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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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Acceleration Consistency Analysis Based GNSS Spoofing detection for Autonomous Vehicles 3

Acceleration Consistency Analysis Based GNSS Spoofing Detect...

引用

3rd international conference on Consumer Electronics and Computer Engineering, ICCECE 2023

作者： Zhang, Cheng Wang, Hongmei Hu, Hongxing Wang, Shouwei Liu, Hong School of Software Engineering East China Normal University Shanghai China Information Engineering College Xinjiang Institute of Engineering Xinjiang China China Automotive Innovation Corporation Department of Automotive Cybersecurity Nanjing China

ISBN: (纸本)9798350331578

Global Navigation Satellite system (GNSS) positioning technology has been widely applied in Autonomous Vehicles (AVs), but these AVs suffer from potential security threats of GNSS spoofing attacks. This work proposes a GNSS spoofing detection algorithm for AVs. The algorithm checks the consistency of two acceleration series deceived from GNSS and IMU to detect GNSS spoofing. Ideally, the two acceleration series are consistent in normal conditions and inconsistent under spoofing. However, measurement noise and outliers may cause inconsistency even in normal conditions, which will produce false alarms. Thus, the work also develops a method to analyze acceleration series in the frequency domain and find frequency components that really have benefits to consistency checking. As a result, the algorithm can obtain a high detection accuracy and a low false alarm rate simultaneously in all attack scenarios proposed in the work. © 2023 IEEE.

关键词： Frequency domain analysis

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detection of Acoustic Scenes and Events using Audio Analysis - A Survey 3

Detection of Acoustic Scenes and Events using Audio Analysis...

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Vij, Dinesh Yogesh, Yogesh Srivastava, Durgesh Shankar, Hari Chitkara University Chitkara University Institute of Engineering & Technology Punjab Rajpura India

ISBN: (纸本)9798350399264

The field of Acoustic Event detection is generally overshadowed by the traditional field of Automatic Speech Recognition. But our auditory environment is made up of many sounds other than speech. Most of the current surveillance systems, diagnostic systems, etc. use video cameras and other sensors. But acoustic sensors are not used frequently because of lack of event detection accuracy in case of overlapped events. This motivates the need for developing a system that is able to identify acoustic scenes and event correctly. Researchers have worked on the acoustic events of diverse fields such as bird species recognition, classification of different type of vehicles, etc. This work has considered the all such acoustic scenes in which humans interact with the environment directly or indirectly. In this work, we have provided an overview of major studies done in this field along with the suggestions regarding what can be done in future in this field. We have also studied the various types of features related to the various acoustic scenes and events. We have summarized the recent open-source datasets and challenges conducted by IEEE AASP done in this field, which could help the future researcher to develop a foundation in this field. © 2023 IEEE.

关键词： Feature extraction

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Design and acceleration of driving behavior detection model 3

Design and acceleration of driving behavior detection model

引用

3rd international conference on Consumer Electronics and Computer Engineering, ICCECE 2023

作者： Peng, Wentao Wang, Jing Wu, Zhongcheng Shen, Qing Liu, Qi Zhang, Jun Hefei Institute of Physical Science Chinese Academy of Sciences University of Science and Technology of China China Hefei Institute of Physical Science High Magnetic Field Laboratory Chinese Academy of Sciences Hefei China

ISBN: (纸本)9798350331578

Traditional driving behavior recognition algorithms based on vehicle motion features and machine learning have low recognition accuracy and poor flexibility under complex background. In this paper, a driving behavior detection model implementation and acceleration method based on domestic chip platform RV1126 is proposed. Firstly, the YOLOv5s model operator was adjusted and adapted to replace the Focus operator and the large-size Maxpool operator in the network, so that the model could deploy the chip platform. Then, in order to solve the problem of decreasing the running accuracy of quantization model board and low detection frame rate, the method of adaptive image scaling and filling and multi-Thread binding CPU to complete the detection task concurrently is used to optimize and accelerate. The experimental results show that the proposed method can effectively improve the speed and accuracy of the end-side deep learning detection model. © 2023 IEEE.

关键词： Object detection

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Molten steel detection method based on LFMCW radar ranging 3

Molten steel detection method based on LFMCW radar ranging

引用

3rd international conference on Applied Mathematics, Modelling, and Intelligent Computing, CAMMIC 2023

作者： Zhang, Qixiang Wang, Zhichun Inner Mongolia University of Science and Technology China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

In industrial production, metal level detection is very important. LFMCW millimeter wave radar is suitable for such harsh environments that require high-precision short distance measurement. This paper discusses the reasons that affect the accuracy of millimeter wave ranging, and gives the specific radar parameters and ranging module configuration based on the actual situation of metal level detection, and analyzes them;Based on FFT frequency estimation algorithm, rough target peak is provided, and then frequency spectrum is refined near the peak point through two CZTs to solve phase ambiguity. On this basis, phase correction method is used to further improve the accuracy of the algorithm. Finally, the AWR1843 millimeter wave radar and DCA1000 are used to conduct the cold state experiment on the surface position of the metal liquid level, obtain the IF signal and process the IF signal data. The results show that when the measuring system detects the position of the liquid metal surface 1 m away from the AWR1843 signal transmitting end, the experimental measurement error does not exceed 0.1 mm. © 2023 SPIE.

