scholarly journals Optimization of IoT-Based Motion Intelligence Monitoring System

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jian Qiao ◽  
Zhendong Zhang ◽  
Enqing Chen
Keyword(s):  
Pattern Recognition ◽  
Monitoring System ◽  
Calculation Method ◽  
Human Movement ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Communication Method ◽  
Calculation Results ◽  
Real Value ◽  
Count Statistic

We design and implement an intelligent IoT-based motion monitoring system to realize the monitoring of three important parameters, namely, the type of movement, the number of movements, and the period of movement in physical activities, and optimize the system to support the simultaneous use by multiple users. Considering the motion monitoring scenario for smart fit, the framework of an IoT-based motion monitoring system is proposed. The framework contains components such as active acquisition nodes, wireless access points, data processing servers, and terminals. In terms of algorithm optimization, research related to active pattern recognition and periodic calculation methods is conducted. For active pattern recognition, two types of classification algorithms with different complexity are proposed based on Support Vector Machine (SVM) and deep neural networks, respectively, to adapt to scenarios with different computational capabilities. For period calculation, a method based on over-zero detection and wavelet transform is proposed to count the number of actions and calculate the period of each action. In 100 times action cycle calculation experiments, the count statistic calculation method achieves 100% calculation accuracy and the active cycle calculation results are close to the real value, which proves the effectiveness of the cycle calculation method. The system provides a multiuser-oriented communication method and realizes accurate and reliable human movement monitoring, which has a wide application prospect in the fields of physical education and rehabilitation training.

Computational Intelligence Techniques

2011 ◽  
pp. 361-388
Author(s):  
Bharat Sundaram ◽  
Marimuthu Palaniswani ◽  
Alistair Shilton ◽  
Rezaul Begg
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Computational Intelligence ◽  
Human Movement ◽  
Support Vector ◽  
New Developments ◽  
Vector Machines

Computational intelligence (CI) encompasses approaches primarily based on artificial neural networks, fuzzy logic rules, evolutionary algorithms, support vector machines and also approaches that combine two or more techniques (hybrid). These methods have been applied to solve many complex and diverse problems. Recent years have seen many new developments in CI techniques and, consequently, this has led to many applications in a variety of areas including engineering, finance, social and biomedical. In particular, CI techniques are increasingly being used in biomedical and human movement areas because of the complexity of the biological systems. The main objective of this chapter is to provide a brief description of the major computational intelligence techniques for pattern recognition and modelling tasks that often appear in biomedical, health and human movement research.

Computational Intelligence Techniques

2011 ◽  
pp. 139-171
Author(s):  
Bharat Sundaram ◽  
Marimuthu Palaniswani ◽  
Alistair Shilton ◽  
Rezaul Begg
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Computational Intelligence ◽  
Human Movement ◽  
Support Vector ◽  
New Developments ◽  
Vector Machines

Computational intelligence (CI) encompasses approaches primarily based on artificial neural networks, fuzzy logic rules, evolutionary algorithms, support vector machines and also approaches that combine two or more techniques (hybrid). These methods have been applied to solve many complex and diverse problems. Recent years have seen many new developments in CI techniques and, consequently, this has led to many applications in a variety of areas including engineering, finance, social and biomedical. In particular, CI techniques are increasingly being used in biomedical and human movement areas because of the complexity of the biological systems. The main objective of this chapter is to provide a brief description of the major computational intelligence techniques for pattern recognition and modelling tasks that often appear in biomedical, health and human movement research.

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

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.

Similarity detection of English text and teaching evaluation based on improved TCUSS clustering algorithm

2020 ◽  
pp. 1-11
Author(s):  
Yu Wang
Keyword(s):  
Language Processing ◽  
Calculation Method ◽  
Clustering Algorithm ◽  
Research Result ◽  
Structural Features ◽  
English Text ◽  
Support Vector ◽  
Similarity Calculation ◽  
Other Information ◽  
Calculation Task

The semantic similarity calculation task of English text has important influence on other fields of natural language processing and has high research value and application prospect. At present, research on the similarity calculation of short texts has achieved good results, but the research result on long text sets is still poor. This paper proposes a similarity calculation method that combines planar features with structured features and uses support vector regression models. Moreover, this paper uses PST and PDT to represent the syntax, semantics and other information of the text. In addition, through the two structural features suitable for text similarity calculation, this paper proposes a similarity calculation method combining structural features with Tree-LSTM model. Experiments show that this method provides a new idea for interest network extraction.

