scholarly journals Underwater Moving Target Classification Using Multilayer Processing of Active Sonar System

2019 ◽  
Vol 9 (21) ◽  
pp. 4617
Author(s):  
Iksu Seo ◽  
Seongweon Kim ◽  
Youngwoo Ryu ◽  
Jungyong Park ◽  
Dong Seog Han
Keyword(s):  
Ambient Noise ◽  
Roc Curves ◽  
Detection Algorithm ◽  
Classification Performance ◽  
Probability Of Detection ◽  
Support Vector ◽  
High Signal ◽  
Classification Rate ◽  
Active Sonar ◽  
Sonar System

The task of detecting and classifying highly maneuverable and unidentified underwater targets in complex environments is significant in active sonar systems. Previous studies have applied many detection schemes to this task using signals above a preset threshold to separate targets from clutter; this is because a high signal-to-noise ratio (SNR) target has sufficient feature vector components to be separated out. However, in real environments, the received target return’s SNR is not always above the threshold. Therefore, a target detection algorithm is needed for varied target SNR conditions. When the clutter energy is too strong, false detection can occur, and the probability of detection is reduced due to the weak target signature. Furthermore, since a long pulse repetition interval is used for long-range detection and ambient noise tends to be high, classification processing for each ping is needed. This paper proposes a multilayer classification algorithm applicable to all signals in real underwater environments above the noise level without thresholding and verifies the algorithm’s classification performance. We obtained a variety of experimental data by using a real underwater target and a hull-mounted active sonar system operated on Korean naval ships in the East Sea, Korea. The detection performance of the proposed algorithm was evaluated in terms of the classification rate and false alarm rate as a function of the SNR. Since experimental environment data, including the sea state, target maneuvering patterns, and sound speed, were available, we selected 1123 instances of ping data from the target over all experiments and randomly selected 1000 clutters based on the distribution of clutters for each ping. A support vector machine was employed as the classifier, and 80% of the data were selected for training, leaving the remaining data for testing. This process was carried out 1000 times. For the performance analysis and discussions, samples of scatter diagrams and feature characteristics are shown and classification tables and receiver operation characteristic (ROC) curves are presented. The results show that the proposed algorithm is effective under a variety of target strengths and ambient noise levels.

Visualizing Probability of Detection to Aid Sonar Operator Performance

2017 ◽  
Vol 61 (1) ◽  
pp. 302-306 ◽  
Author(s):  
Kingsley I. Fletcher ◽  
Megan L. Bartlett ◽  
Susan J. Cockshell ◽  
Jason S. McCarley
Keyword(s):  
Spatial Attention ◽  
Target Detection ◽  
Response Bias ◽  
Human Performance ◽  
Detection Task ◽  
Probability Of Detection ◽  
Uncertainty Range ◽  
Active Sonar ◽  
Operator Performance ◽  
Sonar System

This study tested whether the display of rings indicating the probability of target detection would improve human performance on a simulated active sonar detection task. Participants viewed a series of simulated sonar returns and decided whether a target was present or not. Participants performed the task both with and without uncertainty range rings that indicated 90% and 10% detectability ranges. The probability of detection rings did not improve the overall ability of participants to distinguish targets from noise, but did appear to influence response bias and spatial attention. These results suggest that displaying probability of detection may not be an effective way of improving the performance of sonar system operators.

The recognition of plastic bottle using linear multi hierarchical SVM classifier

2021 ◽  
pp. 1-14
Author(s):  
LiHua Cai ◽  
Jin Cao ◽  
MingQiang Wang ◽  
Ta Zhou ◽  
HaiFeng Fang
Keyword(s):  
Loss Function ◽  
Deep Structure ◽  
Principal Component ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Input Matrix ◽  
Classification Rate ◽  
Pet Bottles ◽  
Combination Methods

