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recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional Computing 3

Recognition of Active Deception Jamming for LFM Radar Based ...

引用

2022 3rd international conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

作者： Dong, Hao Wang, Yan Hu, Wen Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time-frequency diagrams as the input, which time-frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Hamming distance

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IoT-based simulation of flocking algorithm 3

IoT-based simulation of flocking algorithm

引用

3rd international conference on Signal Processing and machine Learning, CONF-SPML 2023

作者： Wang, Xiaojian College of Artificial Intelligence Nanjing Agricultural University Jiangsu Province Nanjing210000 China

The study of animal groups' flocking behavior is of great significance to the study of multi-agent system. Flocking algorithm is a typical algorithm for group control based on animal group behavior. The research on flocking algorithm is mature, but there is little research on the implementation of Flocking algorithm using digital twin. Digital twins, as a key technology in the direction of IoT, is potentially valuable for improving the efficiency of cloud and devices. Therefore, the main research goal of this paper is to simulate the flocking phenomenon in nature by using digital twins and flocking algorithm to realize the cooperative control of multi-agent systems under the scenarios of obstacle-free and obstacle. In this paper, the author uses digital twins and flocking algorithm to achieve simulations of animal flocking Behaviors. The final simulation results show that the Multi-agent system can converge agents and reach consensus, and finally cross the obstacles to reach the target node, which verifies the rationality and effectiveness of the algorithm. Through the way of digital twins, the burden of the intelligence body's computation on obstacle identification can be reduced, and the synchronization efficiency of the cloud and the device can be effectively elevated, thus in a practical sense, favoring the deployment implementation. © Published under licence by IOP Publishing Ltd.

关键词： Multi agent systems

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Facial Expression Transfer Based on Generative Adversarial Networks: A Review 3

Facial Expression Transfer Based on Generative Adversarial N...

引用

3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Fan, Yang Jiang, XingGuo Lan, ShuXing You, Song Sichuan University of Science and Engineering ZiGong643000 China Artificial Intelligence Key Laboratory of Sichuan Province YiBin644000 China

ISBN: (纸本)9781510667563

The fields of computer vision and special effects production often require editing and synthesis of facial expressions, such as transfer one person's expression to another's face. It is crucial to synthesize realistic facial expressions to ensure accurate expression of a person's feelings, however, because expressions are very subtle and subject to individual differences, facial expressions generated using methods such as 3D facial reconstruction and face morphing are difficult to guarantee sufficient facial details and the workload is huge, requiring a lot of time and effort. Facial expression transfer techniques can synthesize a large library of face expressions while ensuring human privacy, and provide data support for related face research problems. With the development of deep learning, generative adversarial networks provide a new solution for facial expression transfer. The currently available GANs are CGAN, StarGAN, G2-GAN, GC-GAN, geometric feature and attribute tagging driven GAN, Expr-GAN, TER-GAN, and StyleGAN. This paper focuses on the above methods of facial expression transfer based on generative adversarial networks, and discusses their respective features and advantages;summarizes the available facial expression transfer;finally, based on the problems of existing methods, research directions for future work are proposed. © 2023 SPIE.

关键词： Generative adversarial networks

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Overview of Autonomous Marine Surveillance Device 3

Overview of Autonomous Marine Surveillance Device

引用

3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Balaje, Dhivya Vibha, V. Pichumani, Harish Sarkar, Swagata Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665462754

The use of Autonomous Underwater Vehicles (AUVs) for environment exploration, data collection and research developments has been growing day by day. Beach checks and surveys are documented studies about land erosion, beach erosion, underwater mapping, and various other aspects of the marine environment. These surveys are done manually or with quad bikes, commercially coded hovercrafts, etc, that are ultimately powered by humans. We propose an autonomous beach-check survey device, making use of image processing and machine learning algorithms to inspect the entire camera in the underwater body and collect the data required. This vehicle will focus on covering all the operations to be performed which would reduce human intervention and manpower. © 2022 IEEE.

关键词： Autonomous vehicles

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Hybrid architecture CNN-BLSTM for automatic speech recognition

Hybrid architecture CNN-BLSTM for automatic speech recogniti...

引用

Artificial intelligence For Internet of Things (AIIoT), international conference on

作者： Muzaffar Ahmad Dar Jagalingam Pushparaj Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Vellore Tamil Nadu India Department of Computational Intelligence School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

With several deep learning approaches, the domain of automatic speech recognition (ASR) has seen notable advancements in recent times. The domains of intelligent human- computer interaction and machine translation greatly benefited from accurate speech recognition. The present study introduces a hybrid architecture amalgamating a convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) for speech recognition. For aligning speech input sequences with corresponding text output sequences, it uses the connectionist temporal classification (CTC) technique. The experiments were done on the LJ speech dataset, and the results showed with an increased number of training samples, the performance of the speech recognition algorithm tended to be increased but the time taken for training gradually increased over time. Moreover, a trained speech recognition algorithm exhibits a longer training time when the recognition accuracy is lower. In this study, we implemented a hybrid deep learning model CNN-BLSTM, in conjunction with the CTC loss function, which attains a word error rate (WER) of 36.97% on the testing dataset.

