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Anti-jamming Method of FM Fuze Based on Deep learning 4

Anti-jamming Method of FM Fuze Based on Deep Learning

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4th international conference on Defence Technology, ICDT 2024

作者： Zhang, Jin-Yu Yan, Xiao-Peng Hao, Xin-Hong Zhang, Jin-Cheng Science and Technology on Electromechanical Dynamic Control Laboratory China Beijing Institute of Technology China

In response to the challenges posed by sweeping jamming and Digital Radio Frequency Memory (DRFM) jamming in Frequency Modulation Continuous Wave (FMCW) fuzes, a jamming mitigation method based on deep learning method is proposed in this paper. Initially, the target beat frequency signal data affected by sweeping jamming and DRFM jamming is gathered and analysed using the Transformer method. A comparison is made between the classification and recognition outcomes of Transformer model and Support Vector machine (SVM) methods to identify the optimal model. Subsequently, the feature information of the echo signal under interference is extracted using Empirical Mode Decomposition (EMD) decomposition, and the target features are reconstructed. Experimental results utilizing simulated data confirm that the proposed method achieves a classification accuracy of 92% and ensures that the fuze detonation distance remains within the pre-set 9 m range. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Support vector machines

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2022 IEEE 4th international conference on Power, Intelligent Computing and Systems, ICPICS 2022

2022 IEEE 4th International Conference on Power, Intelligent...

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4th IEEE international conference on Power, Intelligent Computing and Systems, ICPICS 2022

ISBN: (纸本)9781665467735

the proceedings contain 191 papers. the topics discussed include: virtual inertia control strategy for PV using dc capacitive and electrochemical energy storage;liquid drip detection in power plant based on machine vision;intelligent recognition algorithm of specific pattern content information based on mobile terminal equipment;research on power communication defect diagnosis technology based on unsupervised learning;key technologies of high speed modulator for satellite data transmission;research on privacy fraud detection of logistic regression based on homomorphic encryption;application research on x-ray imaging technology for online detection of gas insulated switchgear;power transformer state identification method based on operational deflection shapes and visual measurement technology;research on identification method of potential abnormal station for miniature circuit breaker;trajectory tracking error optimization based on iterative learning;research and design of the intelligent bulk transfer laboratory interconnection;energy management cloud platform in softwarized energy internet;simulation and application of a new power stealing method based on the comparison of two types of transformers;lane line detection based on machine vision;an analysis method of GIS equipment fault causes based on online monitoring and joint diagnosis;and virtual prototype design of flexible exoskeleton based on pattern recognition technology.

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A Novel Architecture for a Medical Image recognition System Using Deep learning-Based Multiple Regression Evaluation 4th

A Novel Architecture for a Medical Image Recognition System ...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Parashar, Jyoti Upreti, Kamal Fatima, Iram Gangwar, Divya Saini, Ashok Kumar Vats, Prashant Department of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Computer Science Christ University Delhi NCR Campus Ghaziabad India Department of AI & ML Dr. Akhilesh Das Gupta Institute of Technology & Management New Delhi India Department of Management Babu Banarasi Das Institute of Technology & Management New Delhi India Department of Computer Science & Engineering School of Computer Science & Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819751990

Models based on machine learning are optimization models that collect data, assess it, and deliver the reports required by specialists and management to make the best decisions. the application of contemporary machine learning allows the organization to quickly analyze photographs, differentiate voices assist in providing customer service, assess the information that is at hand, and uncover connections to aid in decision-making processes. the results of this investigation use quantitative methodologies to collect data and analyze it using mathematical procedures such as regression modeling as well as analysis of variance. Deep learning techniques applied to digital imaging, particularly in medical treatment, can increase picture quality, aid in modeling, aid in making the best possible diagnosis, and successfully address demands from patients. To analyze the hypothesis, investigators intend to utilize statistical approaches such as descriptive data analysis, regression evaluation, and analysis of variance (ANOVA). the authors employ the purposive sample approach to choose respondents from the healthcare industry. Purpose sampling is a non-probability sampling approach. Researchers collected data from 193 respondents working at hospitals that are privately owned in Southern Asia. As stated by the study, all factors, including efficiently meeting patient needs, have a probability value of under 0.05, indicating that they are statistically noteworthy. Following the study, the coefficient of variance (R squared) is 0.744, or 74.4%. According to the study, there is a high association between better image quality and ML-based digital picture identification systems. the recognition of patterns and the application of artificial intelligence to computerized recognition of pictures also have a close link. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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machine learning Techniques for the Prediction of Cyber-Attacks 4

