Pattern Recognition and Classifier Design of Bio-Signals based Interface in Human - Artificial Intelligence Interaction(HAII) Framework for Real Time Evaluation of Emotions

Actually as a question of pattern recognition, the research of cash recognition mainly focuses on three aspects: data acquisition, feature extraction and classifier design. In order to finish the real time recognition of Chinese cash, a fuzzy recognition intelligent system is presented in this paper. We use this arithmetic in the processing of information of cash and the recognition of the cash. Experiments has proved that this method can auto organize and auto study, and meet the need of complex system for the networks no linearity and high collateral.

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New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i2.9572 ◽

2016 ◽

Vol 6 (2) ◽

pp. 870

Author(s):

Ibrahim Saeh ◽

Wazir Mustafa ◽

Nasir Al-geelani

Keyword(s):

Decision Tree ◽

Real Time ◽

Security Evaluation ◽

Classifier Design ◽

Test Systems ◽

Tree Methods ◽

Static Security ◽

Classi Fication ◽

Decision Tree Methods ◽

Time Evaluation

This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design.

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Real-Time Pattern-Recognition of GPR Images with YOLO v3 Implemented by Tensorflow

Sensors ◽

10.3390/s20226476 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6476

Author(s):

Yuanhong Li ◽

Zuoxi Zhao ◽

Yangfan Luo ◽

Zhi Qiu

Keyword(s):

Artificial Intelligence ◽

Pattern Recognition ◽

Real Time ◽

High Precision ◽

Electromagnetic Waves ◽

Superior Performance ◽

Experimental Result ◽

Single Shot ◽

Time Pattern ◽

Bounding Boxes

Artificial intelligence (AI) is widely used in pattern recognition and positioning. In most of the geological exploration applications, it needs to locate and identify underground objects according to electromagnetic wave characteristics from the ground-penetrating radar (GPR) images. Currently, a few robust AI approach can detect targets by real-time with high precision or automation for GPR images recognition. This paper proposes an approach that can be used to identify parabolic targets with different sizes and underground soil or concrete structure voids based on you only look once (YOLO) v3. With the TensorFlow 1.13.0 developed by Google, we construct YOLO v3 neural network to realize real-time pattern recognition of GPR images. We propose the specific coding method for the GPR image samples in Yolo V3 to improve the prediction accuracy of bounding boxes. At the same time, K-means algorithm is also applied to select anchor boxes to improve the accuracy of positioning hyperbolic vertex. For some instances electromagnetic-vacillated signals may occur, which refers to multiple parabolic electromagnetic waves formed by strong conductive objects among soils or overlapping waveforms. This paper deals with the vacillating signal similarity intersection over union (IoU) (V-IoU) methods. Experimental result shows that the V-IoU combined with non-maximum suppression (NMS) can accurately frame targets in GPR image and reduce the misidentified boxes as well. Compared with the single shot multi-box detector (SSD), YOLO v2, and Faster-RCNN, the V-IoU YOLO v3 shows its superior performance even when implemented by CPU. It can meet the real-time output requirements by an average 12 fps detected speed. In summary, this paper proposes a simple and high-precision real-time pattern recognition method for GPR imagery, and promoted the application of artificial intelligence or deep learning in the field of the geophysical science.

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Real-Time Evaluation Algorithm of Human Body Movement in Football Training Robot

Mathematical Problems in Engineering ◽

10.1155/2021/9932737 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Ning Hu ◽

Shuhua Lin ◽

Jiayi Cai

Keyword(s):

Artificial Intelligence ◽

Real Time ◽

Vision System ◽

Body Movement ◽

Human Motion ◽

Wide Range ◽

Evaluation Algorithm ◽

Football Training ◽

Time Evaluation ◽

Soccer Robots

As one of the most challenging topics in the field of artificial intelligence, soccer robots are currently an important platform for humanoid robotics research. Its fields cover a wide range of fields, including robotics, artificial intelligence, and automatic control. Kinematics analysis and action planning are the key technologies in the research of humanoid soccer robots and are the basis for realizing basic actions such as walking. This article mainly introduces the real-time evaluation algorithm of human motion in the football training robot. The football robot action evaluation algorithm proposed here designs the angle and wheel speed of the football robot movement through the evaluation of the angular velocity and linear velocity of the center of mass of the robot. The overall system of the imitation human football robot is studied, including the mechanical system design. The design of the leg structure, the decision-making system based on the finite state machine, the robot vision system, and the image segmentation technology are introduced. The experimental results in this article show that the action of the football training robot model is very stable, the static rotation movement time is about 220 ms, and the fixed-point movement error is less than 1 cm, which fully meets the accuracy requirements of the large-space football robot.

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New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i2.pp870-876 ◽

2016 ◽

Vol 6 (2) ◽

pp. 870

Author(s):

Ibrahim Saeh ◽

Wazir Mustafa ◽

Nasir Al-geelani

Keyword(s):

Decision Tree ◽

Real Time ◽

Security Evaluation ◽

Classifier Design ◽

Test Systems ◽

Tree Methods ◽

Static Security ◽

Classi Fication ◽

Decision Tree Methods ◽

Time Evaluation

This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design.

Download Full-text