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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

共 761 条记录，以下是181-190 订阅

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Optimized Fault Detection and Classification for 3-Phase Electric Drives-An AI/ML Approach 3

Optimized Fault Detection and Classification for 3-Phase Ele...

引用

3rd international conference on Innovations in science and Technology for Sustainable Development, ICISTSD 2022

作者： Giri, Antarip Mehendale, Niranjan Waghode, Nitesh Cognizant Technology Solutions India India

ISBN: (纸本)9781665499361

This paper illustrates the creation of robust data inference pipeline for fault classification in a 3-phase induction motor which enables to analyze, evaluate, and compare various machine learning (ML) classification algorithms for detecting inter-Turn short circuit faults in induction motor. The data is analyzed in the time and frequency domain using signal processing techniques such as FFT/PSD and further processed with filtering, and peak detection to extract the important feature dataset for classification algorithm. A Recursive Feature Elimination (RFE) with six different estimator settings are compared to select optimum feature set, which ensured enhanced performance for classification model. Further, five classification algorithms viz, random forest, nearest neighbors, and gradient boosting classifier, linear support vector machines and Naïve Bayes, are devised, tuned, and compared for train-Test accuracy, time profile etc. The study concludes that RFE with decision-Tree based estimator followed by random forest classifier gives the results with highest accuracy (~98%). Further, adding cross-validation technique to RFE (RFECV), ~90% decrease in training time is observed. This work can be extended for analysis of multiple frequencies for VFD based control and varying operating loads. © 2022 IEEE.

关键词： Induction motors

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A Review of Conversational Agents in Education 3rd

A Review of Conversational Agents in Education

引用

3rd international conference on Technology and Innovation in Learning, Teaching and Education (TECH-EDU)

作者： Rodrigues, Carlos Reis, Arsenio Pereira, Rodrigo Martins, Paulo Sousa, Jose Pinto, Tiago Univ Tras Os Montes & Alto Douro Vila Real Portugal Univ Aberta Aberta Portugal INESC TEC Vila Real Portugal

ISBN: (纸本)9783031229176;9783031229183

The use of mobile conversations is increasing all around the world. A conversational agent (CA) is mostly useful due to the fast response times and their simple nature. Recently, we have seen the development and increasing use of dialog systems on the Web. A conversational agent (CA) is a system capable of conversing with a user in natural language, in a way that it simulates a human dialog. Examples of CA can be found in several areas, including healthcare, entertainment, business, and education. In this paper a state of the art review of these dialog systems is presented, comprising different categories, different approaches and trends. The purpose of this work is to identify and compare the main existing approaches for building CA, categorizing them and highlighting the main strengths and weaknesses. Furthermore, it seeks to contextualize their use in an educational context and to discover the issues related to this task that may help in the choice of future investigations in the area of conversational natural language processing in educational context.

关键词： Conversational agents Education Natural language processing

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Information Recommendation of English Oral Teaching Resource Library Based on Collaborative Filtering and Support Vector Machine 3

Information Recommendation of English Oral Teaching Resource...

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3rd international signal processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Chen, Hang Zhang, Meifang He, Weiying School of Information Engineering Guangzhou Vocational College of Technology and Business Guangdong Guangzhou511442 China School of Foreign Languages Guangzhou Institute of Science and Technology Guangdong Guangzhou510540 China School of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangdong Guangzhou510540 China

ISBN: (纸本)9798350307023

In order to improve the sharing and real-time access scheduling capabilities of English oral teaching resource library information, a collaborative filtering and support vector machine based English oral teaching resource library information recommendation model is proposed. Using the keyword graph model and text semantic feature detection method to construct an information allocation network structure model for English oral teaching resource library, using collaborative filtering method to achieve keyword feature detection and interference information filtering for English oral teaching resources, and combining support machine classification recognition method to extract semantic keywords from text pairs, Introducing syntax parsing tree and pre trained language model to establish semantic information tree learning in the information recommendation process of English oral teaching resource library, constructing a text graph model based on extracted keywords, fully utilizing the semantic information tree of the text to pair the recommendation information between learning nodes and neighboring nodes, and inputting the generated text sequence into the recommendation sequence vector table, Combining collaborative filtering method and support vector machine algorithm to achieve information recommendation for English oral teaching resource library. The simulation results show that this method has good feature matching ability for recommending information in the English oral teaching resource library, with an Fl of 87.3 % on datasets such as CNSE. It has strong filtering ability for mixed information in the context of English oral resources, and improves the ability to accurately recommend information. © 2023 IEEE.

