Strength abilities: pattern recognition method in the management of the cumulative effect of strength loads in 8-year-old boys

2021 ◽  
Vol 25 (4) ◽  
pp. 253-260
Author(s):  
Olha Ivashchenko ◽  
Oleg Khudolii ◽  
Wladyslaw Jagiello
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Strength Training ◽  
Cumulative Effect ◽  
Training Effect ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
The Third ◽  
Strength Exercises ◽  
The Impact

Background and Study Aim. The purpose of the study was to determine the peculiarities of using pattern recognition method in the management of the cumulative effect of strength loads in 8-year-old boys. Materials and methods. The study participants were 48 boys aged 8. The experiment was conducted using a 22 factorial design. The study materials were processed by the IBM SPSS 22 statistical analysis program. Discriminant analysis was performed. The study examined the impact of four variants of strength load on the formation of the cumulative training effect of three, six, nine, and twelve classes in 8-year-old boys. Results. The discriminant analysis provided information about the impact of four orthogonal variants of strength loads on the formation of the cumulative training effect of strength exercises of three, six, nine, and twelve classes in 8-year-old boys. The obtained data make it possible to choose a load mode at each step of the CTE formation and to manage schoolchildren’s strength training. Conclusions. The verification of the obtained discriminant functions shows their high discriminative ability and value in interpretation with respect to the general population (p < 0.05). It was found that the formation of the CTE of three classes is most influenced by the third load variant, six classes – by the third load variant, nine classes – the third load variant, twelve classes – the first load variant. The discriminant function structure coefficients made it possible to identify the factor structure of the CTE of 3, 6, 9, 12 classes, to find that the CTE3, CTE6 are associated with the work at the first place “Exercises to strengthen arm muscles”, the CTE9, CTE12 – with the work at the third (“Exercises to strengthen back muscles”) and the fourth (“Exercises to strengthen leg muscles”) places. The CTE of three, six, nine, and twelve classes depends on the modes of strength exercises and has different focuses. The CTE3 – speed and strength focus; CTE6, 9 – comprehensive focus; CTE12 – explosive-strength focus. The obtained values of centroids for the CTE of 3, 6, 9, 12 classes enable the management of schoolchildren’s strength training.

scholarly journals Strength Abilities: Immediate and Delayed Training Effects of Orthogonal Modes of Strength Training in Boys Aged 8 Years

2020 ◽  
Vol 20 (2) ◽  
pp. 109-116
Author(s):  
Olha Ivashchenko ◽  
Oleg Khudolii ◽  
Krzysztof Prusik ◽  
Vasilios Giovanis
Keyword(s):  
Discriminant Analysis ◽  
Strength Training ◽  
Training Effects ◽  
Back Muscles ◽  
The Third ◽  
Study Materials ◽  
Leg Muscles ◽  
Shoulder Muscles ◽  
Strength Exercises ◽  
The Impact

The study purpose was to determine the dynamics of training effects of orthogonal modes of strength training in boys aged 8 years. Materials and methods. The study participants were 48 boys aged 8 years. The experiment was performed using a 22 factorial design. The study materials were processed using the IBM SPSS 22 statistical analysis program.Discriminant analysis was performed. The study examined the impact of four variants of strength training loads on the immediate (ITE) and delayed (DTE) training effects of orthogonal modes of strength exercises and rest intervals in 8-year-old boys. Results. In the first variant of strength training, the largest contribution to the dynamics of training effects is made by the work performed at the first place “exercises to strengthen arm and shoulder muscles”; in the second variant, the largest contribution to the dynamics of training effects is made by the work performed at the third place “exercises to strengthen back muscles”; in the third variant, the largest contribution to the dynamics of training effects is made by the work performed at the first “exercises to strengthen arm and shoulder muscles” and the third “exercises to strengthen back muscles” places; in the fourth variant, the largest contribution to the dynamics of ITE is made by thework performed at the first “exercises to strengthen arm and shoulder muscles” and the third “exercises to strengthen back muscles” places. The most significant changes in the DTE are associated with the fourth place’s work “exercises to strengthen leg muscles”. Conclusions. The response to strength training load includes immediate and delayed training effects. Thus it can be argued that training effects can be classified using the given battery of tests based on discriminant analysis. The efficiency of discriminant analysis increases when using 2k FFE active experiments.

scholarly journals Rapid Discrimination for Traditional Complex Herbal Medicines from Different Parts, Collection Time, and Origins Using High-Performance Liquid Chromatography and Near-Infrared Spectral Fingerprints with Aid of Pattern Recognition Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Haiyan Fu ◽  
Yao Fan ◽  
Xu Zhang ◽  
Hanyue Lan ◽  
Tianming Yang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Discriminant Analysis ◽  
High Performance ◽  
Near Infrared ◽  
Herbal Medicines ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Collection Time

As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC) and near-infrared (NIR), with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information ofHibiscus mutabilisL. andBerberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA), linear discriminant analysis (LDA), and particularly partial least squares discriminant analysis (PLSDA) with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines.

scholarly journals Pattern Recognition: Effectiveness of Teaching Girls Aged 14 the Cartwheel Authors

2021 ◽  
Vol 2 (3) ◽  
pp. 140-145
Author(s):  
Inna Kalistratova ◽  
Oleg Khudolii
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Physical Education ◽  
Research Methods ◽  
Rest Interval ◽  
The Third ◽  
Second Mode ◽  
Study Participants ◽  
The Impact

Purpose. To determine the impact of exercise modes on the effectiveness of teaching girls aged 14 the cartwheel. Materials and methods. The study participants were 20 girls aged 14. The children and their parents were fully informed about all the features of the study and gave their consent to participate in the experiment. To solve the tasks set, the following research methods were used: study and analysis of scientific and methodological literature; pedagogical observation, timing of training tasks; pedagogical experiment, methods of mathematical statistics, discriminant analysis. Results. The study found that statistically significant differences in the number of repetitions were observed in performing all series of training tasks, except the third one (p < 0.05). The girls aged 14 who used the first mode (6 sets 1 time each with a rest interval of 60 s) needed fewer repetitions to master the movements of the first, second, fourth, fifth, and sixth series of tasks (p < 0.05). The girls aged 14 who used the second mode (6 sets 2 times each with a rest interval of 60 s) needed fewer repetitions to master the movements of the third series of tasks (p < 0.05). Conclusions. Discriminant analysis made it possible to determine the impact of the number of repetitions on the effectiveness of developing the cartwheel skill in girls aged 14. Based on the analysis of group centroids, it was found that exercise modes significantly influence the cartwheel skill development in girls aged 14 during physical education classes. The results of classification of the groups show that 100.0 % of the original grouped cases were classified correctly.

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

2022 ◽  
Author(s):  
Canyi Du ◽  
Rui Zhong ◽  
Yishen Zhuo ◽  
Xinyu Zhang ◽  
Feifei Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Vibration Signals ◽  
One Dimensional ◽  
Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

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