The most existing Low Probability of Intercept (LPI) radar signal recognition algorithms generally have poor recognition performance due to the extraction of radar intra-pulse modulation features at low signal-to-nois...
ISBN:
(纸本)9783800758760
The most existing Low Probability of Intercept (LPI) radar signal recognition algorithms generally have poor recognition performance due to the extraction of radar intra-pulse modulation features at low signal-to-noise ratio (SNR) is difficult. To solve that issue, this paper proposes a novel attention neural network based on the time-frequency images (TFIs) for intra-pulse modulation patternrecognition of radar pulse signals. First, in order to obtain the TFIs from the radar received signals with low cross-terms and high time-frequency resolution, smoothed pseudo Wigner-Ville distribution (SPWVD) is leveraged for generating the TFIs from the received radar signals. Second, Block-matching and 3D filtering (BM3D) is utilized to preprocess the TFIs to eliminate the impact of the background noise for preserving detailed features of the signal, and then we adopt bilinear interpolation algorithm in terms of obtaining the TFIs with the same size for further network training. Finally, the pre-processed TFIs are fed into the deep neural network embedded with SENet attention module, which is called SE-ResNet, to do the recognition. SENet can help the model extract valid feature of TFIs and achieve effective recognition on different radar signal intra-pulse modulation patterns from LPI radar. The simulation results show that the proposed method can achieve an overall recognition rate of 93.2% for eight LPI radar signals when SNR in a low SNR environment, e.g., -8 dB.
The lower limb exoskeleton is an intelligent robot worn by the human body and moves in coordination with the human limbs. It can enhance the human body's weight-bearing capacity, maneuverability and operation capa...
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ISBN:
(数字)9798350367119
ISBN:
(纸本)9798350367126
The lower limb exoskeleton is an intelligent robot worn by the human body and moves in coordination with the human limbs. It can enhance the human body's weight-bearing capacity, maneuverability and operation capabilities. Perception and control, as the brain of the exoskeleton robot, directly determine its collaborative performance and auxiliary effect with the main body. However, the current lower limb exoskeleton has not been widely adopted. One of the reasons is that the ‘brain’ of the exoskeleton robot is not intelligent enough. Therefore, this article reviewed relevant literature on lower limb exoskeleton sensing and control technology in the past five years, and studied the impact of different sensing methods and control strategies on exoskeleton performance. In addition, this article discusses the current challenges of lower limb exoskeletons and provides some innovative insights into future research directions to promote the further development of lower limb exoskeleton technology.
The Memetic Algorithm (MA), introduced by Pablo Moscato in 1989, integrates Evolutionary Algorithms with local search methods, enhancing its effectiveness in solving complex optimization problems. This paper provides ...
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ISBN:
(数字)9798350367492
ISBN:
(纸本)9798350367508
The Memetic Algorithm (MA), introduced by Pablo Moscato in 1989, integrates Evolutionary Algorithms with local search methods, enhancing its effectiveness in solving complex optimization problems. This paper provides a comprehensive survey of MA research published in 2019, reviewing 75 selected papers from an initial pool of 112 identified through Google Scholar. The selected papers were categorized into five types: optimization problems (40 papers), image processing (10 papers), parallel processing (5 papers), gene/DNA datasets (4 papers), and other applications (16 papers). The survey highlights MA’s versatility and effectiveness across various domains, particularly its potential for solving complex optimization problems. Key findings include the adaptability of MA for diverse applications, its ongoing relevance in addressing challenging issues, and promising opportunities for combining MA with other algorithms to enhance performance. The paper also emphasizes the significance of MA in fields such as image processing, where it improves patternrecognition and image enhancement, and in bioinformatics, where it optimizes gene selection and genetic algorithms. Despite the extensive study of MA, there remains a significant research gap in non-English literature, particularly in Bahasa, limiting accessibility for Indonesian researchers. This survey aims to bridge this gap by providing valuable insights and encouraging further exploration and application of MA to solve increasingly complex problems. It offers a comprehensive overview that underscores the importance of MA and its potential for future research and innovation.
