A Novel Motion Recognition Method Based on Force Myography of Dynamic Muscle Contractions

Surface electromyogram-based pattern recognition (sEMG-PR) has been considered as the most promising method to control multifunctional prostheses for decades. However, the commercial applications of sEMG-PR in prosthetic control is still limited due to the ambient noise and impedance variation between electrodes and skin surface. In order to reduce these issues, a force-myography-based pattern recognition method was proposed. In this method, a type of polymer-based flexible film sensors, the piezoelectrets, were used to record the rate of stress change (RSC) signals on the muscle surface of eight able-bodied subjects for six hand motions. Thirteen time domain features and four classification algorithms of linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM) were adopted to decode the RSC signals of different motion classes. In addition, the optimal feature set, classifier, and analysis window length were investigated systematically. Results showed that the average classification accuracy was 95.5 ± 2.2% by using the feature combination of root mean square (RMS) and waveform length (WL) for the classifier of KNN, and the analysis window length of 300 ms was found to obtain the best classification performance. Moreover, the robustness of the proposed method was investigated, and the classification accuracies were observed above 90% even when the white noise ratio increased to 50%. The work of this study demonstrated the effectiveness of RSC-based pattern recognition method for motion classification, and it would provide an alternative approach for the control of multifunctional prostheses.

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

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.

Multiple Collaborative Supervision Pattern Recognition Method within Social Organizations Based on Data Clustering Algorithm

This paper proposes a multiple collaborative supervision pattern recognition method within social organizations based on data clustering algorithm to realize diversified supervision within social organizations and improve the effect of the said pattern recognition. Firstly, the characteristics and functions of social organizations are analyzed, and the definition of social organizations is given. Further, this paper studies the meaning and characteristics of social organization supervision, analyzes the failure of internal supervision of social organizations, and then determines the internal governance elements of social organizations. In addition, the basic steps of pattern recognition are given. Finally, multiple collaborative supervision patterns recognition within social organizations is realized based on data clustering algorithm. Experiments show that this method can improve the recognition accuracy of multiple collaborative supervision patterns and reduce the recognition time.

Evaluation of the seismogenic (M6+) nodes for the territory of Bulgaria and adjacent areas

In this study seismogenic nodes capable to generate earthquakes with magnitudes M ≥ 6 are identified for the territory of Bulgaria and adjacent areas. Definition of nodes is based on a morphostructural zonation. Pattern recognition algorithm Cora-3 is applied to identify the seismogenic nodes, characterized by specific geological and geophysical data. The pattern recognition method is trained on information for 30 seismic events with M ≥ 6 for the period 29 BC–2020, selected from historical and instrumental Bulgarian earthquake catalogues. As a result, 56 seismogenic nodes are recognized, most of them in southwestern Bulgaria.

Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS

Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy of fault diagnosis in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers. This newly proposed method not only solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC condition but also immensely facilitates the fault diagnosis of HVDC GIS.

The SYSTEM IDENTIFICATION OF FRIEND AND ENEMY USING NIGHT VISION CAMERA ON BATTLE ROBOT (CQB) USING PATTERN RECOGNITION METHOD

– Indonesian Army has the main task of protecting against enemy attacks, which include forest battles as well as urban battles. The obstacle faced today is that in the process of urban warfare operations, infiltration and hostage rescue in buildings are still less efficient and optimal. The robot is designed with a system that can identify friends and foes using a Night Vision camera and the Pattern Recognition method. Pattern recognition is a symbolic grouping automatically that is done by a computer to find out objects or patterns. The results of the Night vision camera test are able to detect human objects with a maximum distance of 6 meters. This night vision scope has a fairly large accuracy rate of 83%. And light intensity has an influence on the identification process because if the light intensity is more than 200 lux then the system is not able to identify the object.

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

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.