GPOGC: Gaussian Pigeon-Oriented Graph Clustering Algorithm for Social Networks Cluster

Sun, Yang; Yin, Shoulin; Li, Hang; Teng, Lin; Karim, Shahid

doi:10.1109/access.2019.2926816

Cited by 16 publications

(11 citation statements)

References 23 publications

(30 reference statements)

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“…In the landmark operator, the population quantity is too small in the later iteration period, which affects the optimization of the algorithm. In the stage of landmark operator, the population size can be slightly larger at the beginning of iteration [27][28][29], but it should gradually decrease as the iteration number increases. Logsig function has the characteristics of nonlinear reduction from 1 to 0, so Logsig function is introduced in this paper as the adaptive step of the pigeon number, and the improved landmark operator update formula is shown as follows:…”

Section: The Pigeon Number With Adaptive Step Lengthmentioning

confidence: 99%

A novel A* method fusing bio-inspired algorithm for mobile robot path planning

Sun¹,

Wang²

2018

ICST Transactions on Scalable Information Systems

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The path planning of mobile robot is to find an optimal collision-free path in time distance or space from the starting point to the target point in a given environment. With the popularization and application of mobile robots, if the efficiency of mobile robots path is not high, the working quality will be seriously affected. How to quickly plan an effective safe path is of great research significance and practical application value. Therefore, we propose a novel A* algorithm based on Bioinspired algorithm for mobile robot path planning. Firstly, the synchronous bidirectional A* algorithm is used to optimize the pheromone of ant colony algorithm, and the transition probability and pheromone update mechanism of ant colony algorithm are improved, so that the global optimization speed of the algorithm is faster and the path length of mobile robot is shortened. Furthermore, the static path is used to initialize the pigeon algorithm. Then, the improved pigeon algorithm is utilized to plan the local path of the mobile robot, and the simulated annealing criterion is introduced to solve the local optimal problem. The logarithmic S-type transfer function is adopted to optimize the step size of the pigeon number, so that the collision with the dynamic obstacles can be better avoided. Finally, a modified B-spline curve is used to smooth and re-plan the path. The simulation results show that the proposed method can realize path planning more effectively in complex dynamic environment.

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Section: The Pigeon Number With Adaptive Step Lengthmentioning

confidence: 99%

A novel A* method fusing bio-inspired algorithm for mobile robot path planning

Sun¹,

Wang²

2018

ICST Transactions on Scalable Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…e main processes include the emotion database establishment, the phonetic emotion features extraction, dimensionality reduction and features selection, and emotion classification and recognition. ere are many methods for music emotion recognition, which have achieved better effects, such as hidden Markov model (HMM) [6], artificial neural network (ANN) [7], Gaussian mixture model (GMM) [8], support vector machine (SVM) [9], K-nearest neighbor (KNN) [10], and maximum likelihood Bayesian classification [11,12]. However, the research objects (languages) are different, and there is no unified standard for the corpus database, so the recognition results are greatly different with each other.…”

Section: Introductionmentioning

confidence: 99%

A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT

Huang

Shafiq

et al. 2021

Mobile Information Systems

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The popularity of the Internet has brought the rapid development of artificial intelligence, affective computing, Internet of things (IoT), and other technologies. Particularly, the development of IoT provides more references for the realization of smart home. However, when people have achieved a certain amount of material satisfaction, they are more likely to want to communicate emotionally. Music contains a lot of emotion information. Music data is an important communication way between people and a better way to convey emotions. Therefore, it has become one of the most convenient and natural interactive ways expected by people in intelligent human-computer interaction. Traditional music emotion recognition methods have some demerits such as low recognition rate and time-consuming. So, we propose a generative adversarial network (GAN) model based on intelligent data analytics for music emotion recognition under IoT. Driven by the double-channel fusion strategy, the GAN can effectively extract the local and global features of the image or voice. Meanwhile, in order to increase the feature difference between the emotional voices, the feature data matrix of the Meyer frequency cepstrum coefficient of the music signals is transformed to improve the expression ability of the GAN. The experiment results show that the proposed model can effectively recognize the music emotion. Compared with other state-of-the-art approaches, the error recognition rate of proposed music music data recognition is greatly reduced. In terms of the accuracy, it exceeds 87% which is higher than that of other methods.

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“…Also, the network can be trained end-to-end, which makes the model calculation more efficient. In the numerous deep learning network structures, the convolutional neural network (CNN) is the most widely used (Sun et al, 2019 ; Teng and Li, 2019 ; Yin and Bi, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Student Behavior Recognition From Heterogeneous View Perception in Class Based on 3-D Multiscale Residual Dense Network for the Analysis of Case Teaching

Liu

Zhang

et al. 2021

Front. Neurorobot.

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The study of student behavior analysis in class plays a key role in teaching and educational reforms that can help the university to find an effective way to improve students' learning efficiency and innovation ability. It is also one of the effective ways to cultivate innovative talents. The traditional behavior recognition methods have many disadvantages, such as poor robustness and low efficiency. From a heterogeneous view perception point of view, it introduces the students' behavior recognition. Therefore, we propose a 3-D multiscale residual dense network from heterogeneous view perception for analysis of student behavior recognition in class. First, the proposed method adopts 3-D multiscale residual dense blocks as the basic module of the network, and the module extracts the hierarchical features of students' behavior through the densely connected convolutional layer. Second, the local dense feature of student behavior is to learn adaptively. Third, the residual connection module is used to improve the training efficiency. Finally, experimental results show that the proposed algorithm has good robustness and transfer learning ability compared with the state-of-the-art behavior recognition algorithms, and it can effectively handle multiple video behavior recognition tasks. The design of an intelligent human behavior recognition algorithm has great practical significance to analyze the learning and teaching of students in the class.

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GPOGC: Gaussian Pigeon-Oriented Graph Clustering Algorithm for Social Networks Cluster

Cited by 16 publications

References 23 publications

A novel A* method fusing bio-inspired algorithm for mobile robot path planning

A novel A* method fusing bio-inspired algorithm for mobile robot path planning

A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT

Student Behavior Recognition From Heterogeneous View Perception in Class Based on 3-D Multiscale Residual Dense Network for the Analysis of Case Teaching

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