关键词： Fast Fourier transforms

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Teaching Innovation and Practice for the Interdisciplinary Course "Electrical Engineering plus AI" in the New Era - Taking the Course "Artificial intelligence: Thinking Mode, Theoretical Basis and Practical Application" as an Example 3

Teaching Innovation and Practice for the Interdisciplinary C...

引用

3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Zhang, Junbo Han, Yongxia Zhang, Shouzhi Chen, Ge South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

ISBN: (纸本)9798350375145;9798350375138

Various learning problems caused by the significant gap between students' existing knowledge, competency levels, thinking patterns and the teacher's expected goals in the interdisciplinary course "Artificial intelligence: Thinking Mode, Theoretical Basis and Practical Application" within the Electrical Engineering major is addressed in this paper. An innovative "3-stage 5-dimension" matrix teaching scheme is proposed, aiming to cultivate new-era talents capable of engaging in core tasks of planning, operation, and maintenance of new power systems with advanced digital tools. The effectiveness of course construction through the proposed innovative teaching methods is proved by significantly enhanced learning outcomes from students with an electrical engineering background, and effectively achieved teaching objectives.

关键词： matrix teaching "Electrical Engineering plus AI" teaching innovation

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Identifying Unusual Human Movements Using Multi-Agent and Time-Series Outlier detection Techniques 3

Identifying Unusual Human Movements Using Multi-Agent and Ti...

引用

3rd international conference on Advanced Research in Computing, ICARC 2023

作者： Asanka, Ppg Dinesh Rajapakshe, Chathura Takahashi, Masakazu University of Kelaniya Department of Industrial Management Kelaniya Sri Lanka Yamaguchi University Graduate School of Innovation and Technology Management Yamaguchi Japan

ISBN: (纸本)9798350347371

This research paper has introduced knowledge-driven multi-agent technology for automated machine learning in time series analysis in the context of human mobility. The main objective of this research is to identify unusual human mobility using Time Series outlier detection techniques with a more efficient multi-agent system. detection of unusual human movement can be helpful for many domains, such as security, marketing, and health. A mobile dataset in Hiroshima, Japan between 2019-December to 2020-November was used for this research. The mobile dataset was converted to time series for multiple locations in Hiroshima, Japan. Since many different parameters are selected for time series, the message space multi-agent technique is used. Sub agents are introduced for duplicate removal, missing data replacement, and outlier detection. Multiple processing agents and a control agent were introduced to predict the missing values to improve the efficiency of the model. Finally, using the Seasonal-Trend decomposition techniques, unusual movements are identified, and unusual human movements are plotted with the holidays. Multiple outlier points were detected for all the locations, and there were more than a hundred outlier points were detected for the selected locations. © 2023 IEEE.

关键词： Time series analysis

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Hybrid Optimization Based BERT Model for Drug detection in NLP 3

Hybrid Optimization Based BERT Model for Drug Detection in N...

引用

3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Kruthika, C.G. Namitha Devi, N.N. Madlur, Vijayananda V Asha, K. Aruna, T.M. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Vidya Vikas Institute of Engineering and Technology Department of Computer Science and Engineering Mysuru India Mangalore Institute of Technology and Engineering Department of Computer Science and Engineering Moodabidri India Reva University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350340358

When it comes to natural language processing (NLP), sentence embedding is a hot topic because of the knowledge it transfers to subsequent tasks. Recent studies have shown that sequential neural networks, which can be trained on large quantities of data, can be used to encode info through a one-way analysis of text sequences, making them a powerful tool for drug detection. The purpose of this study is to smear cutting-edge deep learning techniques to publicly available data in order to accurately detect and identify adverse medication reactions that have not been recorded by medical professionals (open data). This study presents a deep learning-based strategy employing Enhanced Encoder on a dataset of 10,000 reviews culled from WebMD and *** and labelled by hand (IBERT). The model is created by integrating the BERT with many parallel blocks of a single-layer deep Convolutional Neural Network (CNN) that use varying kernel sizes and filters. To deal with ambiguity, the biggest obstacle to NLP, such a combination is helpful. Cuckoo Search (CS) and Antlion Optimization (ALO) are coupled to create a hybrid optimization strategy for determining the best values for the proposed model's hyper-parameters. Results from a number of different classification methods demonstration that the suggested model meaningfully outdoes the state-of-The-Art models. © 2023 IEEE.

关键词： Embeddings

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Continuous Glucose Monitoring: A Non-Invasive Approach for Improved Daily Healthcare 3

Continuous Glucose Monitoring: A Non-Invasive Approach for I...

