scholarly journals Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Phat Nguyen Huu ◽  
Tan Phung Ngoc
Keyword(s):  
Gesture Recognition ◽  
Processing Speed ◽  
Industrial Applications ◽  
Recognition Algorithm ◽  
Robotic System ◽  
Support Vector ◽  
Histogram Of Oriented Gradient ◽  
Real Time Processing ◽  
Vector Machines ◽  
The Status

In this study, we propose the gesture recognition algorithm using support vector machines (SVM) and histogram of oriented gradient (HOG). Besides, we also use the CNN model to classify gestures. We approach and select techniques of applying problem controlling for the robotic system. The goal of the algorithm is to detect gestures with real-time processing speed, minimize interference, and reduce the ability to capture unintentional gestures. Static gesture controls are used in this study including on, off, increasing, and decreasing. Besides, it uses motion gestures including turning on the status switch and increasing and decreasing the volume. Results show that the algorithm is up to 99% accuracy with a 70-millisecond execution time per frame that is suitable for industrial applications.

scholarly journals An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

2018 ◽  
Vol 5 (9) ◽  
pp. 180160 ◽  
Author(s):  
Jian-hong Yang ◽  
Huai-ying Fang ◽  
Ren-cheng Zhang ◽  
Kai Yang
Keyword(s):  
Low Voltage ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Signal Loss ◽  
Current Waveform ◽  
Accurate Identification ◽  
Metallic Contacts ◽  
Vector Machines ◽  
Integral Characteristics

Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit. With capacitance coupling and a logarithmic detector, the high-frequency radiation characteristics of arc faults can be extracted. A rapid method for computing the current waveform slope characteristics of an arc fault provides another characteristic. Current waveform periodic integral characteristics can be extracted according to asymmetries of the arc faults. These three characteristics are used to develop a detection algorithm of arc faults based on multiinformation fusion and support vector machine learning models. The tests indicated that for series arc faults with single and combination loads and for parallel arc faults between metallic contacts and along carbonization paths, the recognition algorithm could effectively avoid the problems of crosstalk and signal loss during arc fault detection.

DETEKSI DAN REKOGNISI RAMBU-RAMBU LALU LINTAS DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Kurniawan Nur Ramadhani ◽  
M.Syahrul Mubarok ◽  
Agnes Dirgahayu Palit
Keyword(s):  
Support Vector Machines ◽  
Road Safety ◽  
Support Vector ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Histogram Of Oriented Gradient ◽  
Vector Machines ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

[Id]Kota-kota besar pasti tidak lepas dengan penggunaan rambu lalu lintas untuk meningkatkan keselamatan pengguna jalan. Rambu lalu lintas dirancang untuk pembantu pengemudi untuk mencapai tujuan mereka dengan aman, dengan menyediakan informasi rambu yang berguna. Meskipun demikian, hal yang tidak diinginkan dapat terjadi ketika informasi yang tersimpan pada rambu lalu lintas tidak diterima dengan baik pada pengguna jalan. Hal ini dapat menjadi masalah baru dalam keamanan berkendara. Dalam meminimalisasi masalah tersebut, dapat dibuat suatu teknologi yang mengembangkan sistem yang mengidentifikasi objek rambu lalu lintas secara otomatis yang dapat menjadi salah satu alternatif meningkatkan keselamatan berkendara, yaitu Traffic Sign Detection and Recognition (Sistem Deteksi dan Rekognisi Rambu Lalu Lintas). Sistem ini menggunakan menggunakan deteksi ciri warna dan bentuk. metode Histogram of Oriented Gradient (HOG) untuk ektraksi ciri citra bentuk, colour moment untuk ekstraksi warna dan Support Vector Machines (SVM) untuk mengklasifikasikan citra rambu lalu lintas. Sehingga dapat dianalisa bagaimana Sistem dapat mendeteksi dan mengenali citra yang merupakan objek rambu lalu lintas Diharapkan dengan adanya paduan metode-metode tersebut dapat membangun sistem deteksi dan rekognisi rambu lalu lintas, dan meningkat performansi sistem dalam mendeteksi dan mengenali rambu lalu lintas. Performansi yang dihasilkan dari sistem adalah 94.5946% menggunakan micro average f1-score.Kata kunci : ekstraksi ciri fitur, ekstraksi ciri warna, klasifikasi, HOG, colour moment, SVM, micro average f1-score.[En]The big cities must not be separated by the use of traffic signs to improve road safety. Traffic signs are designed to aide drivers to reach their destination safely, by providing useful information signs. Nonetheless, undesirable things can happen when information stored in the traffic signs are not received well on the road. It can be a new problem in road safety. In minimizing the problem, can be made of a technology that is developing a system that identifies an object traffic signs automatically which can be one alternative to improve driving safety, the Traffic Sign Detection and Recognition (Detection System and Traffic Sign Recognition). The system uses using the detection characteristics of colors and shapes. methods Histogram of Oriented Gradient (HOG) to extract image characteristic shape, color moment for the extraction of color and Support Vector Machines (SVM) to classify traffic signs image. So it can be analyzed how the system can detect and recognize the image which is the object of traffic signs Expected by the blend of these methods can build a system of detection and recognition of traffic signs, and increased system performance to detect and recognize traffic signs. Performasi generated in the system is 94.5946% using micro average f1-score.

Recognition algorithm for the disengagement of cement asphalt mortar based on dynamic responses of vehicles

2018 ◽  
Vol 233 (3) ◽  
pp. 270-282 ◽  
Author(s):  
Hui Shi ◽  
Zujun Yu ◽  
Hongmei Shi ◽  
Liqiang Zhu
Keyword(s):  
Support Vector Machines ◽  
Coupled Model ◽  
Recognition Algorithm ◽  
Dynamic Responses ◽  
Support Vector ◽  
Safe Operation ◽  
Cement Asphalt Mortar ◽  
Vector Machines ◽  
Track Substructure ◽  
Asphalt Mortar

Disengagement of emulsified cement asphalt mortar will increase the dynamic action between the vehicle and the track; as a consequence, the rate of cement asphalt mortar disengagement will increase further. This is a serious threat for the safe operation of high-speed railways and the service life of rail equipment. In this study, a vertical coupled model for the vehicle–China Railway Track System II-type slab track with cement asphalt disengagement was established. The cement asphalt mortar was divided into units in order to simulate the arbitrary length of disengagement. Under different conditions, the effects of the cement asphalt mortar disengagement on the dynamic characteristics of the coupled model were analyzed. The results show that when the length of disengagement exceeds 0.65 m under the condition of horizontal complete disengagement, the dynamic responses of the system increase much sharply than the condition of horizontal partly disengagement. Because of the difficulty in identifying defects in the track substructure, a novel method was proposed to rapidly identify the cement asphalt mortar disengagement based on the dynamic responses of the coupled system and particle swarm optimization–support vector machines. The feature vectors were extracted from the acceleration of the wheelset, which were used as training samples in support vector machines. The classification results show that the recognition algorithm based on the acceleration of the wheelset and support vector machines is effective. The location of the track plate with the cement asphalt mortar disengagement at lengths of 0.65 m, 1.3 m, and 1.95 m can be identified with an acceptable accuracy. The robustness of the proposed algorithm under different vehicle speeds, track spectrums, and signal–noise ratios was verified. Recognition of defects in the track substructure using sensors mounted on in-service vehicles has the potential to provide a valuable tool for ensuring the safe operation of railways and for developing a maintenance plan.

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