scholarly journals Underwater Target Noise Recognition and Classification Technology based on Multi-Classes Feature Fusion

2020 ◽  
Vol 38 (2) ◽  
pp. 366-376
Author(s):  
Shaokang Zhang ◽  
Chao Wang ◽  
Qindong Sun
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Classification Model ◽  
Underwater Acoustic ◽  
Model Based ◽  
Acoustic Target ◽  
Underwater Target ◽  
Level Fusion

As one of the main signal sources of underwater acoustic target recognition, the target noise signal is difficult to characterize the characteristics of the target from clearly comparing with the multi-sensor detection technology, which may lead to lower recognition rate and higher false alarm rate and seriously restricts the function of underwater acoustic detection system. In order to solve this problem, a multi-layers LSTM underwater acoustic target noise feature extraction model is established by using the long short term memory network. The information features such as time-domain envelope of target noise, Demon line spectrum and Mel frequency cepstrum coefficient are extracted, and a subset of multi-classes features is constructed. On this basis, the feature level fusion recognition and classification model based on the multi-classes features subset and the decision level fusion recognition and classification model based on the D-S evidence theory are established, and the above-mentioned models are tested by using the sample database. The difference of classification result between the multi-classes feature fusion and the single class feature recognition classification is compared, and the above model is tested and verified by using the relevant test data of port basin verification experiment. The correlation results show that the present intelligent recognition and classification method of underwater target noise based on the multi-classes feature fusion is more robust, and the recognition rate and false alarm rate of underwater target are better than those of single category feature discrimination method.

scholarly journals Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1429
Author(s):  
Gang Hu ◽  
Kejun Wang ◽  
Liangliang Liu
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Target Feature ◽  
Underwater Acoustic ◽  
Radiated Noise ◽  
Automatic Feature Extraction ◽  
Acoustic Target ◽  
And Cluster Analysis ◽  
Underwater Target ◽  
The One

Facing the complex marine environment, it is extremely challenging to conduct underwater acoustic target feature extraction and recognition using ship-radiated noise. In this paper, firstly, taking the one-dimensional time-domain raw signal of the ship as the input of the model, a new deep neural network model for underwater target recognition is proposed. Depthwise separable convolution and time-dilated convolution are used for passive underwater acoustic target recognition for the first time. The proposed model realizes automatic feature extraction from the raw data of ship radiated noise and temporal attention in the process of underwater target recognition. Secondly, the measured data are used to evaluate the model, and cluster analysis and visualization analysis are performed based on the features extracted from the model. The results show that the features extracted from the model have good characteristics of intra-class aggregation and inter-class separation. Furthermore, the cross-folding model is used to verify that there is no overfitting in the model, which improves the generalization ability of the model. Finally, the model is compared with traditional underwater acoustic target recognition, and its accuracy is significantly improved by 6.8%.

Combination of gated recurrent unit and Network in Network for underwater acoustic target recognition

2021 ◽  
Vol 263 (6) ◽  
pp. 486-492
Author(s):  
Shuang Yang ◽  
Xiangyang Zeng
Keyword(s):  
Information Acquisition ◽  
Target Recognition ◽  
Recognition Rate ◽  
Temporal Correlation ◽  
Feedback Mechanism ◽  
Underwater Acoustic ◽  
Acoustic Information ◽  
Proposed Model ◽  
Acoustic Target ◽  
Gated Recurrent Unit

Underwater acoustic target recognition is an important part of underwater acoustic signal processing and an important technical support for underwater acoustic information acquisition and underwater acoustic information confrontation. Taking into account that the gated recurrent unit (GRU) has an internal feedback mechanism that can reflect the temporal correlation of underwater acoustic target features, a model with gated recurrent unit and Network in Network (NIN) is proposed to recognize underwater acoustic targets in this paper. The proposed model introduces NIN to compress the hidden states of GRU while retaining the original timing characteristics of underwater acoustic target features. The higher recognition rate and faster calculation speed of the proposed model are demonstrated with experiments for raw underwater acoustic signals comparing with the multi-layer stacked GRU model.

