being included in tacit knowledge. We also define a set of skills based on experiences and intuitive sense as seen in creating an art, sport, craft, or other skilled task. Embodied knowledge is difficult to express explicitly. As our goals, we analyze embodied knowledge acquisition for human action modeling and apply to a control system by using MTS. An analysis of embodied knowledge using devices and pattern-recognition techniques to recognize un-explicit knowledge are being developed owing to recent improvements in technology. Embodied knowledge acquisition element of recognition can be represented as a pattern recognition technique. In this paper, we confirm that MTS is an adaptive method for recognizing pattern in human action modeling. We set up a control model including a controller using the MTS, which is modeled after an internal model of the cerebellum. We apply the controller based on the Recognition Taguchi (RT) method to invert the control of the pendulum. The result indicate that the controller is capable of detecting disturbance.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Structural Investigation of Iron Particles Used for Magnetic Recording Media

1980 ◽  
Vol 38 ◽  
pp. 406-407
Author(s):  
Alfred Baltz
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Electron Microscope ◽  
Structural Investigation ◽  
Magnetic Interaction ◽  
Recording Media ◽  
Magnetic Recording Media ◽  
Recording Medium ◽  
Iron Particles ◽  
Transmission Electron

As part of a program to develop iron particles for next generation recording disk medium, their structural properties were investigated using transmission electron microscopy and electron diffraction. Iron particles are a more desirable recording medium than iron oxide, the most widely used material in disk manufacturing, because they offer a higher magnetic output and a higher coercive force. The particles were prepared by a method described elsewhere. Because of their strong magnetic interaction, a method had to be developed to separate the particles on the electron microscope grids.

Pattern Recognition: The Basis of All Cognition?

1989 ◽  
Vol 34 (11) ◽  
pp. 988-989
Author(s):  
Erwin M. Segal
Keyword(s):  
Pattern Recognition
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