引用

3rd international conference on Electronic and Electrical Engineering and Intelligent system, ICE3IS 2023

作者： Putra, Karisma Trinanda Surahmat, Indar Nazilah Chamim, Anna Nur Ramadhan, Muhammad Zhafran Wicaksana, Darma Dhea Namyra Alissa, Raden Ajeng Center of Artificial Intelligence and Robotics Studies Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Bantul Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Bantul Indonesia

ISBN: (纸本)9798350327762

Diabetes mellitus is a prevalent health condition affecting approximately one in eight individuals worldwide. Insufficient management of this disease can lead to various medical complications, including vision loss and limb amputation. Continuous glucose monitoring (CGM) is crucial for effective diabetes management. However, current one-time glucose monitoring methods still rely on invasive techniques, causing pain and lacking continuous monitoring capability. This study aims to develop a non-invasive technology for measuring blood glucose levels that can be conveniently monitored through a smartphone using wireless communication. The device utilizes an ESP32 microprocessor and a PPG MAX 30102 sensor capable of recording pulse waves, a biomarker, at the fingertips. In this preliminary study, non-invasive data from 22 participants were used and divided into three classes according to conventional invasive measurements. Artificial intelligence (AI) methods, specifically Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) models, were utilized to achieve convergence between the conventional invasive measurements and the proposed approach. The trained model demonstrated the highest accuracy of 0.944, with a minimum model loss of 0.177. In the validation phase, the proposed approach proved its reliability by achieving an average accuracy of 0.750. The non-invasive approach has the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly enhancing diabetes management and improving the overall quality of life for those affected by this condition. © 2023 IEEE.

关键词： Glucose

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detection of estrus through automated classification approaches using vocalization pattern in Murrah buffaloes 3

Detection of estrus through automated classification approac...

引用

3rd international conference on Artificial intelligence and Signal Processing, AISP 2023

作者： Sharma, Sugandha Kadyan, Virender Uttarakhand Dehradun India

ISBN: (纸本)9798350320749

The field that deals with the studying of animal sounds to detect or classify various animal conditions in an open or a closed setup. Bioacoustics has its prospective applications in speaker identification, detection of respiratory distress, estrus phases, stress induced by ecological conditions, separation and weaning and so on. In dairy cattle, especially murrah buffalo breed, inaccurate detection of estrus is a significant constraint that affects the reproductive capacity of small as well as large cattle-herds. Detecting estrus in dairy cattle at correct time assists livestock managers and farmers to augment milk production, profitability and maintaining welfare of *** studies on relationship between vocal behavior and reproductive performance is still at a very primary stage. This is due to inadequate amount of research work that has been done in order to study the association between acoustic features of animal vocal sounds and estrus owing to limited publicly available dataset. Outcomes of animal vocal sounds based studies are primarily dataset-dependent, i.e., the findings are ineffective if recording conditions are unidentified. Modern developments in deep-learning and machine-learning based approaches have led to the development of high-performance non-invasive ''animal sound classification system These systems, however, are built on scarce speech-corpora which is a preferred requisite for machine learning model training, which requires hours of good-quality recordings of sound of a single speaker in a noiseless environment. Developing such a good-quality sound corpus manually requires labour and time which makes the process expensive. In the present paper, the authors have executed machine-learning models like Random Forest, Decision Tree and SVM as well as a custom-built CNN architecture on a dataset and have carried out a comparative analysis of the above-mentioned techniques. © 2023 IEEE.

关键词： Animals

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Enhancing use of BERT information in neural machine translation with Masking-BERT attention 3

Enhancing use of BERT information in neural machine translat...

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3rd international conference on Artificial intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Chen, Xi Wu, Lin Zhang, Yuanhao Yunnan Provincial Artificial Intelligence Key Laboratory Kunming University of Science and Technology Yunnan Kunming China Cybersecurity and Safety Research Institute Beijing Tiandi Hexing Technology Co. Ltd. Beijing China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

BERT has shown remarkable performance in several natural language processing tasks, but it fails to exhibit the same high performance in cross-lingual tasks, particularly machine translation. To address this issue, we propose a BERT-enhanced neural machine translation (BE-NMT) model that optimizes the use of the information contained in BERT by NMT. Our proposed model comprises three components: (1) A MASKING strategy to mitigate knowledge forgetting caused by fine-tuning of BERT on the NMT task. (2) Serial and parallel processing of multi-attention models for incorporating BERT into the NMT system. (3) Fusing multiple hidden layer outputs of BERT to supplement the missing linguistic information of its final hidden layer output. We conducted experiments on several translation tasks, and our proposed model notably outperforms the strong baseline by improving 1.93 BLEU points on the United Nations Parallel Corpus English→Chinese task. Additionally, our model also achieves remarkable performance on other translation tasks. © 2023 SPIE.

关键词： Neural machine translation

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