scholarly journals Music Waveform Analysis Based on SOM Neural Network and Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xinmei Zhang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Audio Signal ◽  
The Self ◽  
Waveform Analysis ◽  
Self Organizing Map ◽  
Matching Method ◽  
Emotion Model ◽  
Som Neural Network ◽  
First Time

Music is an indispensable part of our life and study and is one of the most important forms of multimedia applications. With the development of deep learning and neural network in recent years, how to use cutting-edge technology to study and apply music has become a research hotspot. Music waveform is not only the main form of music frequency but also the basis of music feature extraction. This paper first designs a method of note extraction based on the fast Fourier transform principle of the audio signal packet route under the self-organizing map (SOM neural network) which can accurately extract the musical features of the note, such as amplitude, loudness, period, and so on. Secondly, the audio segments are divided into summary by adding window moving matching method, and the music features such as amplitude, loudness, and period of each bar are obtained according to the performance of audio signal in each bar. Finally, according to the similarity of the audio music theory of the adjacent summary of each bar, the audio segments are divided, and the music features of each segment are obtained. The traditional recurrent neural network (RNN) is improved, and the SOM neural network is used to recognize the audio emotion features. The final experimental results show that the proposed method based on SOM neural network and big data can effectively extract and analyze music waveform features. Compared with previous studies, this paper creatively proposed a new algorithm, which can more accurately and quickly extract and analyze the data sound waveform, and used SOM neural network to analyze the emotion model contained in music for the first time.

Task Allocation of Multiple Robotic Fishes Based on Self-Organizing Map Neural Network

2014 ◽  
Vol 563 ◽  
pp. 308-311 ◽  
Author(s):  
Yu Lian Jiang
Keyword(s):  
Neural Network ◽  
Task Allocation ◽  
Robotic Fish ◽  
The Self ◽  
Self Organizing Map ◽  
Ball Game ◽  
Target Task ◽  
Water Polo ◽  
Som Neural Network ◽  
Self Organizing

For a water polo ball game there are multiple water polos and multiple robotic fishes in each team, seeking a reasonable task allocation plan is the key point to win the game. To resolve the problem, this paper proposed a multi-target task allocation method based on the Self-organizing map (SOM) neural network. This method takes the position of the water polos as the input vector, competes and compares the position of the water polos and robotic fishes, outputs the corresponding robotic fish of each water polo. The robotic fish will move toward the target water polo when the weight was adjusted, and will finally reach the target water polo. Simulations show that the score of the team using this method is higher than another team. The results prove the correctness and reliability of this method.

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3296
Author(s):  
Hongwei Tang ◽  
Anping Lin ◽  
Wei Sun ◽  
Shuqi Shi
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Task Assignment ◽  
Dynamic Environments ◽  
Self Organizing Map ◽  
Cooperative Search ◽  
Dynamic Task ◽  
Locking Mechanism ◽  
Som Neural Network ◽  
Multi Robot

The methods of task assignment and path planning have been reported by many researchers, but they are mainly focused on environments with prior information. In unknown dynamic environments, in which the real-time acquisition of the location information of obstacles is required, an integrated multi-robot dynamic task assignment and cooperative search method is proposed by combining an improved self-organizing map (SOM) neural network and the adaptive dynamic window approach (DWA). To avoid the robot oscillation and hovering issue that occurs with the SOM-based algorithm, an SOM neural network with a locking mechanism is developed to better realize task assignment. Then, in order to solve the obstacle avoidance problem and the speed jump problem, the weights of the winner of the SOM are updated by using an adaptive DWA. In addition, the proposed method can search dynamic multi-target in unknown dynamic environment, it can reassign tasks and re-plan searching paths in real time when the location of the targets and obstacle changes. The simulation results and comparative testing demonstrate the effectiveness and efficiency of the proposed method.

scholarly journals SUSSOL—Using Artificial Intelligence for Greener Solvent Selection and Substitution

