检索结果-内蒙古大学图书馆

A Seismic Phase-Picking Model Transfer Learning Approach Based on Maximum Mean Discrepancy 3

A Seismic Phase-Picking Model Transfer Learning Approach Bas...

3rd international conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Zhu, Kun'ao Huang, Yongming Chen, Jun Sun, Yejun School of Automation Southeast University Nanjing China School of Electronic Information and Electrical Engineering Hefei Normal University Hefei China Jiangsu Earthquake Agency Nanjing China

ISBN: (纸本)9798350372052

Thanks to the abundance of earthquake data and the advancement of deep neural networks, many seismic phase-picking models have been proposed to detect the arrival time of P-waves and S-waves. Currently, the accuracy of these models exceeds that of traditional methods, such as short-term average/long-term average (STA/LTA). While many studies concentrate on phase picks on a single seismic dataset, there is limited research regarding the generalization of phase-picking models. Specifically, applying a model trained on one seismic dataset to an unseen dataset remains underexplored. A phase-picking model is designed in this paper, which consists of an encoder and a decoder. The self-attention is applied in the encoder module to focus seismic features. Then a seismic phase-picking model transfer learning approach based on maximum mean discrepancy (SPPTL-MMD) is proposed to train a universal seismic feature extraction module by utilizing diverse seismic datasets. The module will be transferred and a significantly improved phase-picking model is obtained through fine-tuning techniques. Experiments show that the model after fine-tuning can be generalized to other datasets. © 2023 IEEE.

关键词： signal encoding

来源：评论

学校读者我要写书评

暂无评论

Advanced Bandwidth Management Techniques Using Wireless Mesh Networks for High-Speed Optical Fiber Communications 3

Advanced Bandwidth Management Techniques Using Wireless Mesh...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Yadav, Rakesh Kumar Sharma, Sunil Seth, Kanika Jeyalaxmi, M. Ingle, Yashwant S. Vivek, V. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This abstract encompasses superior bandwidth management techniques the usage of Wi-Fi mesh networks to improve the performance of high-velocity optical fiber communications. the point of interest lies at the access-, relay-and center-mesh architectures with their diverse, discrete techniques consisting of dynamic frequency allocation, load balancing and node optimization, as well as on highlights from latest research. Within the experiments, the effects exhibit giant enhancement in signal first-rate and effectiveness of bandwidth resources when the use of the proposed version, based totally at the convergence speed of the network fee. Furthermore, it's also evident that the optimized wireless mesh configuration brings about enormous fee financial savings. All the findings obtained at some stage in this research have eminently greater the talents of the proposed structure for growing green communications networks. © 2023 IEEE.

关键词： Communications Convergence Experiments Networks Superior

来源：评论

学校读者我要写书评

暂无评论

Exploring the Effectiveness of BERT for Sentiment Analysis on Large-Scale Social Media data 3

Exploring the Effectiveness of BERT for Sentiment Analysis o...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Bikku, Thulasi Jarugula, Jyothi Kongala, Lavanya Tummala, Navya Deepthi Vardhani Donthiboina, Naga NRI Institute of Technology Computer Science and Engineering Visadala Guntur India NIT Mizoram Computer Science and Engineering India K L Deemed to Be University Computer Science and Engineering Vaddeswaram India Vignan's Nirula Institute of Technology and Science for Women Computer Science and Engineering Guntur India

ISBN: (纸本)9798350338607

Sentiment analysis is a crucial task in the field of natural language processing (NLP) and has gained significant attention due to the widespread use of social media platforms. Social media data presents unique challenges for sentiment analysis due to its unstructured nature, informal language, and abundance of noise and irrelevant information. To tackle these challenges, advanced techniques such as BERT have emerged as powerful tools for sentiment analysis. In our study, we aim to explore the effectiveness of BERT specifically for sentiment analysis on large-scale social media data. BERT is a state-of-the-art language model that has demonstrated impressive performance on various NLP tasks by capturing contextual information from both left and right contexts of a given word. By leveraging the pre-training and fine-tuning capabilities of BERT, we investigate its potential for sentiment analysis in the context of social media. To establish a comprehensive evaluation, we compare the performance of BERT with traditional machine learning algorithms commonly used for sentiment analysis. Our experimental results indicate that BERT surpasses the performance of traditional machine learning algorithms, achieving state-of-the-art results in sentiment analysis on the social media dataset. BERT's ability to capture intricate contextual information and understand the subtleties of social media language contributes to its superior performance. The model demonstrates exceptional accuracy, precision, recall, and F1-score, showcasing its effectiveness in classifying sentiment labels accurately. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