Both classification rate and accuracy are crucial for the recyclable PET bottles, and the existing combination methods of SVM all simply use SVM as the unit classifier, ignoring the improvement of SVM’s classification performance in the training process of deep learning. A linear multi hierarchical deep structure based on Support Vector Machine (SVM) is proposed to cover this problem. A novel definition of the input matrix in each layer enhances the optimization of Lagrange multipliers in Sequential Minimal Optimization (SMO) algorithm, thus the datapoint in maximum interval of SVM hyperplane could be recognized, improving the classification performance of SVM classifier in this layer. The loss function defined in this paper could control the depth of Linear Multi Hierarchical SVM (LMHSVM), the generalization parameters are added in the loss function and the input matrix to enhance the generalization performance of LMHSVM. The process of creating Bottle dataset by Histogram of Oriented Gradient (HOG) and Principal Component Analysis (PCA) is introduced meanwhile, reducing the data size of bottles. Experiments are conducted on LMHSVM and multiple typical classification algorithms with Bottle dataset and UCI datasets, the results indicated that LMHSVM has excellent classification performances than FNN classifier, LIBSVM (Gaussian) and GFS-AdaBoost-C in KEEL.

scholarly journals Classifying smoke in laparoscopic videos using SVM

2017 ◽  
Vol 3 (2) ◽  
pp. 191-194 ◽  
Author(s):  
Tamer Abdulbaki Alshirbaji ◽  
Nour Aldeen Jalal ◽  
Lars Mündermann ◽  
Knut Möller
Keyword(s):  
Detection Algorithm ◽  
Classification Performance ◽  
Visual Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Smoke Detection ◽  
Lighting Conditions ◽  
Event Based ◽  
Laparoscopic Videos ◽  
Smoke Removal

AbstractSmoke in laparoscopic videos usually appears due to the use of electrocautery when cutting or coagulating tissues. Therefore, detecting smoke can be used for event-based annotation in laparoscopic surgeries by retrieving the events associated with the electrocauterization. Furthermore, smoke detection can also be used for automatic smoke removal. However, detecting smoke in laparoscopic video is a challenge because of the changeability of smoke patterns, the moving camera and the different lighting conditions. In this paper, we present a video-based smoke detection algorithm to detect smoke of different densities such as fog, low and high density in laparoscopic videos. The proposed method depends on extracting various visual features from the laparoscopic images and providing them to support vector machine (SVM) classifier. Features are based on motion, colour and texture patterns of the smoke. We validated our algorithm using experimental evaluation on four laparoscopic cholecystectomy videos. These four videos were manually annotated by defining every frame as smoke or non-smoke frame. The algorithm was applied to the videos by using different feature combinations for classification. Experimental results show that the combination of all proposed features gives the best classification performance. The overall accuracy (i.e. correctly classified frames) is around 84%, with the sensitivity (i.e. correctly detected smoke frames) and the specificity (i.e. correctly detected non-smoke frames) are 89% and 80%, respectively.

Particle Swarm Optimisation-Support Vector Machine Optimised by Association Rules for Detecting Factors Inducing Heart Diseases

2017 ◽  
Vol 26 (3) ◽  
pp. 573-583
Author(s):  
Lu Wei-Jia ◽  
Ma Liang ◽  
Chen Hao
Keyword(s):  
Support Vector Machine ◽  
Association Rules ◽  
Heart Diseases ◽  
Particle Swarm ◽  
Detection Algorithm ◽  
Classification Performance ◽  
Particle Swarm Optimisation ◽  
Support Vector ◽  
Classification Algorithms ◽  
Data Set

AbstractExisting systems for diagnosing heart diseases are time consuming, expensive, and error prone. Aiming at this, a detection algorithm for factors inducing heart diseases based on a particle swarm optimisation-support vector machine (PSO-SVM) optimised by association rules (ARs) was proposed. Firstly, AR was used to select features from a disease data set so as to train feature sets. Then, PSO-SVM was used to classify training and testing sets, and then the factors inducing heart diseases were analysed. Finally, the effectiveness and reliability of the proposed algorithm was verified by experiments on the UCI Cleveland data set with confidence as the index. The experimental results showed that females have less risk of having a heart attack than males. Irrespective of gender, once diagnosed with chest pain without symptoms and angina caused by exercise, people are more likely to suffer from heart disease. Moreover, compared with another two advanced classification algorithms, the proposed algorithm showed better classification performance and therefore can be used as a powerful tool to help doctors diagnose and treat heart diseases.