Molecules ◽  
2020 ◽  
Vol 25 (13) ◽  
pp. 3037 ◽  
Author(s):  
Hannes Sels ◽  
Herwig De Smet ◽  
Jeroen Geuens
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Physical Properties ◽  
The Self ◽  
Self Organizing Map ◽  
Solvent Selection ◽  
Sustainable Solvents ◽  
Cluster A ◽  
User Friendly ◽  
Intuitive Sense

Solvents come in many shapes and types. Looking for solvents for a specific application can be hard, and looking for green alternatives for currently used nonbenign solvents can be even harder. We describe a new methodology for solvent selection and substitution, by applying Artificial Intelligence (AI) software to cluster a database of solvents based on their physical properties. The solvents are processed by a neural network, the Self-organizing Map of Kohonen, which results in a 2D map of clusters. The resulting clusters are validated both chemically and statistically and are presented in user-friendly visualizations by the SUSSOL (Sustainable Solvents Selection and Substitution Software) software. The software helps the user in exploring the solvent space and in generating and evaluating a list of possible alternatives for a specific solvent. The alternatives are ranked based on their safety, health, and environment scores. Cases are discussed to demonstrate the possibilities of our approach and to show that it can help in the search for more sustainable and greener solvents. The SUSSOL software makes intuitive sense and in most case studies, the software confirms the findings in literature, thus providing a sound platform for selecting the most sustainable solvent candidate.

CENTROID NEURAL NETWORK WITH WEIGHTED FEATURES

2009 ◽  
Vol 18 (08) ◽  
pp. 1353-1367 ◽  
Author(s):  
DONG-CHUL PARK
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Objective Function ◽  
Lagrange Multiplier ◽  
Synthetic Data ◽  
Feature Weighting ◽  
The Self ◽  
Self Organizing Map ◽  
Data Set ◽  
Self Organizing

A Centroid Neural Network with Weighted Features (CNN-WF) is proposed and presented in this paper. The proposed CNN-WF is based on a Centroid Neural Network (CNN), an effective clustering tool that has been successfully applied to various problems. In order to evaluate the importance of each feature in a set of data, a feature weighting concept is introduced to the Centroid Neural Network in the proposed algorithm. The weight update equations for CNN-WF are derived by applying the Lagrange multiplier procedure to the objective function constructed for CNN-WF in this paper. The use of weighted features makes it possible to assess the importance of each feature and to reject features that can be considered as noise in data. Experiments on a synthetic data set and a typical image compression problem show that the proposed CNN-WF can assess the importance of each feature and the proposed CNN-WF outperforms conventional algorithms including the Self-Organizing Map (SOM) and CNN in terms of clustering accuracy.

scholarly journals A HYBRID KANSEI ENGINEERING SYSTEM USING THE SELF-ORGANIZING MAP NEURAL NETWORK

2016 ◽  
Vol 2 (1) ◽  
pp. 23-38
Author(s):  
C.S. Teh ◽  
C.P. Lim
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Product Design ◽  
The Self ◽  
Design Parameters ◽  
Decision Making Process ◽  
Engineering System ◽  
Self Organizing Map ◽  
Kansei Engineering ◽  
Self Organizing

Kansei Engineering (KE), a technology founded in Japan initially for product design, translates human feelings into design parameters. Although various intelligent approaches to objectively model human functions and the relationships with the product design decisions have been introduced in KE systems, many of the approaches are not able to incorporate human subjective feelings and preferences into the decision-making process. This paper proposes a new hybrid KE system that attempts to make the machine-based decision-making process closely resembles the real-world practice. The proposed approach assimilates human perceptive and associative abilities into the decision-making process of the computer. A number of techniques based on the Self-Organizing Map (SOM) neural network are employed in the backward KE system to reveal the underlying data structures that are involved in the decision-making process. A case study on interior design is presented to evaluate the efficacy of the proposed approach. The results obtained demonstrate the effectiveness of the proposed approach in developing an intelligent KE system which is able to combine human feelings and preferences into its decision making process.