关键词： Training Deep learning Accuracy Error analysis Computer architecture Real-time systems Classification algorithms

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Evaluating Large Language Model Understanding of Due Process

Evaluating Large Language Model Understanding of Due Process

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Computing and machine intelligence (ICMI), international conference on

作者： Joshua P. Johnson Adrian P. Lauf Dept. of Computer Science and Engineering University of Louisville Louisville US

In this paper the Llama-2 and GPT-2 large language models are evaluated for their fundamental understanding of basic due process concepts. The reference implementations and versions fine-tuned on judicial opinions wer... 详细信息

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

关键词： Law Computational modeling Large language models Decision making Reliability machine intelligence Context modeling

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SaaS-based Intelligent conference Management System using Facial recognition

SaaS-based Intelligent Conference Management System using Fa...

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Information Technology,Big Data and Artificial intelligence (ICIBA), international conference on IEEE international conference on

作者： Yue Ling Wuxi City College of Vocational Technology Huishan District Wuxi China

An SaaS-based conference management system that implements facial recognition technology has been designed to address the requirement for intelligent conference management. The system leverages the SaaS model to achieve rapid deployment, enabling centralized management of multiple conference venues via a conference management service center. This center enables unified scheduling of attendees, equipment, and conference facilities, and provides customization to meet the requirements of various conference scenarios. The system uses facial recognition technology for conference check-in and data collection during the event, thereby optimizing conference automation management based on attendee information. User feedback has indicated a significant improvement in conference management efficiency.

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A Study of Earthquake Forecasting Using Artificial intelligence (AI) Approaches 3rd

A Study of Earthquake Forecasting Using Artificial Intellige...

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

作者： Dubey, Dharmendra K. Tomar, Arjit Mishra, Mohit Yadav, Amit K. Yadav, E. Prabhash K. Shree Dhanvantary College of Engineering and Technology Kim Surat India Noida International University Noida India Lovely Professional University Phagwara India BSSITM UP Lucknow India Bhagwant University Ajmer India

ISBN: (纸本)9789819727155

The investigation of seismic tremor gauging utilizing man-made intelligence approaches has shown promising outcomes of late. AI calculations have had the option to distinguish examples and connections in seismic and different information that might be demonstrative of approaching tremors. Nonetheless, the precision of these conjectures differs relying upon the nature of the information, the intricacy of the fundamental topographical and geophysical cycles, and different variables. In some instances, deterministic forecasting methods that concentrate on identifying specific seismic patterns linked to earthquakes have produced positive outcomes. For instance, a 2018 study that used machine learning algorithms to analyze seismic data from the San Andreas Fault in California was able to identify seismic patterns that were associated with previous earthquakes and was published in the journal Nature. The researchers were then prepared to use these guides to make assumptions regarding prospective quakes with a serious degree of precision. Methods of probabilistic determining that make use of factual models to estimate the likelihood of a quake occurring in a particular region have also produced positive results. Utilizing seismic information from Japan and an AI calculation, a recent report distributed in Science Advances had the option to foresee the probability of tremors in different areas precisely. Moreover, a few specialists have communicated worry that putting an excess of confidence in estimates in view of computer-based intelligence could provide individuals with a misguided feeling of safety and prompt them to become careless in their endeavors to get ready for and answer catastrophes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Implemention of a machine intelligence Pronunciation System Based on Mul Tran to Improve the Basic English Speaking Skills

Implemention of a Machine Intelligence Pronunciation System ...

引用

Artificial intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Jingjing Zou School of Foreign Languages Shenyang Jianzhu University Shenyang Liaoning China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In the process of learning English, English pronunciation has always been difficult. This article was based on the Mul Tran (Multilingual Translation) platform and used various methods such as animation, sound, images, and text to learn and train English phonetics. It can provide effective pronunciation feedback for learners, guide and correct their continuous training, and improve their oral pronunciation. The system includes pronunciation demonstration, pronunciation follow-up, pronunciation comparison, and pronunciation scoring functions. The system also provides the function of “continuous following of original sounds”, which can continuously follow and score the original sounds, and comprehensively compare the pronunciation of each speech of the learners. For each pronunciation, according to the resonance peak image, the mouth shape was improved. The effective rate of correcting vowel pronunciation was 0.899, and the effective rate of correcting word pronunciation was 0.928, which had a certain guiding effect on student pronunciation correction.

关键词： Training Accuracy Shape Mouth Speech recognition Phonetics Speech Resonance Noise measurement machine intelligence

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Application of Gradient Boosting Classifier-Based Computational intelligence to Detect Drug Addiction Threat in Society 3rd

Application of Gradient Boosting Classifier-Based Computatio...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Kumar, Ashutosh Sinha, Abhigyan Bakshi, Tamoghno Choudhury, Sibashish Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Jadara Research Center Jadara University Irbid21110 Jordan

ISBN: (纸本)9789819708918

Currently, drug and alcohol addiction has become a major menace to society's youth. As responsible citizens of this country, we must act now to keep these young brains from succumbing to this lethal addiction. In this research, a method based on intelligent analytics is designed for forecasting the threat of drug addiction using machine learning algorithms. Initially, we identify several reasons for drug addiction by speaking with health professionals, individuals addicted to drugs, and reading related studies and articles. Then we gather information from both addicts and abstainers. After preprocessing the data, we use Random Forest, Naive Bayes, Adaptive Boosting, and Gradient Boosting machine and assess the interpretation of each of these classifiers using certain well-known performance criteria. Outcome with Gradient Boosting showed promising with the highest accuracy rate of 95.4% and minimum error rate of 0.0854. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaptive boosting

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