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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291-300 订阅

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Intelligent ventilation remote practice system based on cloud platform 3

Intelligent ventilation remote practice system based on clou...

引用

3rd international conference on computers, Information processing and Advanced Education, CIPAE 2022

作者： Sun, Weibo Gao, Xuefeng Wang, Yan Sun, Xuan Duan, Siyu Wang, Zihan School of Energy Engineering Xi'An University of Science and Technology Xi'an710054 China

ISBN: (纸本)9781665468121

Based on the current situation of practical education in Colleges, for the purpose of cultivating the practical ability of mine ventilation, this paper summarizes a remote interactive 'intelligent ventilation remote practice system'. This system integrates the virtual experiment system and ventilation experiment model, constructs a cloud practical learning platform, provides students with a series of ventilation practical learning resources, and simulates the offline practical learning process to the greatest extent. It is mainly composed of physical experiment module, electrical control module and cloud control module. Combined with experimental model and virtual experiment platform, the system fully combines traditional and virtual teaching methods to promote students' effective learning. © 2022 IEEE.

关键词： Ventilation

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Standardized detection and counting of sit-ups based on CenterNet 3

Standardized detection and counting of sit-ups based on Cent...

引用

3rd international conference on Artificial Intelligence, Information processing and Cloud Computing, AIIPCC 2022

作者： Zhu, Zhilei Gao, Xiaoming Shi, Yaling Shi, Zhenyu Embedded Technology Lab. School of Computer Science and Technology Southwest University of Science and Technology China Anhui Boshian Intelligent Technology Co. Ltd. Anhui Province Hefei China Wanjiang College of Anhui Normal University Anhui Province Wuhu China

ISBN: (纸本)9783800759330

Motion detection technology based on deep learning can adapt to the impact on recognition accuracy in a variety of complex backgrounds, but the existing algorithms are difficult to ensure real-time and accurate detection of sit-ups. Aiming at this problem, a scheme for the detection and counting of sit-ups based on the Centernet algorithm is proposed. Collect 14,000 samples for training and testing. And analyze the action characteristics of sit-ups, and establish a parameter classification model to judge whether the exercise is performed correctly. In order to improve the recognition accuracy of the Centernet algorithm, an attention mechanism is introduced to pay attention to detailed information to improve the accuracy of detection. In terms of network optimization, deep separable convolution is used to reduce the amount of network parameters. The final experimental results show that the accuracy of the scheme is 91.90%, and the precision is 92.51%. The average FPS reached 30 at the camera resolution of 480P, proving that the solution can be used for sit-up detection and counting accurately and in real time. © VDE VERLAG GMBH.

关键词： Deep learning

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Energy-Efficient Virtual Machine Migration Algorithm in Data Centers 3

Energy-Efficient Virtual Machine Migration Algorithm in Data...

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3rd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Wu, Xiaodong Zeng, Taisheng School of Mathmatics and Computer Science Quanzhou Normal University Fujian Province Quanzhou362000 China Fujian Provincial Key Laboratory of Intensive Computing Fujian Province Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian Province Quanzhou362000 China

The issue of data center power consumption is becoming increasingly serious. One of the most effective techniques for increasing resource utilization and lowering power consumption is virtual machine consolidation. This paper proposes a threshold-based energy-efficient and SLA-aware algorithm for virtual machine migration and consolidation. Based on statistics and analysis of historical virtual machine status, the algorithm dynamically obtains the upper CPU utilization threshold of the physical servers and identifies whether to perform the virtual machine migration, so as to reduce the number of active hosts to save energy. The experimental results demonstrate that the proposed algorithm has better comprehensive performance. When compared to the existing algorithm, our method can effectively decrease energy consumption and SLA violations. © Published under licence by IOP Publishing Ltd.

关键词： Electric power utilization

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Research and Implementation of Sentence Error Correction Method in Thangka Field 3

Research and Implementation of Sentence Error Correction Met...

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2022 3rd international conference on computer Information and Big Data Applications, CIBDA 2022

作者： Wang, Tiejun Cheng, Sujie Wang, Yu Northwest Minzu University School of Mathematics and Computer Science Lanzhou Gansu730000 China Northwest Minzu University National Languages Information Technology Boulder Lanzhou Gansu730000 China Northwest Minzu University Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Lanzhou Gansu730000 China

ISBN: (纸本)9783800758760

Text error correction is one of the research fields of natural language processing, which is applied in search engine, intelligent question answering, associative input and so on. At present, text error correction is mostly in the general domain, and there are few researches on the error correction of Thangka statements. This paper proposes a method of error correction of Thangka statements. Firstly, Thangka data is pre-processed to complete the training of language model and improve the efficiency and accuracy of text recognition. Then detect the suspected wrong position of the statement, and use domain confusion set, word granularity n-gram and word granularity n-gram to detect the wrong word. Finally, the candidate set of editing distance and phonetic/shape near word recall error correction were used, and the candidate results were sorted by the degree of confusion. The method proposed in this paper has a good application value in the field of Thangka error correction. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Error correction

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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...

