scholarly journals Characteristics of preceding Ia activity on postactivation depression in health and disease

2015 ◽  
Vol 113 (10) ◽  
pp. 3751-3758 ◽  
Author(s):  
Behdad Tahayori ◽  
Bahman Tahayori ◽  
David Koceja
Keyword(s):  
Patient Group ◽  
H Reflex ◽  
Separate Experiment ◽  
Healthy Individuals ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Exponential Decrease ◽  
Health And Disease ◽  
Abnormal Pattern ◽  
Postactivation Depression

Previous activation of the soleus Ia afferents causes a depression in the amplitude of the H-reflex. This mechanism is referred to as postactivation depression (PAD) and is suggested to be presynaptically mediated. With the use of a paired reflex depression paradigm (eliciting two H-reflexes with conditioning-test intervals from 80 ms to 300 ms), PAD was examined in a group of healthy individuals and a group of hemiplegic patients. Healthy individuals showed substantial depression of the test H-reflex at all intervals. Although the patient group showed substantially less depression at all intervals, increasing the interval between the two reflexes sharply reduced the depression. In a separate experiment, we varied the size of the conditioning H-reflex against a constant test H-reflex. In healthy individuals, by increasing the size of the conditioning H-reflex, the amplitude of the test H-reflex exponentially decreased. In the patient group, however, this pattern was dependent on the conditioning-test interval; increasing the size of the conditioning H-reflex caused an exponential decrease in the size of the test reflex at intervals shorter than 150 ms. This pattern was similar to that of healthy individuals. However, conducting the same protocol at a longer interval (300 ms) in these patients resulted in an abnormal pattern (instead of an exponential decrease in the size of the test reflex, exaggerated responses were observed). Fisher discriminant analysis suggested that these two patterns (which differed only in the timing between the two stimuli) were substantially different from each other. Therefore, it is suggested that the abnormal pattern of PAD in hemiplegic stroke patients could be a contributing factor for the pathophysiology of spasticity.

Optimal multi-kernel local fisher discriminant analysis for feature dimensionality reduction and fault diagnosis

2021 ◽  
pp. 1748006X2110093
Author(s):  
Qing Zhang ◽  
Heng Li ◽  
Xiaolong Zhang ◽  
Haifeng Wang
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Feature Space ◽  
High Dimensional ◽  
Fisher Discriminant Analysis ◽  
Vibration Signals ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis ◽  
Diagnosis Accuracy ◽  
Diagnosis Model

To achieve a more desirable fault diagnosis accuracy by applying multi-domain features of vibration signals, it is significative and challenging to refine the most representative and intrinsic feature components from the original high dimensional feature space. A novel dimensionality reduction method for fault diagnosis is proposed based on local Fisher discriminant analysis (LFDA) which takes both label information and local geometric structure of the high dimensional features into consideration. Multi-kernel trick is introduced into the LFDA to improve its performance in dealing with the nonlinearity of mapping high dimensional feature space into a lower one. To obtain an optimal diagnosis accuracy by the reduced features of low dimensionality, binary particle swarm optimization (BPSO) algorithm is utilized to search for the most appropriate parameters of kernels and K-nearest neighbor (kNN) recognition model. Samples with labels are used to train the optimal multi-kernel LFDA and kNN (OMKLFDA-kNN) fault diagnosis model to obtain the optimal transformation matrix. Consequently, the trained fault diagnosis model implements the recognition of machinery health condition with the most representative feature space of vibration signals. A bearing fault diagnosis experiment is conducted to verify the effectiveness of proposed diagnostic approach. Performance comparison with some other methods are investigated, and the improvement for fault diagnosis of the proposed method are confirmed in different aspects.

Two-parameter KNN algorithm and its application in recognition of brand rice

2021 ◽  
pp. 1-7
Author(s):  
Zhu Siyu ◽  
He Chongnan ◽  
Song Mingjuan ◽  
Li Linna
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Pattern Class ◽  
Fisher Discriminant ◽  
Two Parameter

In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm.

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