Optimization of a Specimen Size for the Determination of the Diffusible Hydrogen Content in Metals

2014 ◽  
Vol 698 ◽  
pp. 466-471
Author(s):  
Oleg V. Panchenko ◽  
Alexey M. Levchenko ◽  
Victor A. Karkhin
Keyword(s):  
Hydrogen Content ◽  
Calculation Method ◽  
Specimen Size ◽  
Experimental Results ◽  
Diffusible Hydrogen ◽  
Measuring Methods ◽  
Deposited Metal ◽  
Calculation Results

Specimens of various sizes are used to determine hydrogen content in deposited metals in such standards as ISO 3690, AWS A 4.3, and GOST 23338 while measuring methods are the same. It causes problems in comparison of experimental results and brings up the following question: what kind of specimen size is optimal to determine hydrogen content? An optimal specimen size was estimated using a calculation method. Experimental and calculation results obtained by using specimens with estimated dimensions were compared to the results obtained by using the specimen with dimensions of 100*25*8 mm to determine hydrogen content in a deposited metal.

Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines

2009 ◽  
Vol 119 (1-2) ◽  
pp. 32-38 ◽  
Author(s):  
Paula Martiskainen ◽  
Mikko Järvinen ◽  
Jukka-Pekka Skön ◽  
Jarkko Tiirikainen ◽  
Mikko Kolehmainen ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machines ◽  
Three Dimensional ◽  
Behaviour Pattern ◽  
Support Vector ◽  
Vector Machines

scholarly journals Data curation to improve the pattern recognition performance of B-cell epitope prediction by support vector machine

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Li Cen Lim ◽  
Yee Ying Lim ◽  
Yee Siew Choong
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
B Cell ◽  
Recognition Performance ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Support Vector ◽  
B Cell Epitopes ◽  
Prediction Module ◽  
B Cell Epitope

Abstract B-cell epitope will be recognized and attached to the surface of receptors in B-lymphocytes to trigger immune response, thus are the vital elements in the field of epitope-based vaccine design, antibody production and therapeutic development. However, the experimental approaches in mapping epitopes are time consuming and costly. Computational prediction could offer an unbiased preliminary selection to reduce the number of epitopes for experimental validation. The deposited B-cell epitopes in the databases are those with experimentally determined positive/negative peptides and some are ambiguous resulted from different experimental methods. Prior to the development of B-cell epitope prediction module, the available dataset need to be handled with care. In this work, we first pre-processed the B-cell epitope dataset prior to B-cell epitopes prediction based on pattern recognition using support vector machine (SVM). By using only the absolute epitopes and non-epitopes, the datasets were classified into five categories of pathogen and worked on the 6-mers peptide sequences. The pre-processing of the datasets have improved the B-cell epitope prediction performance up to 99.1 % accuracy and showed significant improvement in cross validation results. It could be useful when incorporated with physicochemical propensity ranking in the future for the development of B-cell epitope prediction module.

scholarly journals A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives

Foods ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 609
Author(s):  
Claudio Fredes ◽  
Constantino Valero ◽  
Belén Diezma ◽  
Marco Mora ◽  
José Naranjo-Torres ◽  
...  
Keyword(s):  
Oil Content ◽  
Near Infrared ◽  
Lipid Extraction ◽  
Reference Value ◽  
Support Vector ◽  
Estimation Model ◽  
Soxhlet Method ◽  
Real Value ◽  
Leave One Out ◽  
Score Index

Lipid extraction using the traditional, destructive Soxhlet method is not able to measure oil content (OC) on a single olive. As the color and near infrared spectrum are key parameters to build an oil estimation model (EM), this study grouped olives with similar color and NIR for building EM of oil content obtained by Soxhlet from a cluster of similar olives. The objective was to estimate OC of individual olives, based on clusters of similar color and NIR in two seasons. This study was performed with Arbequina olives in 2016 and 2017. The descriptor of the cluster consisted of the three color channels of c1c2c3 color model plus 11 reflectance points between 1710 and 1735 nm of each olive, normalized with the Z-score index. Clusters of similar color and NIR spectrum were formed with the k-means++ algorithm, leaving a sufficient number of olives to perform the Soxhlet analysis of OC, as reference value of EM. The training of EM was based on Support Vector Machine. The test was performed with Leave One-Out Cross Validation in different training-testing combinations. The best EM predicted the OC with 6 and 13% deviation with respect to the real value when one season was tested with itself and with another season, respectively. The use of clustering in EM is discussed.

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