An Intelligent Approach for Intrusion Detection using Convolutional Neural Network

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
P. Manoj Kumar ◽  
M. Parvathy ◽  
C. Abinaya Devi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
False Alarm Rate ◽  
Real Time ◽  
Detection Rate ◽  
Performance Metrics ◽  
Classification Model ◽  
Real Time Traffic

Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.

scholarly journals An Intelligent Rockburst Prediction Model Based on Scorecard Methodology

Minerals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1294
Author(s):  
Honglei Wang ◽  
Zhenlei Li ◽  
Dazhao Song ◽  
Xueqiu He ◽  
Aleksei Sobolev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
False Alarm ◽  
False Alarm Rate ◽  
Learning Model ◽  
Underground Engineering ◽  
Risk Levels ◽  
Model Based ◽  
Machine Learning Model ◽  
Sample Category

Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were constructed using ChiMerge, evidence weight theory, and the logistic regression algorithm. An intelligent rockburst prediction model based on scorecard methodology (IRPSC) was obtained by integrating the three scorecards. The effects of hazard sample category weights on the missed alarm rate, false alarm rate, and accuracy of the IRPSC were analyzed. Results show that the accuracy, false alarm rate, and missed alarm rate of the IRPSC for rockburst prediction in riverside hydropower stations are 75%, 12.5%, and 12.5%, respectively. Setting higher hazard sample category weights can reduce the missed alarm rate of IRPSC, but it will lead to a higher false alarm rate. The IRPSC can adaptively adjust the threshold and weight value of the indicator and convert the abstract machine learning model into a tabular form, which overcomes the commonly black box problems of machine learning model, as well as is of great significance to the application of machine learning in rockburst risk prediction.

A Blind Detection Method of Video Steganography Based on Feature Fusion

2015 ◽  
Vol 727-728 ◽  
pp. 867-871
Author(s):  
Wan Qing Wang ◽  
Deng Yin Zhang ◽  
Guang Shuai Shi
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Detection Method ◽  
Feature Fusion ◽  
Research Area ◽  
Blind Detection ◽  
Video Steganography ◽  
Lower False Alarm Rate ◽  
Steganalysis Method

Due to lack of generalanalysis method in video digital steganalysis research area, a blind detection method which is based onfeature fusion aimed at the video steganography is proposed in this paper.Compared with special steganalysis method, the method has better detection rate,lower false alarm rate, and more extensive applicability.

scholarly journals An Alarm Method for a Loose Parts Monitoring System

2012 ◽  
Vol 19 (4) ◽  
pp. 753-761 ◽  
Author(s):  
Yanlong Cao ◽  
Yuanfeng He ◽  
Huawen Zheng ◽  
Jiangxin Yang
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Monitoring System ◽  
Power Plants ◽  
Detection Rate ◽  
Recognition Rate ◽  
Support Vector ◽  
Linear Predictive Coding ◽  
Loose Parts ◽  
The Impact

In order to reduce the false alarm rate and missed detection rate of a Loose Parts Monitoring System (LPMS) for Nuclear Power Plants, a new hybrid method combining Linear Predictive Coding (LPC) and Support Vector Machine (SVM) together to discriminate the loose part signal is proposed. The alarm process is divided into two stages. The first stage is to detect the weak burst signal for reducing the missed detection rate. Signal is whitened to improve the SNR, and then the weak burst signal can be detected by checking the short-term Root Mean Square (RMS) of the whitened signal. The second stage is to identify the detected burst signal for reducing the false alarm rate. Taking the signal's LPC coefficients as its characteristics, SVM is then utilized to determine whether the signal is generated by the impact of a loose part. The experiment shows that whitening the signal in the first stage can detect a loose part burst signal even at very low SNR and thusly can significantly reduce the rate of missed detection. In the second alarm stage, the loose parts' burst signal can be distinguished from pulse disturbance by using SVM. Even when the SNR is −15 dB, the system can still achieve a 100% recognition rate

Research On Exercise Fatigue Estimation Method of Pilates Rehabilitation Based On ECG and sEMG Feature Fusion

2021 ◽  
Author(s):  
Dujuan Li ◽  
Caixia Chen
Keyword(s):  
Feature Fusion ◽  
Recognition Rate ◽  
Estimation Method ◽  
Classification Model ◽  
Support Vector ◽  
Ecg Signal ◽  
Data Types ◽  
Semg Signal ◽  
Linear Discriminant ◽  
Fatigue Estimation

Abstract Purpose. Fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury in Pilates rehabilitation. Surface electromyography (sEMG) is used to estimate fatigue with low and unstable recognition rates. To improve the rate, this paper fused electrocardiogram (ECG) signal and sEMG signal under three different states, and the classification model of the improved proved particle swarm optimization support vector machine (IPSO-SVM) algorithm was established. Methods. Twenty subjects performed 150 minutes of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. After necessary preprocessing, the IPSO-SVM classification model based on feature fusion was established to identify three different fatigue states (relaxed, transition, and tired). The model effects of different classification algorithms and different fused data types were compared. Results. Compared with common physiological signal classification methods such as BP neural network algorithm(BPNN), K-nearest neighbor(KNN), and Linear discriminant analysis(LDA), IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion. The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. This study establishes technical support for establishing relevant man-machine devices and improving the safety of Pilates rehabilitation.

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