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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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Chinese Medical Word Embedding Based on Transfer Learning 3

Chinese Medical Word Embedding Based on Transfer Learning

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3rd international conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Zhou, Menglin Gong, Kecun Chengdu University of Information Technology School of Software Engineering Chengdu China Southwest Minzu University School of Computer Science and Engineering Chengdu China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Word embedding has been successfully applied to Natural Language processing (NLP) tasks. In this paper, we learn Chinese medical word embeddings in different ways and use the same corpus to evaluate the quality of the word embeddings. Firstly, we perform word embedding training through target domain data combined with data from different domains to achieve word embedding sharing and enrich the semantic information of word embeddings;Secondly, static word embedding and dynamic word embedding are combined to solve semantic ambiguity by adding dynamic word embedding. Finally, word embedding sharing combined with parameter migration is used to reduce the use of training data and adjust word embedding. Furthermore, we evaluate the quality of word embeddings obtained by different models through three metrics: word analogy, word embedding visualization, and named entity recognition task. By comparing the quality of word embedding in different models, we found that our proposed method improved in each evaluation result. © 2022 IEEE.

关键词： Visualization

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State-of-the-Art Methods for Fine-Grained Emotion Detection from Malayalam Text using Deep Learning: A Survey 3

State-of-the-Art Methods for Fine-Grained Emotion Detection ...

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3rd international conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Anuja, K. Raj, P. C. Reghu Remesh Babu, K.R. Govt. Engineering College Dept. of Computer Science and Engineering Palakkad India A. P. J. Abdul Kalam Technological University Kerala India

ISBN: (纸本)9781665482684

Social media has changed the way people express their opinions, views, and feedback about anything in their minds. This large volume of sentiment-rich data needs to be examined properly in order to comprehend the mood, attitude, or sentiment underlying those words. computers can process, interpret, and analyze vast volumes of natural data using natural language processing (NLP), which helps them to comprehend human languages. Now all organizations are looking for more fine-grained sentiment analysis to plan their business strategy. This paper investigates state-of-the-art textual data analysis using NLP techniques. The increased use of Indian languages in communicating platforms is a challenging task for opinion mining. In this paper, we survey the state-of-the-art methods based on deep learning and fuzzy rules for sentiment analysis. © 2022 IEEE.

关键词： Sentiment analysis

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Character Segmentation on Indian Vehicle License Plate using K-means, OTSU and Niblack algorithm 3

Character Segmentation on Indian Vehicle License Plate using...

引用

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

作者： Jha, Abhishek Singh, Aman Basit Ali, Abdul Jindal, Rajni Delhi Technological University Department of Computer Science & Engineering Delhi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Automatic License Plate Recognition (ALPR) is a growing research area as with the increase in the number of vehicles, requirements for high accuracy ALPR systems are increasing as it has been extensively used for automatic road tax collection, monitor unlike activities etc. Character segmentation is a major step in the ALPR system. This paper includes the use of image pre-processing techniques, K-means clustering, Otsu and Niblack method and Connected Component Analysis (CCA) to perform character segmentation. Unlike existing works which are based on license plates of other countries and therefore may not perform well on Indian license plates, in this work we have dealt with Indian license plates. Many of the existing works are based on license plates that are captured from a straight angle and good illumination and hence may not perform well if the images of license plates have noises or are captured at different angles. Therefore, in this work we have used license plates which are at different angles with different lighting conditions. © 2022 IEEE.

关键词： K-means clustering

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A Novel Fake News Detection Approach Using Machine Learning 3

A Novel Fake News Detection Approach Using Machine Learning

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

作者： Mahijith, K.V. Unni Sunil, Aswin Ashin, C. Sreelathan, Abhinand Thomas, Manoj V. Vimal Jyothi Engineering College Department of Computer Science and Engineering Kerala Kannur670632 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The largest number of smartphone users choose to check statistics through social networks over the Internet. Social media websites post facts and provide certifications. This question may relate to the credibility of knowledge and articles distributed on social networks such as WhatsApp, Facebook pages, Twitter, and various microblogs and social networking *** is very dangerous for society to simply accept rumors and false information as truth. An hour's need is to prevent gossip, especially within developing countries such as India, and to prevent interest in relevant and credible news *** article will show you the versions and how to detect fake news accordingly. We used machine learning and natural language processing to gather news and tried to use Vector's support engine to determine if the news was real or fake. The proposed options is compared with the existing model. The proposed version works correctly and determines accuracy with up to 93.6% accuracy. © 2022 IEEE.

关键词： Support vector machines

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Named entity extraction from images using natural language processing pipelines 3

Named entity extraction from images using natural language p...

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

作者： Lodh, Anirudh Saxena, Utkarsh Markhedkar, Akshay Khan, Ajmal Manoj Votavat, Bhavya Mendon, Akshay Vit Bhopal University School of Computer Science and Engineering Bhopal India Thakur College of Engineering and Technology Dept. of Electronics and Telecommunication Mumbai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Named entity extraction is the process of extracting a specific entity or a piece of information from a bunch of text or an image where a lot of text has been mentioned. For example, identifying the name of a candidate from a resume or picking out the name of a cereal brand from its box. Technologies like optical character recognition and named entity recognition have been widely used in their respective fields of applications. Both of these technologies can be combined to form a pipeline in which we can extract a particular word using named entity recognition from the output digitized text from the optical character recognition system. The combined models and their use cases will be discussed in this paper. The implementation on the drug strips and their various applications both in consumer as well as retailer sub sections will be mentioned. Models and their improvements with recent and concurrent published papers will be discussed. © 2022 IEEE.

关键词： Optical character recognition

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