A combined wind speed prediction model based on data processing, multi-objective optimization and machine learning

引用

ENERGY REPORTS 2023年 9卷 413-421页

作者： Wang, He Li, Yan Xiong, Min Chen, Hongfeng Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Energy Dev Res Inst China Southern Power Grid Guangzhou 510623 Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN USA

The ability to estimate wind speed accurately is crucial for optimizing the utilization of wind energy in power systems. The randomness of wind speed and the performance of prediction models are the two issues that have the greatest impact on wind speed prediction. This paper starts from the two aspects of digging wind speed characteristics and optimizing model performance. A new wind speed combined prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved artificial bee colony algorithm (IABC) to optimize least squares support vector machine (LSSVM) performance. In order to reduce the randomness of the wind speed sequence and increase its predictability, CEEMDAN is first used to break down the wind speed sequence. Secondly, an LSSVM prediction model optimized by an improved artificial bee colony algorithm is established for each subsequence obtained by decomposition;finally, the wind speed forecast value is created by superimposing the results of each subsequence's prediction. Case studies demonstrate that the combined prediction model put out in this study outperforms other models in terms of prediction performance. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Wind speed forecasting CEEMDAN Improved artificial bee colony Least squares support vector machine Combined model

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Potential of Deep Learning for Enhancing data Science Practices 3

Analyzing the Potential of Deep Learning for Enhancing Data ...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mercilin Raajini, X. Deshmukh, Gopal B. Prabha, Savita Singh, Jagtej Agarwal, Ankita Manu, S.E. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India

ISBN: (纸本)9798350319125

Deep learning is a progressive new field of artificial intelligence that has drastically changed the landscape of technology over the last few years. It has enabled the creation of clever packages, including self-using motors, facial recognition, natural language processing, and others. It has also affected information science, with deep getting to know presenting a new set of tools to system and examine information. This paper discusses the capacity of deep gaining knowledge for enhancing records technological know-how practices, consisting of predictive analytics, visible insights, and self-studying structures. For each subject, the paper provides a short evaluation accompanied by an evaluation and discussion. Subsequently, it provides an outline of cutting-edge developments and guidelines for how records scientists can OK use their abilities and revel to capitalize on the capacity of deep learning. © 2023 IEEE.

关键词： capitalize discussion learning recommendations scientists

来源：评论

学校读者我要写书评

暂无评论

Sparse signal recovery for multispectral demosaicking using pseudo-panchromatic image 3

Sparse signal recovery for multispectral demosaicking using ...

引用

3rd international conference on Image processing and Intelligent Control, IPIC 2023

作者： Liao, Ronghao Zhou, Shifu Wu, Guangyuan Jinan250353 China Shandong Publicity public opinion Research Center Jinan250353 China

ISBN: (纸本)9781510668140

Single-sensor multispectral imaging technology has been widely used in computer vision, mechanical diagnosis, cultural history protection and other industries due to its convenience and low cost. Single-sensor multispectral imaging can only generate a single mosaic image, so an efficient method is needed to convert mosaic images into multispectral images. Based on the concept of pseudo-panchromatic image, a 9-band multispectral imaging system is designed in this paper. We directly estimate the pseudo-panchromatic image from the mosaic image, and use the correlation between the pseudo-panchromatic image and each channel to generate a multispectral image by guided filtering and residual interpolation. The experimental results show that the multispectral images obtained by our method are superior to the other two methods in objective and subjective evaluation. © 2023 SPIE.

关键词： Costs

来源：评论

学校读者我要写书评

暂无评论

An Energy-Efficient Metaheuristic for Offloading Security-Critical Tasks in Mobile Edge Computing with Integrated DPUs 3

An Energy-Efficient Metaheuristic for Offloading Security-Cr...

引用

3rd IEEE international conference on Intelligent Technology and Embedded Systems, ICITES 2023

作者： Hong, Shaoying Zhu, Jiayin Yuan, Yiqi Sun, Jin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China International Joint Audit Institute Nanjing Audit Technology Nanjing211815 China

