scholarly journals Cross‐Training: Time Well Spent Leading to Time Saved!

2022 ◽  
Vol 2 (1) ◽  
Author(s):  
Bradley A. Gross
Keyword(s):  
2009 ◽  
Author(s):  
Kevin C. Stagl ◽  
Cameron Klein ◽  
Patrick J. Rosopa ◽  
Deborah DiazGranados ◽  
Eduardo Salas ◽  
...  
Keyword(s):  

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.


2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.


2018 ◽  
Vol 22 (2) ◽  
pp. 353-356
Author(s):  
V.P. Kovalchuk ◽  
I.M. Kovalenko ◽  
S.V. Kovalenko ◽  
V.M. Burkot ◽  
V.O. Kovalenko

Innovations change the world in all spheres of life, and education is not an exception. Modern requirements of time put us new challenges that require the use of new information technologies at all stages of the educational process in higher education institutions, in particular the use of the Internet. In addition, it has been noted that Internet resources increase motivation and contribute to the formation of a fully developed personality. Testing and evaluating students' knowledge and abilities is an integral part of the credit-module system. One of the forms of evaluation of the initial level of knowledge, consolidation and improvement of assimilation of information is testing. It should be noted that in a number of countries, testing has shifted traditional forms of control — oral and written exams and interviews. However, in Ukraine, educators remain adherents of a combination of testing and classical analysis of material. It allows the most efficient distribution of the training time of a practical class, 100% control of the knowledge and the effectiveness of mastering the material of all the students of the academic group. Technical progress stimulates the search for new variants and possibilities of testing, its various variations. One of the options that can help solve this problem was a smartphone. In order to facilitate the work of the teacher at the Department of Microbiology, an online testing system with the use of smartphones was introduced. Online testing is conducted among students with Ukrainian and English language training. With the Google Forms platform, the teacher creates a form which contains the student's records and tests. Students directly from the teacher get a link to fill out an online form directly at the lesson. For testing, a database containing standard KROK-1 licensed test tasks is used. The form can contain any number of test tasks that are in arbitrary order, as well as a changed order of distractors, which makes it impossible to write off. At the same time, all students are in the same conditions: all write one option. After submitting the form, the student receives a notification that his response is recorded. Re-linking is not possible. In turn, the teacher receives a message on the result of the test in the table — the ratio of correct answers to the total number of questions, as well as options for their answers. First and foremost, questions are displayed on which students gave the largest number of incorrect answers. This allows the topic to be considered in the process of discussion of the most difficult tasks from the students perspective, and in the future it will allow more efficiently to create forms for on-line tests and to focus on these issues.


Biomimetics ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Michelle Gutiérrez-Muñoz ◽  
Astryd González-Salazar ◽  
Marvin Coto-Jiménez

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality.


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