关键词： Support vector machines

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Research on intelligent construction data resource catalogue based on power grid business 3

Research on intelligent construction data resource catalogue...

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3rd international conference on computer science and Communication Technology, ICCSCT 2022

作者： Gao, Lingchao Chen, Xiangzhou Liu, Shunhua Zheng, Fei State Grid Big Data Center Beijing100031 China Beijing China-Power Information Technology Co. Ltd. Beijing100089 China

ISBN: (纸本)9781510661240

With the high-quality development of informatization, power grid enterprises promote the steady development of various businesses by virtue of strong technical barriers. In this paper, it is urgent to carry out research on the construction of intelligent data resource catalogue based on machine learning technology for the sake of consolidating the data application foundation of power grid enterprises, facilitating the potential value of data and optimizing the intelligent level of data services rapidly. Additionally, in this work, we analyze the framework, construction process and key technologies of intelligent data resource catalogue. Ultimately, this work realizes the intelligent construction of data resources in power grid enterprises. © 2022 SPIE.

关键词： Analytical research Data centers Data modeling Data storage Data processing Machine learning Evolutionary algorithms Data analysis Databases Internet

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Network-Based Control for Nonlinear System 3

Network-Based Control for Nonlinear System

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Latheef, Abdul Benny, Soniya Devadhas, Glan Chemperi Apj Abdul Kalam Technological University Vimal Jyothi Engineering College Kerala Thiruvananthapuram India Vimal Jyothi Engineering College Computer Science and Engineering Chemperi Kannur India Vimal Jyothi Engineering College Electronics and Instrumentation Chemperi Kannur India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The most popular technique for classifying images now is the convolutional neural network. During its training, it is crucial to adjust two factors: the learning rate and gradient. The pre-processing phases involve the fuzzy clustering approach. To change the learning rate, two Event-Based control loops called E (Exponential) and PD (Proportional Derivative)-Control are used. An exponential control loop is employed to prevent an abrupt decline in learning rate as the model gets closer to its ideal state. The learning rate is used by the proportional derivative control loop to determine when to move on to the subsequent data batch. The weight is updated using the back propagation approach based on the loss value that was determined. The proposed method improves the performance with an Accuracy of 96.8%. © 2022 IEEE.

关键词： Backpropagation

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Sentimental Analysis Using Supervised Learning Algorithms 3

Sentimental Analysis Using Supervised Learning Algorithms

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3rd international conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Shah, Devansh Singh, Arun Prasad, Sudha Shanker Lovely Professional University School of Computer Science and Engineering Punjab Jalandhar India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The humongous growth of online-grounded operations, similar to forums and blogs, led to commentary and reviews related to diurnal conditioning. Sentimental analysis is the process of collection of data and analyzing one's studies, ideas, also extremities about colorful motifs, products, motifs, and services. One's ideas can be useful to companies, governmental organizations, and individualities by gathering data and practicing vision-grounded opinions. Still, emotional analysis and the process of assessment are at stake numerous challenges. These challenges produce walls to directly interpret feelings and determine applicable emotional diversity. Emotional judges and excerpt practical information from the textbook using natural language processing as well as digging the textbook. This composition discusses a complete idea of how to negotiate this work and the use of Sentimental analysis. Also, it is responsible for evaluating, comparing, and investigating the styles used to understand both good and bad. Eventually, Sentimental analysis challenges are explored to define unborn directions. © 2022 IEEE.

