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Design and research of waveform detection experimental platform 3

Design and research of waveform detection experimental platf...

引用

3rd IEEE international conference on Information technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Guo, Chunlai Cui, Li Cao, Xianying Shang, Lei Ni, Xinru Wang, Xinyi Qu, Yuexin Harbin Cambridge University Harbin China Harbin Instiute of Petroleum Harbin China

ISBN: (纸本)9781665490788

With the rise of artificial intelligence, machine learning methods have been applied to classify many waveform processes, such as ECG. Since the detection signal waveform and image have similar data forms, there is a relationship between local and global, i.e., the relationship between low-level features and high-level features. Therefore, the features of CNN can be used to abstract some low-level and visible waveform changes in the diagnosis process into some concepts of passing and failing in engineering inspection, and realize automatic feature extraction and classification. In order to reduce the parameter operation and improve the efficiency, this paper designs a 7-layer CNN shallow model to complete the automatic classification of pass and fail in a simple and efficient way, and then effectively solve the classification problem of manual inspection images. It provides a practical basis for further application of the trained optimal model to guide engineering practice, and provides further validation of the CNN in the development of both efficiency, accuracy, and generalizability algorithms, which have great potential for future applications in waveform *** this paper, an experimental platform for waveform detection by machine vision is established to address the problem of waveform classification for ultrasonic conduction technology detection, using a self-built waveform detection database, which contains a total of 1000 test images, applying a deep learning convolutional neural network (CNN) method for training, and using 700 waveform images in the period as the detection data set, with a training and detection accuracy of 93& or higher. In the detection of continuous waveforms, the CNN method outperforms the conventional method of manual reading in terms of speed and accuracy. © 2023 IEEE.

关键词： Efficiency

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Microwave-based Concentration detection system of Pulverized Coal 3

Microwave-based Concentration Detection System of Pulverized...

引用

3rd international conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Yuan, Cenjie Dong, Qunli Li, Shuai Jiang, Xiongjie Gao, Feng Song, Zhenming Tian, Bing Tian, Haoyu Zhejiang Zheneng Jiahua Power Generator Co. Ltd Jiaxing China Zhejiang Energy Digital Technology Co. Ltd Hangzhou China

ISBN: (纸本)9798350318180

In light of the lack of consensus and effective means for measuring pulverized coal concentration in dilute-phase pneumatic conveying, this paper proposes a microwave-based pulverized coal concentration detection system. The sensor employs multiple electrodes with freely switchable transceiving states, enabling the construction of a full cross-section signal path and allowing insertion loss measurement. A mapping function utilizing L1 distance is established for insertion loss, mapping it to the mixed dielectric constant of the pulverized coal-air mixture, thereby estimating pulverized coal concentration. Simultaneously, a concentration classification algorithm is proposed. The effectiveness and accuracy of the sensor measurement are verified through concentration calibration and realtime classification. Experimental results demonstrate the system's ability to detect and classify various pulverized coal concentrations, offering novel insights into concentration measurement of gas-solid mixtures in dilute-phase pneumatic conveying conditions. © 2023 IEEE.

关键词： concentration classification dilute-phase pneumatic conveying microwave sensor pulverized coal

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Federated Learning Integrated CNN-Trans DDoS Attack detection Model 3

Federated Learning Integrated CNN-Trans DDoS Attack Detectio...

引用

3rd international conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Shen, Qiaoling Wang, Yi Xie, Linzhe Zhao, Xin Bai, Junwen Zheng, Yongxin Power China Kunming Engineering Corporation Limited Kunming China

ISBN: (纸本)9798350316674

In DDoS attack monitoring [1] [2], effective detection algorithms [3] that rely on a large amount of data support are needed to achieve the desired detection results. Because individual organisations generate limited amount and characteristics of data, and considering data privacy riskiness they are reluctant to share training data [4], which often results in "data silos", and insufficient data quantity and quality can lead to inaccurate detection results. To address the above issues, this paper will explore how to use joint learning techniques to train models on massive network data while ensuring the privacy and security of organisational data. © 2023 IEEE.

关键词： Data privacy Data silos DDoS attack monitoring Federated learning

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Chewing detection Using Brightness Changes in Video based on Deep Learning 3

Chewing Detection Using Brightness Changes in Video based on...