scholarly journals Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Magnus Johnsson ◽  
Christian Balkenius
Keyword(s):  
Neural Network ◽  
Haptic Perception ◽  
Cell Structure ◽  
The Self ◽  
Robot Hand ◽  
Self Organizing Map ◽  
Training Set ◽  
Input Space ◽  
Growing Cell ◽  
Self Organizing

We have implemented and compared four biologically motivated self-organizing haptic systems based on proprioception. All systems employ a 12-d.o.f. anthropomorphic robot hand, the LUCS Haptic Hand 3. The four systems differ in the kind of self-organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). The systems were trained and tested with 10 different objects of different sizes from two different shape categories. The generalization abilities of the systems were tested with 6 new objects. The systems showed good performance with the objects from the training set as well as in the generalization experiments. Thus the systems could discriminate individual objects, and they clustered the activities into small cylinders, large cylinders, small blocks, and large blocks. Moreover, the self-organizing ANNs were also organized according to size. The GCS-DN system also evolved disconnected networks representing the different clusters in the input space (small cylinders, large cylinders, small blocks, large blocks), and the generalization samples activated neurons in a proper subnetwork in all but one case.

scholarly journals Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs

2021 ◽  
Vol 22 (9) ◽  
pp. 4443
Author(s):  
Viktor Drgan ◽  
Benjamin Bajželj
Keyword(s):  
Neural Network ◽  
Development Process ◽  
The Self ◽  
Training Algorithms ◽  
Drug Development Process ◽  
Self Organizing Map ◽  
High Ability ◽  
Good Separation ◽  
Propagation Algorithm ◽  
Self Organizing

The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Therefore, the evaluation of the hepatotoxic potential of drugs is an important part of the drug development process. The aim of this work was to evaluate the relative abilities of different supervised self-organizing algorithms in classifying the hepatotoxic potential of drugs. Two modifications of standard counter-propagation training algorithms were proposed to achieve good separation of clusters on the self-organizing map. A series of optimizations were performed using genetic algorithm to select models developed with counter-propagation neural networks, X-Y fused networks, and the two newly proposed algorithms. The cluster separations achieved by the different algorithms were evaluated using a simple measure presented in this paper. Both proposed algorithms showed a better formation of clusters compared to the standard counter-propagation algorithm. The X-Y fused neural network confirmed its high ability to form well-separated clusters. Nevertheless, one of the proposed algorithms came close to its clustering results, which also resulted in a similar number of selected models.

scholarly journals Defect identification method for poplar veneer based on progressive growing generated adversarial network and MASK R-CNN model

BioResources ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 3041-3052
Author(s):  
Kai Hu ◽  
Baojin Wang ◽  
Yi Shen ◽  
Jieru Guan ◽  
Yi Cai
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Surface Defects ◽  
Learning Strategy ◽  
Back Propagation ◽  
Self Organizing Map ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Main Production ◽  
Som Neural Network

As the main production unit of plywood, the surface defects of veneer seriously affect the quality and grade of plywood. Therefore, a new method for identifying wood defects based on progressive growing generative adversarial network (PGGAN) and the MASK R-CNN model is presented. Poplar veneer was mainly studied in this paper, and its dead knots, live knots, and insect holes were identified and classified. The PGGAN model was used to expand the dataset of wood defect images. A key ideal employed the transfer learning in the base of MASK R-CNN with a classifier layer. Lastly, the trained model was used to identify and classify the veneer defects compared with the back- propagation (BP) neural network, self-organizing map (SOM) neural network, and convolutional neural network (CNN). Experimental results showed that under the same conditions, the algorithm proposed in this paper based on PGGAN and MASK R-CNN and the model obtained through the transfer learning strategy accurately identified the defects of live knots, dead knots, and insect holes. The accuracy of identification was 99.05%, 97.05%, and 99.10%, respectively.

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