关键词： Sentiment analysis

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Drone Assisted Forest Structural Classification of Kejimkujik National Park using Deep Learning 3

Drone Assisted Forest Structural Classification of Kejimkuji...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Roy, Sutirtha Bose, Sarosij Harper, Karen Jaiswal, Vaibhav Bansal, Manu Thapar Institute of Engineering and Technology Electronics and Instrumentation Engineering Patiala India University of Calcutta Computer Science and Engineering Kolkata India Saint Mary's University Biology Halifax Canada

ISBN: (纸本)9781665462006

The wide array of terrestrial forest and wooded lands is one of the richest sources of biodiversity because of inherent structural diversity. Structure plays a significant role in a diversity indicator of a forest. We propose a transfer learning framework consisting of the ResNet-50 architecture for which we obtained a test accuracy of 75.86%. Analysis of structural diversity of the Kejimkujik National Park was done with the help of a drone using deep learning methods that predict the structural class of the forest. We used a novel forest structural diversity dataset collected using DJI Mavic drone to train the deep learning model. © 2022 IEEE.

关键词： Drones

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Development of Digital Algorithm for Flicker Noise Adaptive Non-Linear Filtering 11

Development of Digital Algorithm for Flicker Noise Adaptive ...

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11th Mediterranean conference on Embedded Computing (MECO) / 3rd Summer School on Cyber-Physical + Systems and Internet of Things (CPS and IoT)

作者： Parshin, Alexander Parshin, Yuri Ryazan State Radio Engn Univ Radio Devices Dept Ryazan Russia

ISBN: (纸本)9781665468282

The article deals with the problem of ultra-narrowband IoT signals processing in the presence of thermal noise and flicker noise (FN). To achieve the maximum efficiency of processing under conditions of a priori uncertainty, adaptation of the parameters of the FN model is used. An adaptive filtering algorithm is obtained based on the logarithm of the likelihood ratio in discrete time. The computer simulation method is used to study the speed and accuracy of adaptive parameter adjustment. The proposed method is relevant in the development of wireless sensors in the Internet of Things NB-IoT systems, as well as in other systems for receiving and transmitting ultra-narrowband signals.

关键词： flicker noise phase noise stochastic model non-Gaussian model adaptive signal processing

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Research on Graphic Design of Digital Media Art Based on computer Aided Algorithm 3rd

Research on Graphic Design of Digital Media Art Based on Com...

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3rd international conference on 3D Imaging Technologies - Multidimensional signal processing and Deep Learning (3DIT-MSP and DL)

作者： Xu, Saihua Nanchang Inst Sci Technol Nanchang Jiangxi Peoples R China

ISBN: (纸本)9789811924484;9789811924477

Digital media art graphic design in the new media environment began to be used in many fields in a dynamic way. Especially in the background of big data era, art graphic design with computer-aided algorithm as the core is not only the main direction of practical exploration, but also an important content to meet the development of the industry. In this article, therefore, to understand digital media, on the basis of dynamic graphical development history, according to the digital age of graphic design content and characteristics of dynamic and systematic research on the unique function of visual graphic design, and then validated combining computer aided the shape interpolation algorithm analysis, the final results show that based on shape interpolation can meet the demand of the subjective visual experience, or the objective laws of physics.

关键词： computer aided algorithm Digital media Art graphics Shape interpolation

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Network anomaly detection based on ensemble learning 3

Network anomaly detection based on ensemble learning

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3rd international conference on computer science and Communication Technology, ICCSCT 2022

作者： Pan, Tong Chen, Wei Qian, Long School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781510661240

With the improvement of facility condition and network speed, the network traffic has also presented an exponential growth in recent years. However, a growing number of problems concerning cyber security have appeared. Anomaly network traffic detection can identify abnormal traffic from massive traffic. Accurate identification can reduce anomaly network traffic and protect user client. Our research is based on the traffic collected and processed by National Chiao Tung University. These data include normal traffic and abnormal traffic, the former one being majority. We propose a processing method with high accuracy. We first pre-sampled the data set, and then analysed the data features. Later on, based on theoretical research, we integrated the model of random forests and other boosting method and proposed a greedy multi-classification to binary classification model based on ensemble learning model. © 2022 SPIE.

关键词： Anomaly detection

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