引用

3rd IEEE international conference on Computer Communication and Artificial intelligence, CCAI 2023

作者： Nakada, Daiki Ogawa, Tomomi Tokyo Denki University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9798350335262

Chewing well is known to be beneficial for human health. However, a simple method to measure the number of chews for health guidance has not been established. In this paper, we propose a simple method to measure the number of chews using a photographic device such as a smart phone. When a video of chewing during eating is filmed, the brightness of the chewer's face changes as the jaw moves up and down due to chewing. When the values are graphed, the change in brightness results in a waveform shape that is easy to understand. Since the number of chews can be estimated from the number of waves in the waveform, the number of chews is measured using a neural network that counts the number of waves. To compensate for the small amount of data, we use a large amount of pseudowaveforms, such as sine waves. Then, a learning model that determines the number of repetitions is created, and a large amount of pseudo-waveform data is used for pre-training. The parameters of the trained model are determining by transfer learning so that the model can be applied to a small amount of data. As a result of learning the video waveform data, we were able to measure 96.6% of the data within ± 2 by moving all the parameters of the trained model. © 2023 IEEE.

关键词： Luminance

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Intelligent Traffic Monitoring system using Infrared Automatic Number Plate Recognition (IR-ANPR) 3

Intelligent Traffic Monitoring System using Infrared Automat...

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Patil, S.T. Gite, Gayatri Hirkani, Haider Bassan, Inderdeep Raghvani, Isha Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350343632

90% of souls lost on roads is due to rash driving the most common being overspeeding which can be minimized by keeping people accountable and recorded for their crime. In order to tackle and automate this security system at the ground level we have moved in the direction of implementing and executing the automation of the vehicle speed detection system. This paper proposes a new method for determining the speed of a car while simultaneously automating a speed breaker system that will appear when the speed of the car goes above a certain value, also storing the license plate and image of the car in a database. The results show that the proposed electronic system provides a low-cost and efficient method for determining the speed of a vehicle and taking the necessary action. The time complexity of the system is less due to the successful interfacing of speed detection and different sensors. Successful implementation of the Automatic Number Plate Recognition (ANPR) system is carried out using ESP8266 with an accuracy of 92.69% along with an automated speed breaker. © 2023 IEEE.

关键词： (OCR (Optical Character Recognition) ANPR (Automatic Number Plate Recognition) ESP8266(Wifi-module) License Plate Recognition) Speed detection

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Forecast Model for Scheduling an HPC Application on CPU and GPU Architecture 3

Forecast Model for Scheduling an HPC Application on CPU and ...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Chandrashekhar, B.N. Mohan, M. Geetha, V. BMS Institute of Technology & Management Artificial Intelligence and Machine Learning Benguluru India Nitte Meenakshi Institute of Technology Information Science & Engineering Benguluru India Reva University School of Management Bengaluru India

ISBN: (纸本)9798350338607

Process scheduling is an essential part of multiprogramming operating systems. Scheduling is a process that allows one process to use the processing unit while the execution of another process is on hold (in a waiting state) due to the unavailability of any resource like I/O, thereby making full use of CPU or GPU. The major issue of scheduling is to make the system efficient, fast, and fair. This work focuses on developing a Forecast model and constructing scheduling strategies to schedule parallel applications on CPU and GPU. During the design of the Forecast model, we will consider the history of the actual execution time set of processes, then we compute the average time of individual sets of processes by considering parameters such as complete execution time, the sum of processes, and the number of threads assigned to individual processes. Then we will evaluate the Prediction time of CPU and GPU for individual sets of processes. By considering parameters such as the average time of the previous set of processes, the weight of processes, and the number of processes. Then based on the prediction time we will develop a scheduling strategy. As the minimum prediction time required set of process resources is assigned to the CPU and the GPU is assigned by the maximum predicted timed resource of the process. In this work we utilized the CPU and GPU resources effectively for stream benchmark application, our experiment shows that less than 20% average percentage prediction error in all cases. © 2023 IEEE.

关键词： Forecasting

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ML-IBotD: Machine Learning based Intelligent Botnet detection 3

ML-IBotD: Machine Learning based Intelligent Botnet Detectio...

