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检索条件"任意字段=International Conference on Computer and Automation Engineering"

共 58519 条记录，以下是4391-4400 订阅

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

引用

33rd IEEE international conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Computational-Heuristic Algorithm for Tomographic Solution of Industrial Flaw Detection Problems 16

Computational-Heuristic Algorithm for Tomographic Solution o...

引用

16th IEEE international Scientific and Technical conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Peshkov, Alexander V. Zerkal, Sergey M. Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia

ISBN: (纸本)9798350330885

This paper presents the results of the development and study of the application of a computational heuristic algorithm for industrial tomography based on the use of a reference sample of an industrial product. Thus, based on a priori information about the reference sample of the product, it is possible to build a local tomographic reconstruction from limited initial data due to the selective sensitivity of the object under study to probing radiation. This approach is consistent with the principle of "strong methods", which has recently proven itself well in the field of artificial intelligence. The results obtained form the basis for the development of an appropriate information and computing technology, including algorithmic and software for computer processing and interpretation of measurement data. The paper also presents the designed architecture and software prototype with a graphical interface that implements local tomographic reconstruction based on the proposed algorithms. The presented experimental results testify to the efficiency and speed of the proposed algorithm in comparison with the classical algorithm of computed tomography. The proposed algorithm also makes it possible to detect defects of small thickness, when the classical tomography algorithm cannot cope. From an applied point of view, this study is of interest in the heuristic search for a defect in a product model and allows us to conclude that it is promising in the field of industrial flaw detection. © 2023 IEEE.

关键词： Heuristic algorithms

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IoT-Based Power Metering automation for Improving data Integrity and Reliability in Power Marketing Meters

IoT-Based Power Metering Automation for Improving data Integ...

引用

2023 international conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Samreen, Sumaiya Chanti, Yerrolla Mounika, P. Mandava, Manjusha Kruthika, C.G. Malla Reddy Engineering College for Women It Department Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

The goal of this paper is to provide an internet of things (IoT)-based approach for automated electricity metering. When measuring power using standard methods, inaccuracies and erroneous readings are possible, putting the data's integrity and authenticity at risk. The suggested method employs sensors linked to the internet of things (IoT) and machine learning algorithms to detect abnormalities and inconsistencies in real-time energy use statistics. This was evident during the testing phase. The proposed method offers several benefits over the present standard of power metering systems in use. The testing findings demonstrated that the proposed system is more reliable and effective in terms of 0.957 success rate, 0.943 accuracy, 0.967 recall, 0.956 F1 score and 0.982 AUC. © 2023 IEEE.

关键词： Anomaly detection Cloud-based data storage and analysis platform Internet of Things (IoT) Machine learning algorithm Power metering Real-time data analysis User interface

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Catenary Parameter Detection for the High-speed Railway Power Supply System Based on Pauta Criterion 5

Catenary Parameter Detection for the High-speed Railway Powe...

引用

5th international conference on Clean Energy and Electric Power engineering, ICCEPE 2024

作者： Su, Qi Chen, Zhongbin Li, Xuefang Wu, Peng Li, Wei School of Electrical and Automation Engineering East China Jiaotong University Nanchang330013 China Yunnan Power Grid Co. Ltd. Pu'er Power Supply Bureau Yunnan China Shanghai Maritime University Shanghai201306 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences National Key Laboratory of Materials for Integrated Circuits Shanghai200050 China

ISBN: (纸本)9798350390315

The geometric parameters of the contact network are important components of the high-speed railway power supply system, which directly affect the stable operation of the railway. Regular inspection and maintenance are crucial. However, there are often issues with high costs and slow detection speed for the detection devices of geometric parameters. Therefore, it is essential to find a way to achieve fast and efficient detection with high accuracy. In this study, a method is proposed to perform linear fitting on residuals using the compensated target trajectory to reduce the interference of gross errors. Through simulation experiments and comparisons, it is suggested that tracking processing and data anomaly handling should be conducted when acquiring data on the upper computer. Furthermore, the Kalman filtering algorithm can effectively reduce the influence of gross errors on the accuracy of geometric parameters, enabling accurate tracking of abnormal targets and improving detection efficiency. Additionally, the static measurement accuracy is improved. © 2024 IEEE.