Robust multimodal biometric authentication algorithms using fingerprint, iris and voice features fusion

2021 ◽  
Vol 40 (1) ◽  
pp. 647-672
Author(s):  
Mohamed S. El_Tokhy
Keyword(s):  
Feature Fusion ◽  
Boundary Energy ◽  
Roc Curves ◽  
Point Of View ◽  
Support Vector ◽  
Svm Classifier ◽  
Biometric Authentication ◽  
Classification Rate ◽  
Computational Point ◽  
Practical Applications

Development of a robust triple multimodal biometric approach for human authentication using fingerprint, iris and voice biometric is the main objective of this manuscript. Accordingly, three essential algorithms for biometric authentication are presented. The extracted features from these multimodals are combined via feature fusion center (FFC) and feature scores. These features are trained through artificial neural network (ANN) and support vector machine (SVM) classifiers. The first algorithm depends on boundary energy method (BEM) extracted features from fingerprint, normalized combinational features from iris and dimensionality reduction methods (DRM) from voice using sum/average FFC. The second proposed algorithm uses extracted features from zoning method of fingerprint, SIFT of iris and higher order statistics (HOS) of voice signals. The third proposed algorithm consists of extracted features from zoning method for fingerprint, SIFT from iris and DRM from voice signals. Classification accuracy of implemented algorithms is estimated. Comparison between proposed algorithms is introduced in terms of equal error rate (EER) and ROC curves. The experimental results confirm superiority of second proposed algorithm which achieves a classification rate of 100% using SVM classifier and sum FFC. From computational point of view, the first algorithm consumes the lowest time using SVM classifier. On other hand, the lowest EER is achieved by first proposed algorithm for extracted features from Karhunen-Loeve transform (KLT) method of DRM. Additionally, the lowest ROC curves are accomplished respectively for extracted features from multidimensional scaling (MDS), generated ARMA synthesis and Isomap features. Their accuracy is improved with SVM. Also, the sum FFC introduces efficient results compared to average FFC. These algorithms have the advantages of robustness and the strength of selecting unimodal, double and triple biometric authentication. The obtained results accomplish a remarkable accuracy for authentication and security within multi practical applications.

scholarly journals A Hybrid Feature Selection and Extraction Methods for Sleep Apnea Detection Using Bio-Signals

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4323 ◽  
Author(s):  
Xilin Li ◽  
Sai Ho Ling ◽  
Steven Su
Keyword(s):  
Feature Selection ◽  
Sleep Apnea ◽  
Regularization Parameter ◽  
Roc Curves ◽  
Extraction Methods ◽  
Classification Performance ◽  
Hill Climbing ◽  
Support Vector ◽  
Svm Classifier ◽  
Increased Risk

People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, the Physionet Apnea Database was used to obtain various features. Electrocardiography (ECG), oxygen saturation (SaO2), airflow, abdominal, and thoracic signals were used to provide various frequency-, time-domain and non-linear features (n = 87). To analyse the significance of these features, firstly, two evaluation measures, the rank-sum method and the analysis of variance (ANOVA) were used to evaluate the significance of the features. These features were then classified according to their significance. Finally, different class feature sets were presented as inputs for an SVM classifier to detect the onset of SA. The hill-climbing feature selection algorithm and the k-fold cross-validation method were applied to evaluate each classification performance. Through the experiments, we discovered that the best feature set (including the top-five significant features) obtained the best classification performance. Furthermore, we plotted receiver operating characteristic (ROC) curves to examine the performance of the SVM, and the results showed the SVM with Linear kernel (regularization parameter = 1) outperformed other classifiers (area under curve = 95.23%, sensitivity = 94.29%, specificity = 96.17%). The results confirm that feature subsets based on multiple bio-signals have the potential to identify patients with SA. The use of a smaller subset avoids dimensionality problems and reduces the computational load.