ISBN: (纸本)9798350343755

In mobile edge computing (MEC) paradigm, for the security-critical computation tasks offloaded from the mobile device, the MEC server needs to decrypt the encrypted task data before task execution, leading to heavy computation load on the CPUs of the MEC server. The integration of data processing unit (DPU) into the MEC server can release CPUs from data decrpytion for the purpose of reducing computation and energy overheads. This paper studies a task offloading problem in a MEC system with integrated DPUs, which aims at reducing the total energy consumption under the deadline constraint on task completion time. We formulate the studied problem as an combinatorial optimization model and propose a group mapping-based cuckoo search (GMCS) metaheuristic algorithm to explore high-quality energy-efficient solutions to the formulated model. The proposed GMCS introduces a group mapping operator and a greedy-based task offloading strategy for converting cuckoo individuals into offloading solutions and evaluating the objective values of the converted solutions, respectively. We provide a theoretical analysis to justify that the mapping operator can improve the diversity of the converted solutions and in turn the metaheuristic’s searching capability. We create various testing instances for a MEC system equipped with DPUs to demonstrate the proposed GMCS produces high-quality solutions to the studied offloading problem, with reduced energy consumption compared with baseline algorithms. © 2023 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

From data Pre-processing to Hate Speech Detection: An Interdisciplinary Study on Women-targeted Online Abuse 3

From Data Pre-processing to Hate Speech Detection: An Interd...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Rathod, Rutuja G. Barve, Yashoda Saini, Jatinderkumar R. Rathod, Sourav Surayadatta College of Management Information & Research Technology Pune India Pune India AI&ML Deparment Pune India

ISBN: (纸本)9798350338607

The term 'misogyny' conveys hatred and disrespect for women. It is a type of sexism that stems from the idea that women are less valuable than men and can take many forms, such as verbal abuse, sexual harassment, and physical violence. One area where misogyny is particularly prevalent is in online spaces. Women who speak out and share their experiences online often face vicious attacks, including violence, rape, and death threats. This phenomenon, often referred to as 'online harassment,' can have devastating effects on women's mental health and well-being. Despite the growing recognition of this issue, there is still a lack of understanding about the nature and extent of hate speech toward women on social media. In this paper, the authors aim to address this gap by analyzing a dataset of tweets on Twitter containing hate speech toward women. The authors introduced the Measuring Hate Speech corpus, a dataset used while studying hate speech towards women. From this dataset, the authors extract some of them to build the model. And authors proposed Machine Learning algorithms like Logistic Regression with 0.92% accuracy. And to look further to fill the gap, the authors developed Support Vector Machine (SVM) algorithm, Topic Modeling technique to extract the topics from the corpus. This study contributes to a better understanding of hate speech on social media platforms as Twitter informs strategies for resisting this pervasive problem. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Time-Weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection 48

Time-Weighted Frequency Domain Audio Representation with GMM...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Guan, Jian Liu, Youde Zhu, Qiaoxi Zheng, Tieran Han, Jiqing Wang, Wenwu Harbin Engineering University Group of Intelligent Signal Processing College of Computer Science and Technology China Harbin Institute of Technology School of Computer Science and Technology China University of Technology Sydney Centre for Audio Acoustics and Vibration Australia University of Surrey Centre for Vision Speech and Signal Processing United Kingdom

ISBN: (纸本)9781728163277

Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer parameters. Existing statistical frequency representations, e.g. the log-Mel spectrogram's average or maximum over time, do not always work well for different machines. This paper presents Time-Weighted Frequency Domain Representation (TWFR) with the GMM method (TWFR-GMM) for anomalous sound detection. The TWFR is a generalized statistical frequency domain representation that can adapt to different machine types, using the global weighted ranking pooling over time-domain. This allows GMM estimator to recognize anomalies, even under domain-shift conditions, as visualized with a Mahalanobis distance-based metric. Experiments on DCASE 2022 Challenge Task2 dataset show that our method has better detection performance than recent deep learning methods. TWFR-GMM is the core of our submission that achieved the 3rd place in DCASE 2022 Challenge Task2. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Retrieval Quality Through Big data and AI Technologies 3

Enhancing Retrieval Quality Through Big Data and AI Technolo...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Abhinav Singh, Akhilendra Pratap Shankar, Amar Kaushik, Harshita Divya, K. Disha, S. Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Food Technology Faculty of Engineering and Technology Karnataka Ramnagar India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Wankhede Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

Huge facts and AI technologies have superior speedy to end up the primary drivers of innovation and progress in lots of one-of-a-kind areas, along with statistics retrieval. through the evaluation of big datasets of formerly retrieved documents and question logs, AI algorithms are capable of expect and perceive the maximum applicable files and also are used to higher recognize and respond to consumer queries. additionally, AI technology which include natural language processing are already beginning to shape the manner human beings engage with engines like Google, permitting extra conversational, person-friendly seek experience. eventually, massive information and AI have the capability to help form semantically based collections of documents with keywords and entities robotically extracted from content, making it easier for a search engine to analyze and connect files higher. This may in the end result in better retrieval satisfactory and greater dynamic records exploration studies for users of search engines. © 2023 IEEE.

关键词： analyze dynamic exploration records search

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：