关键词： Kalman filters

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Efficient video classification of marine organisms through intra and inter prediction

Efficient video classification of marine organisms through i...

引用

2022 international conference on Signal Processing, computer Networks, and Communications, SPCNC 2022

作者： Li, Borui Feng, Qingjuan Zhang, Yang College of Automation Beijing Information Science & Technology University Beijing100192 China

ISBN: (纸本)9781510664616

The purpose of this paper is to improve the efficiency of video recognition process and save computational data. According to the encoding and decoding of video, we can know that not every part of the information of each frame is valid in video classification. There are a lot of invalid information, which takes up computing space. Therefore, this paper proposes an efficient video classification and prediction method based on reinforcement learning intra prediction. The process of eliminating time redundancy is further added, and then the video frames with low value are ignored, which further improves the computational efficiency. This method extracts the key frames from the marine biological video, and then focuses on the key image areas of the key frames, so as to reduce the network computing overhead. © 2023 SPIE.

关键词： Computational efficiency

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Spatial information considered convolutional neural network for electroencephalogram-based motor imagery classification 3

Spatial information considered convolutional neural network ...

引用

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

作者： Shi, Hongbing Zhang, Jinhui Pei, Zhongcai School of Automation Science and Electrical Engineering Beihang University Beijing China North Information Control Research Institute Group Co. Ltd. China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

As brain-computer interface (BCI) technology continues to advance in various fields, it has become one of the possible solutions for patients with motor dysfunction who have healthy thinking ability to regain motor ability. The vigorous development of deep learning (DL) provides it with a possible tool to analyze electroencephalogram (EEG) signals. Through analyzing and categorizing EEG signals associated with motor imagery (MI), the system can effectively perceive the patient's motor intentions. Currently, Convolutional Neural Networks (CNN) have exhibited exceptional performance in a variety of fields, including computer vision (CV) and natural language processing (NLP). However, the brain structure has rich spatial information, which was not fully utilized by CNN for MI-EEG signal analysis in the past. This paper introduces SP-CNN, a convolutional neural network that incorporates spatial information from the brain, to address the classification challenge of MI-EEG signals. The experimental findings indicate that this method exhibits stable and robust performance across diverse subjects. © 2023 SPIE.

关键词： Electroencephalography

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Utilizing Blockchain for Effective Urban Street Vendor Management 4

Utilizing Blockchain for Effective Urban Street Vendor Manag...

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4th international conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Singh, Pushpa Dash, Snigdha Gupta, Aditya Kumar Singh, Narendra Yadav, Prashant Dev Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India School of Business Galgotias University Greater Noida India Amity International Business School Amity University Noida India Gl Bajaj Institute of Technology and Management Department of Management Studies Greater Noida India Noida India

ISBN: (纸本)9798350393248

Blockchain technology provides efficient, transparent and secure street vendor management. The paper aims to investigate the role of blockchain in street vendor management in the city. The study proposes to solve the problem at both ends for municipal corporations to manage street vendors and have a transparent system to address their issues. We suggest a model to improve the functioning of the municipal corporation and help the local administration. The paper focuses on a Blockchain-based Street Vendor Management (SVM) that automates the registration process and allot a street ID to the street vendor. SVM utilised the power of blockchain technology to offer a secure, efficient, immutable and transparent system. Ethereum-based smart contracts are used to build a standard nationwide registration process. The main players of SVM are street vendors, local administrators, and global administrators. Further, the proposed model can be integrated to perform transactions among customers, street vendors, local and global administrators. © 2023 IEEE.