引用

3rd IEEE international conference on Artificial intelligence, ICAI 2023

作者： Arshad, Sobia Zanib, Rida Akram, Adeel Haider, Ali Saeed, Talha Raza, Muhammad Shaheem NCCS DPI Lab UET Taxila Pakistan

ISBN: (纸本)9798350322125

With the advancements in communication technologies, an abundance of smart devices and internet-based applications in every walk of human life has resulted in the production of a huge number of data transmissions over the internet. In line with this emergence, the number of cybersecurity attacks is also rising. Among notable network attacks like mal ware, phishing, etc., we focused on botnet attacks which can cause huge damage on a large scale because botnet works in network form which appears as an adverse risk for the internet. In the botnet, there are many compromised systems known as bots controlled by the botmaster. On the other hand, Machine Learning (ML) is playing an important role in the detection of such network attacks with notable accuracy. In this paper, we select a dataset of CIC-IDS2017 due to its real interpretation of botnets. Then flows are extracted and then relevant four features are selected from the flows. In this paper, we apply four classifiers of SVM, KNN, DT, and Ensemble classifier on a real dataset of CIC-IDS2017. The highest achieved testing accuracy is 99.56% with the Ensemble classifier. © 2023 IEEE.

关键词： Botnet

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Improved Performance of Canny Edge detection in Low-Light Conditions 3

Improved Performance of Canny Edge Detection in Low-Light Co...

引用

3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Dadi, Pradeep Surya Saranya, G. Tamilvizhi, T. Nelson, Leema Surendran, R. Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Amrita Vishwa Vidyaapeetham Chennai Campus Amrita School of Computing Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462167

Visual simultaneous localization and mapping (SLAM), which has numerous potential uses, is swiftly emerging as a significant development in embedded vision. In our analysis, there remain several unsolved dilemmas about SLAM that are visual, making it less favored over Lidar SLAM. Because it offers an affordable solution for mapping and navigation in mobile robots. These mobile robots can be defined as robots whose unique base is not fixed, so as technology establishes and advances, we. In this report, a real-time light, that is, dim or image or video in low light problem enhancement algorithm is proposed that significantly improves the overall performance of the Canny Edge part detection algorithm, which usually may be useful for benefit recognition in low light issues in cellular robots for several mapping and navigation tasks. © 2023 IEEE.

关键词： Mobile robots

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Fully Automated Wheatstone Bridge-Based Transducer Problem Identification with Classifier for Verilog HDL 3

Fully Automated Wheatstone Bridge-Based Transducer Problem I...

引用

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

作者： Choudhary, Sunila Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350399264

This study describes how artificial neural networks (ANNs) were used to develop automated defect monitoring and diagnosis programs in a type of Wheatstone bridge-based actuators. The results indicate that the proposed fault detection is perfect for incorporating awareness within the devices in a form that is interacting with humans. The automated problem detector that is currently exhibited detects the 7 flaws that are frequently found in a category of such actuators. In this study, a two classifying system built on an ANN is used to teach the recommended automated defect detectors. The railway is identified using Verilog in able to differentiate among the outputs at either 1 of its failures, that is strong (1), and the result at the other 3 defects, who are lower (0).Verilog HDL is used to recognise a subway principles, i.e., mass and prejudices from the neurological connectivity toolkit of MATLAB and feedback voltage levels obtained from OrCAD apps, in sequence to differentiate among the outcome at whatever one of the failings, that is elevated (1), and the outcome at the remainder 3 defects, that also are small (0). © 2023 IEEE.

关键词： Fault detection

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Model Distillation for Lane detection on Car-Level Chips 1

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3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Wei, Zixiong Wang, Zerun Chen, Hui Shao, Tianyu Huang, Lihong Kang, Xiaoyun Tian, Xiang Zhuoxi Inst Brain & Intelligence Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China HoloMat Technol Beijing Peoples R China Beijing Normal Univ Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (数字)9789819991198

ISBN: (纸本)9789819991181;9789819991198

As one of the most essential perception modules in the autonomous driving system, the lane detection module must accurately and efficiently detect each traffic lane's location, color and type to secure the vehicle's safety. State-of-the-art lane detection models have already shown great performance using deep and complex architecture. However, since the embedded system of car-level chips such as TDA4 supports limited computing resources and operators, these models could not be deployed directly. Besides, simply compressing models using a smaller backbone usually yields unsatisfied performance. In this work, to tackle these problems, we propose a lightweight and practical lane detection model based on model distillation. Specifically, our model learns the knowledge of a pre-trained teacher model using spatial softmax. We also re-train our model using the method of quantization aware training to further compress the size of model. Compared with the original PINet [1] model, experimental results on TDA4 demonstrate that our proposed method merely consumes a quarter of the parameters while enjoying a close detection precision of 0.7524 and an IoU score of 0.7621.

关键词： Model distillation Lane detection Car-level Chips

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