scholarly journals Vibration-based Processing and Classification Method for Oil Well-testing Data from Downhole Pressure Gauges

2020 ◽  
Vol 206 ◽  
pp. 01024
Author(s):  
Feng Xin ◽  
Li Shaohui ◽  
Feng Qiang ◽  
Liu Shugui
Keyword(s):  
Classification Performance ◽  
Petroleum Exploration ◽  
Classification Method ◽  
Support Vector ◽  
High Signal ◽  
Oil Well ◽  
Well Testing ◽  
Identification Rate ◽  
Time Frequency ◽  
Testing Data

During petroleum exploration and exploitation, the oil well-testing data collected by pressure gauges are used for monitoring the well condition and recording the reservoir performance. However, due to the large number of the collected data, the classification of this large volume of data requires a previous processing for the removal of noise and outliers. It is impractical to partition and process these data manually. Vibration-based features reflect geological properties and offer a promising option to fulfil such requirements. Based on the 75 on-site measured samples, the time-frequency-domain features are extracted and the classification performance of three classical classifiers are investigated. Then the downhole data processing and classification method is present by analysing the cross interaction of different types of data features and different classification mechanism. Several feature combinations are tested to establish a processing flow that can efficiently remove the noise and preserve the shape of curves, high signal to noise ratio rates, with minimum absolute errors. The results show that optimal multi-feature combination can achieve the highest working stage identification rate of 72%, the parameters optimized support vector machine can achieve the better classification performance than other listed classifiers. This paper provides a theoretical study for the data denoising and processing to enhance the working stage classification accuracy.

scholarly journals Active Sonar Target Classification Method Based on Fisher’s Dictionary Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10635
Author(s):  
Tongjing Sun ◽  
Jiwei Jin ◽  
Tong Liu ◽  
Jun Zhang
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Classification Performance ◽  
Classification Method ◽  
Support Vector ◽  
Target Classification ◽  
Active Sonar ◽  
Reconstructed Signal ◽  
Sparse Representation Classification ◽  
Value Decomposition

The marine environment is complex and changeable, and the interference of noise and reverberation seriously affects the classification performance of active sonar equipment. In particular, when the targets to be measured have similar characteristics, underwater classification becomes more complex. Therefore, a strong, recognizable algorithm needs to be developed that can handle similar feature targets in a reverberation environment. This paper combines Fisher’s discriminant criterion and a dictionary-learning-based sparse representation classification algorithm, and proposes an active sonar target classification method based on Fisher discriminant dictionary learning (FDDL). Based on the learning dictionaries, the proposed method introduces the Fisher restriction criterion to limit the sparse coefficients, thereby obtaining a more discriminating dictionary; finally, it distinguishes the category according to the reconstruction errors of the reconstructed signal and the signal to be measured. The classification performance is compared with the existing methods, such as SVM (Support Vector Machine), SRC (Sparse Representation Based Classification), D-KSVD (Discriminative K-Singular Value Decomposition), and LC-KSVD (label-consistent K-SVD), and the experimental results show that FDDL has a better classification performance than the existing classification methods.

Imbalanced Support Vector Machine Classification Based on Hyper-Sphere

2013 ◽  
Vol 339 ◽  
pp. 384-388
Author(s):  
Cun He Li ◽  
Rui Xue Chen ◽  
Yi Zhao Ouyang
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Training Data ◽  
Support Vector ◽  
Classification Rate ◽  
Important Method ◽  
Selection Technique ◽  
Unbalanced Classification

In classification, when the distribution of the training data between classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. Features in the minority classes are normally difficult to be fully recognized. Hyper-sphere support vector machine is an important method for unbalanced classification which is an important issue, but this algorithm has a defect. In order to significantly improve the classification performance of imbalanced datasets, we propose a new method based on Generalized Hyper-sphere Support Vector Machine to enhance the classification accuracy for the minority classes. Support vector machine (SVM) is then used as the base classifier to train the reprocessed dataset. Our experimental results demonstrate that the proposed selection technique improves the classification rate of the rare events, and it also improves the overall accuracy of SVM without data pre-processing.

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