Identification of friend or foe (IFF) system has become an indispensable part in modern war. In order to meet the needs of air target situation control in rapid response operations, it is urgent to find an intelligent...
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The proceedings contain 24 papers. The special focus in this conference is on Machine Learning for Networking. The topics include: Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection;lea...
ISBN:
(纸本)9783030708658
The proceedings contain 24 papers. The special focus in this conference is on Machine Learning for Networking. The topics include: Three Quantum Machine Learning Approaches for Mobile User Indoor-Outdoor Detection;learning Resource Allocation Algorithms for Cellular Networks;Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning;deep Learning-Aided Spatial Multiplexing with Index Modulation;A Self-gated Activation Function SINSIG Based on the Sine Trigonometric for Neural Network Models;spectral Analysis for Automatic Speech recognition and Enhancement;road Sign Identification with Convolutional Neural Network Using TensorFlow;a Semi-automated Approach for Identification of Trends in Android Ransomware Literature;Towards Machine Learning in Distributed Array DBMS: Networking Considerations;using Machine Learning to Quantify the Robustness of Network Controllability;deep Learning Environment Perception and Self-tracking for Autonomous and Connected Vehicles;remote Sensing Scene Classification Based on Effective Feature Learning by Deep Residual Networks;identifying Device Types for Anomaly Detection in IoT;a Novel Heuristic Optimization Algorithm for Solving the Delay-Constrained Least-Cost Problem;terms Extraction from Clustered Web Search Results;Configuration Faults Detection in IP Virtual Private Networks Based on Machine Learning;improving Android Malware Detection Through Dimensionality Reduction Techniques;A Regret Minimization Approach to Frameless Irregular Repetition Slotted Aloha: IRSA-RM;mobility Based Genetic Algorithm for Heterogeneous Wireless Networks;geographical Information Based Clustering Algorithm for Internet of Vehicles;active Probing for Improved Machine-Learned recognition of Network Traffic;a Dynamic Time Warping and Deep Neural Network Ensemble for Online Signature Verification.
The data mining task of the classification algorithm is mainly to classify the data and classify them into each known category. As a classification algorithm, SVM has many unique advantages in solving small sample, no...
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Fabric texture classification can be implemented automatically using fabric texture analysis using their images. Texture analysis using fabric images has lots of applications such as automatic recognition and classifi...
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This article describes detailed notes on the practical implementation of our paper Planar 3D transfer learning for end to end unimodal MRI unbalanced data segmentation (ICPR 2020, Milan), which deals with a problem of...
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With the development of industrial automation, there is a growth in number of vehicles resulted to demand of parking space, which is costly in metropolitan areas. For finding vacant parking facilities, drivers spend m...
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This study presents a novel approach for detecting islanding based upon image categorisation using Ensemble Classifier is proposed. Images are used to derive the histogram of oriented gradient (HOG) characteristics so...
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ISBN:
(数字)9798350390728
ISBN:
(纸本)9798350390735
This study presents a novel approach for detecting islanding based upon image categorisation using Ensemble Classifier is proposed. Images are used to derive the histogram of oriented gradient (HOG) characteristics so as to identify non-islanding as well as islanding events. The ever-increasing need for power encourages the growth of Distributed Generation (DG). Islanding is one of the key challenges associated with excessive penetration of DG sources and can cause harm to the clients and their equipment. The islanding needs to be detected in two seconds, according to the IEEE 1547 DG interconnection rules, and the DG must be turned off. In the suggestive technique, spectrogram images are acquired through the time-series signals that are derived from the point of common coupling (PCC). The features are extracted from these images using the histogram of oriented gradient (HOG). The resulting feature vector is then utilised as an input for both the training and testing processes of the ensemble classifier. For deriving the spectrogram image, the rate of change of voltage is utilised as a parameter. In order to evaluate the performance of the ensemble classifier, 5, 10, 20, and 25-fold cross-validations are performed. The findings from the classification analysis indicate that the utilisation of an ensemble classifier in conjunction with the histogram of oriented gradient feature for image classification yielded remarkable outcomes, including an accuracy of 97.45% and a validation error of 0.188.
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