Machine Learning Techniques for the Prediction of Cyber-Atta...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Verma, Rishabh thakur, Bharti Shoolini University of Bio-Technology and Management Sciences Himachal Pradesh Solan India

ISBN: (纸本)9798350306118

the aim of this paper to develop a machine-learning (ML) model for UNSW-NBI5 dataset cyberattack prediction. ML is a subset of AI which is a powerful tool for detection of the cyber-attacks. It uses existing data to identify the same pattern between old attacks and new attacks. ML helps to identify the features and characteristics of cyber-attacks. this paper employed four different classifiers: Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and ANN (Artificial Neural Network) on the UNSW-NB15 dataset. 9-fold cross-validation is used on the dataset to determine each classifier's best accuracy. For Ann, we used four layers which are the input layer, two hidden layers, and the output layer. In the input layer, we used 10 neurons. In the first hidden layer, we used 15 neurons, and 10 neurons for second layer. 1 neuron is used in the output layer. the classifiers are evaluated on the basis of accuracy, precision, recall, fl-score, mean square error(MSE), true positive rate (TPR), and false positive rate (FPR). Among all of the classifiers, RF overpowered other models and gave an accuracy of 0.95167% with a TPR of 0.96252% and a FPR of 0.06749%. © 2023 IEEE.

关键词： Neural networks

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A Broad learning System Based on the Idea of Vertical Federated learning 4th

A Broad Learning System Based on the Idea of Vertical Fed...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Ge, Junrong Wang, Xiaojiao Li, Fengyin Gaurav, Akshat Qufu Normal University Jining China Ronin Institute Montclair United States

ISBN: (纸本)9783031200984

As a new type of deep neural network, the broad learning system has been attracting attention since it was proposed. It shows good performance in image processing, face recognition, etc. To train a broad learning model requires comprehensive data. But the data on which the models are trained is often held by different companies and these companies are reluctant to share the data. To address this problem, we propose a broad learning system based on Vertical Federated learning. Compared with the existing broad learning systems, it can integrate multi-party data to obtain comprehensive information, and realize the training of broad learning systems in the case of isolated data islands. this facilitates the application and advancement of broad learning systems. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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An Overview of machine learning Techniques for Evaluation of Pavement Condition 4

An Overview of Machine Learning Techniques for Evaluation of...

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4th international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2022

作者： Chavan, Aradhana Pimplikar, Sunil Deshmukh, Ashlesha Mit World Peace University School of Civil Engineering Kothrud Pune India

ISBN: (数字)9781665462464

ISBN: (纸本)9781665462464

Pavement management systems play a vital role in the development of a country as it is a very important part of the economy. Maintaining a good quality of the road is the key duty of the road authorities. they require methods for pavement-related data collection and analysis to evaluate its condition. machine learning (ML) methods can be utilized for defect classification from an image, defect recognition and segmentation in the assessment of pavement distress. this paper presents an overview of the machine learning techniques used to analyze pavement condition data. Moreover, information collection methods and pavement condition indices are also studied from the point of view of ML algorithms. Future research directions are also presented by highlighting the limitations of using ML techniques for the assessment of pavement conditions. © 2022 IEEE.

关键词： Image segmentation

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Non-negative Tensor Representation and Unsupervised Classification of Object Pose in Continuous Image Sequences 4th

Non-negative Tensor Representation and Unsupervised Classifi...

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4th international conference on Artificial Intelligence Logic and Applications

作者： Ying, Zhihao Zhao, Yang Pei, Jihong Yang, Xuan Shenzhen Univ Coll Elect & Informat Engn Shenzhen Guangdong Peoples R China Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen Guangdong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9789819603534;9789819603541

the analysis of video data in machine learning classification tasks has become a significant topic in both research and application areas. the accurate classification of video data by frame sets, particularly when the content contains objects that are in a state of dynamic change, represents a significant and complex undertaking. With regard to the temporal phase of video, the thesis proposes an unsupervised classification method based on non-negative tensor factorization (NTF). In order to transform the video data into low-dimensional data that can be classified by unsupervised clustering, the non-negative tensor factorization method is used to reduce the dimension of the input tensor, resulting in the generation of a non-negative rank-1 tensor combination. Following dimensionality reduction, the extracted features are divided into clusters through Mean Shift, thereby facilitating classification and recognition of video sequences. Experiments on the Coil-100 dataset of item image sequences at different angles and other dynamic pose-changing videos demonstrate the effectiveness of the method.