关键词： Blockchain

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Relevance Vector Machine for Code Smell Detection 22

Relevance Vector Machine for Code Smell Detection

引用

22nd IEEE international conference on Industrial Informatics, INDIN 2024

作者： Putro, Hanson Prihantoro Yuhana, Umi Laili Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Engineering Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798331527471

Various approaches have been proposed to detect code smells, including machine learning models. However, there are still challenges to improving detection accuracy and selecting appropriate quality metrics. This research introduces the use of Relevance Vector Machine (RVM) for code smell detection, especially Magic Value smells, which often affect source code readability and maintainability in a software development industry. This approach utilizes source code analysis using two source code representations (raw source code and token stream) and feature extraction using two text mining techniques (Bag of Words/BoW and Term Frequency-Inverse Document Frequency/TF-IDF). The experiment involves analyzing 983 records of labeled source code datasets. The results show that RVM can detect code smells with a very high level of accuracy, reaching 99.9% on some kernel configurations, also nearly perfect precision, and F1 scores. This paper contributes to introducing the use of RVM for detecting code smells in source code, explores source code representation and feature extraction techniques, and shows that RVM can achieve high detection accuracy. © 2024 IEEE.

关键词： Software design

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Prediction of Telecommunication Company Stock Price using Multiple Linear Regression 3

Prediction of Telecommunication Company Stock Price using Mu...

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3rd international conference on Smart Cities, automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Parjito Abidin, Zaenal Pambudi, Agung Tyas, Dyah Aruming Susanto, Erliyan Redy Suryono, Ryan Randy Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9781509062805

This study presents case studies in finance, mainly stock trading. The paperwork from a portfolio data mining effort and data obtained directly from the Indonesian Stock Exchange serves as the basis for the case study. A technique for assessing future occurrences based on relational patterns in data is prediction (forecasting). This technique is used in field data mining on time-series data that is numerical in characters, such as dates or attribute dates. Examples of prediction techniques include anticipating the temperature, weather, foreign exchange rates, and stock prices for the upcoming three months, determining whether the speed limit will be increased, and more. The 'close' variable is the target variable. At the same time, the 'prior, open price, first trade, high, low, index individual, offer, bid' variables are predictive variables that can be used to extract information that can improve the accuracy of the forecast value - the stock prediction outcomes of PT. Telkom Indonesia Tbk (TLKM) is predicted using the data mining model developed using time series data. Investors can use this information to decide what to do or establish procedures to prevent losses while buying and selling stocks. Testing R2, AUC, MAE, MSE, RMSE, and MAPE determine the accuracy of the stock prediction results. The experimental results from the best MLR model after experimenting with feature engineering yielded an R2 value of 100%, an RMSE value of 0.0548, and a MAPE value of 0.0013%. © 2023 IEEE.

关键词： Multiple linear regression

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Based on End-to-End Object Detection Algorithm with Transformers for Detecting Wafer Maps 5

Based on End-to-End Object Detection Algorithm with Transfor...

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5th international conference on computer Network, Electronic and automation, ICCNEA 2022

作者： Song, Danni Liu, Baolong Li, Yang Xi'an Technological University School of Computer Science and Engineering Xi'an China Product Center Casi Vision Xi'an China

ISBN: (数字)9781665491099

ISBN: (纸本)9781665491099

With the gradual increasing of wafer fabrication yield, defect pattern recognition in wafer maps becomes more challenging, and thus it is especially important to identify problems in the process as early as possible to avoid waste, and new ideas are provided in deep learning for defect pattern recognition (DPR) of wafer maps. In this paper, an End-to-End Object Detection with Transformers is introduced to process wafer defect maps by migrating the learning weights and fine-tuning the parameters. The test results show that the DETR based algorithm for wafer detection improves 8.4% in accuracy compared to the improved Mask R-CNN detector. © 2022 IEEE.

关键词： Defects

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