Sparse representation of transients based on parametric multiple impulse dictionary for impact feature extraction of automatic tool changer system

2020 ◽  
Author(s):  
Guofa Li ◽  
Yongchao Huo ◽  
Jialong He ◽  
Yanbo Wang ◽  
Zhaojun Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Automatic Tool Changer ◽  
Automatic Tool

A Methodology for the Configuration Synthesis of Machining Centers With Automatic Tool Changer

1996 ◽  
Author(s):  
Hong-Sen Yan ◽  
Fu-Chen Chen
Keyword(s):  
Design Methodology ◽  
Systematic Approach ◽  
Coordinate Systems ◽  
Linear Type ◽  
Open Type ◽  
Tool Magazine ◽  
Automatic Tool Changer ◽  
Configuration Synthesis ◽  
Automatic Tool ◽  
Motion Characteristics

Abstract The purpose of this paper is to present a design methodology for the configuration synthesis of machining centers with automatic tool changer to meet the required topology and motion characteristics. According to the concept of coordinate systems, graph theory, generalization, specialization, and motion synthesis, this design methodology is proposed and computerized, and the machining centers with automatic tool changer up to eight links are synthesized. As the result, for the machining centers with drum type tool magazine, the numbers of configurations of machining centers with 6, 7, and 8 links are 2, 13, and 20, respectively. And, for the machining centers with linear type tool magazine, the numbers of configurations of machining centers with 5, 6, 7, and 8 links are 1, 5, 20, and 60, respectively. Furthermore, this work provides a systematic approach for synthesizing spatial open-type mechanisms with topology and motion requirements.

From the idea of “sparse representation” to a representation-based transformation method for feature extraction

2013 ◽  
Vol 113 ◽  
pp. 168-176 ◽  
Author(s):  
Yong Xu ◽  
Qi Zhu ◽  
Zizhu Fan ◽  
Yaowu Wang ◽  
Jeng-Shyang Pan
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Transformation Method

scholarly journals A novel framework for diagnosing automatic tool changer and tool life based on cloud computing

2016 ◽  
Vol 8 (3) ◽  
pp. 168781401663731
Author(s):  
Shang-Liang Chen ◽  
Chin-Fa Su ◽  
Yin-Ting Cheng
Keyword(s):  
Cloud Computing ◽  
Tool Life ◽  
Automatic Tool Changer ◽  
Automatic Tool

Application of Tree Graph Method for Reducing the Total Time of Tool Changing in Milling and Boring Machine Tools

2013 ◽  
Vol 371 ◽  
pp. 431-435 ◽  
Author(s):  
Claudiu Obreja ◽  
Gheorghe Stan ◽  
Lucian Adrian Mihaila ◽  
Marius Pascu
Keyword(s):  
Machine Tools ◽  
Design Method ◽  
Machining Process ◽  
Cnc Machine Tools ◽  
Tree Graph ◽  
Cnc Machine ◽  
High Productivity ◽  
Automatic Tool Changer ◽  
Automatic Tool ◽  
Set Up

With a view of increasing the productivity on CNC machine tools one of the main solution is to reduce, as much as possible, the auxiliary time consumed with the set-up and replacement of the tools and work pieces engaged in the machining process. Reducing the total time of the tool changing process by the automatic tool changer system can be also achieved through minimizing the number of movements needed for the actual exchange of the tool, from the tool magazine to the machine spindle (the optimization of the tool changing sequences). This paper presents a new design method based on the tree-graph theory. We consider an existing automatic tool changing system, mounted on the milling and boring machining centre, and by applying the new method we obtain all the possible configurations to minimize the tool changing sequence of the automatic tool changer system. By making use of the method proposed we obtain the tool changing sequences with minimum necessary movements needed to exchange the tool. Reconfiguring an existing machine tool provided with an automatic tool changer system by making use of the proposed method leads to obtaining the smallest changing time and thus high productivity.

scholarly journals Screening of sleep movement disorders via EMG signal analysis

2021 ◽  
Author(s):  
Mehrnaz Shokrollahi
Keyword(s):  
Feature Extraction ◽  
Sparse Representation ◽  
Movement Disorders ◽  
Dictionary Learning ◽  
Signal Analysis ◽  
Compact Set ◽  
Emg Signal ◽  
Increased Risk ◽  
Non Stationary Signal ◽  
Representation Techniques

It is estimated that 50 to 70 million Americans suffer from a chronic sleep disorder, which hinders their daily life, affects their health, and incurs a significant economic burden to society. Untreated Periodic Leg Movement (PLM) or Rapid Eye Movement Behaviour Disorder (RBD) could lead to a three to four-fold increased risk of stroke and Parkinson’s disease respectively. These risks bring about the need for less costly and more available diagnostic tools that will have great potential for detection and prevention. The goal of this study is to investigate the potentially clinically relevant but under-explored relationship of the sleep-related movement disorders of PLMs and RBD with cerebrovascular diseases. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using sparse representation techniques. To fulfill this objective, at first, we develop a novel algorithm for Electromyogram (EMG) signals in sleep based on sparse representation, and we use a generalized method based on Leave-One-Out (LOO) to perform classification for small size datasets. In the second objective, due to the long-length of these EMG signals, the need for feature extraction algorithms that can localize to events of interest increases. To fulfill this objective, we propose to use the Non-Negative Matrix Factorization (NMF) algorithm by means of sparsity and dictionary learning. This allows us to represent a variety of EMG phenomena efficiently using a very compact set of spectrum bases. Yet EMG signals pose severe challenges in terms of the analysis and extraction of discriminant features. To achieve a balance between robustness and classification performance, we aim to exploit deep learning and study the discriminant features of the EMG signals by means of dictionary learning, kernels, and sparse representation for classification. The classification performances that were achieved for detection of RBD and PLM by means of implicating these properties were 90% and 97% respectively. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The multi-layer feature extraction provide strong and successful characterization and classification for the EMG non-stationary signals and the proposed sparse representation techniques facilitate the adaptation to EMG signal quantification in automating the identification process.

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