关键词： Video timing division Non-negative tensor factorization Unsupervised learning

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A Method of Protecting Sensitive Information in Intangible Cultural Heritage Communication Network Based on machine learning 1

引用

4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Zhang, Xiaoyu Jin, Ye Wuxi Vocational Institute of Commerce Wuxi214153 China

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

In order to accurately identify the sensitive information in the intangible cultural heritage communication network and realize the reasonable protection of intangible cultural heritage data, a method for protecting the sensitive information in the intangible cultural heritage communication network based on machine learning is proposed. With the support of machine learning algorithm, the distance measurement results are solved, and then the specific values of compressed characteristic indexes are calculated by establishing a random measurement matrix to complete the tracking and processing of the target parameters of intangible cultural heritage. On this basis, according to the encryption processing results of sensitive information, the implementation standard of OSBE protocol is established, and then with the help of the formed sensitive information processing process, the effective protection of sensitive information of intangible cultural heritage communication network is realized. the results of comparative experiments show that under the effect of machine learning algorithm, the recognition accuracy of the network host for the sensitive information of intangible cultural heritage has significantly improved, and it really has strong practical value in the reasonable protection of intangible cultural heritage data parameters. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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recognition Of Obstreperous Sleep Apnea And Cardiovascular Diseases Using machine And Deep learning Models 4

Recognition Of Obstreperous Sleep Apnea And Cardiovascular D...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Patel, Ruchi Kashyap, Ramgopal Barolia, Pushpa Arya Banoth, Sai Kiran Naik Kumar, Sumit Mukherjee, Shubham Gyan Ganga Institute of Technology & Sciences Dept of Cse Jabalpur India Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal462044 India Fannie Mae United States Tata Consultancy Services Bhopal India Ggits MP India

ISBN: (纸本)9798331518981

this study examines the SHHS database and analyses and contrasts the performance of several ML and DL techniques for OSA and CVD detection. the dataset is primarily focused on diagnosing sleep apnea and associated breathing disorders, as well as evaluating the potential influence of sleep apnea on cardiovascular events. It contains polysomnography recordings from over 6,000 people. the current study evaluated ten top-notch ML and DL models, including SVM, DT, RF, NB, ANN, 1D-Convolutional Neural Networks (1D-CNN), ResNet, ResNet-100, ResNet-50, and ResNet-18 as well as Artificial Neural Networks (ANN). they suggested a 50:80:20 split for training and testing;however we evaluated the models with data splits ranging from 50:80:20 to see how well they predicted CVD risks in OSA patients. When compared to the other ML models, DT's 86.5 accuracy and 84.4 sensitivity were the best. Similarly, ResNet101 surpassed competing DL models, with an accuracy of 85% and a sensitivity of 85.5%. © 2024 IEEE.

关键词： Convolutional neural networks

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FingerPrintInsight:Unveiling Crime pattern through Deep Fingerprint Analysis 4

FingerPrintInsight:Unveiling Crime Pattern Through Deep Fing...

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4th IEEE international conference on data Engineering and Communication Systems, ICDECS 2024

作者： Kavitha, T. Revappa Handignur, Sachin B Sachin, Y.M. Purandare, Sumit S Rathish, C.R. New Horizon College of Engineering Dept. of Computer Engineering Bangalore India

ISBN: (纸本)9798350393354

A fingerprint identification system employing deep machine learning and Convolutional Neural Networks could automate analysis. Images from crime scene investigation techniques are entered into a database. Partial latent prints are often difficult to classify. the system operates in three phases: preprocessing, feature extraction, and matching. Preprocessing enhances image quality before feature extraction identifies distinctive minutiae points. False minutiae removal further refines the data. the preprocessed fingerprints serve as input to train and test the model. As new prints are incorporated along with confirmed matches to improve accuracy, the system facilitates identification. the approach scales without proportionate increase in human effort. Accelerated evidence analysis could potentially solve more crimes by linking cases in a database. © 2024 IEEE.

关键词： Convolutional neural networks

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