检索结果-内蒙古大学图书馆

A Study on Verification of Sewage Treatment Expert Operating Experience Using RNN

Transactions of the Korean Institute of Electrical Engineers 2025年第4期74卷 691-699页

作者： Nahm, Eui-Seok Dept. of AI Computer Engineering Far East University Korea Republic of

In this paper, we proposed a data quality and diversity guarantee plan and a method to verify the expert operation experience using RNN and a data quality and diversity guarantee plan to 1) solve the two problems of the sewage treatment expert driving experience and 2) the limitation of situation recognition and judgment. As a solution to the lack of data quality and diversity, not all data are used, but a method of filtering and using only the part that is the basis for determining the expert's experience was proposed. In addition, RNN was used to solve the problem of the limitation of situation recognition and judgment. The reason why RNN is used among various neural networks is that RNN is a neural network architecture for deep learning that predicts time series or sequential data, and sewage treatment data is also time series or sequential data. According to the proposed technique, a RNN model with a reliability of more than 8% was implemented, and the effectiveness of the proposed technique was verified by applying two experiences and one incorrect experience of actual operation experts of the target sewage treatment plant. As a result of the verification, both the actual driving expert's experiences 1 and 2 were verified as valid, and for incorrect experiences, there were 3,442 data that matched the expert's experience and 4,444 data that did not match, so the proposed method can verify the expert's experience. © The Korean Institute of Electrical Engineers.

关键词： Sewage treatment plants

来源：评论

学校读者我要写书评

暂无评论

MSA-YOLOv5: An Improved Light-Weight YOLOv5 Model for Small Object Detection 15

MSA-YOLOv5: An Improved Light-Weight YOLOv5 Model for Small ...

引用

15th International Conference on Information and Communication Technology convergence, ICTC 2024

作者： Park, Jong-Chan Kim, Myeongjun Kim, Gun-Woo Gyeongsang National University Dept. of Ai Convergence Engineering Jinju Korea Republic of Gyeongsang National University Dept. of Computer Science & Engineering Jinju Korea Republic of

ISBN: (纸本)9798350364637

Recent object detection research has primarily focused on achieving high accuracy, often overlooking model efficiency and size. To address this issue, we propose the Multi-SpatialLite Attention (MSA) YOLOv5 model, which emphasizes a balance between accuracy and model size. The proposed model was trained on the VHR-10 dataset for small object detection, achieving a lightweight design while maintaining excellent performance in small object detection. Experimental results show that the proposed model achieved 97.6% precision, 99.0% recall, and 97.0% F1-score, while maintaining a lightweight structure with 1.795 million parameters and demonstrating outstanding performance with a mean Average Precision (mAP) of 98.3%. These results suggest the model's ability to enhance precise detection of small objects and provide effective real-time performance even in limited hardware environments. Notably, it has surpassed existing models in applications requiring real-time performance in constrained hardware environments. © 2024 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

Phase Fraction Calculation in Multiphase Steel Microstructures Through Semantic Segmentation 10

Phase Fraction Calculation in Multiphase Steel Microstructur...

引用

10th International Conference on Applied System Innovation, ICASI 2024

作者： Swain, Bishal Ranjan Cheoi, Kyung Joo Ko, Jaepil Kumoh National Institute of Technology Dept. of Computer & Ai Convergence Engineering Gumi Korea Republic of Chungbuk National University Dept. of Computer Science Cheonju Korea Republic of

ISBN: (纸本)9798350394924

The characterization of a material is defined by its transformation kinetics which is measured using the fraction of volume transformed. In common situations, a polycrystalline parent phase is transformed into various sub-phase constituent regions based on the desired material property. Characterization of such multiphase regions include accurate identification of phase and calculation of the phase fraction. To this end, we propose using semantic segmentation to identify and calculate the phase fraction in steel microstructures. We assign each pixel in an image to accomplish both phase identification and segmentation which we perform using enhanced ELU-Net coupled with improved combined loss function. Though scalable experiments we demonstrate more accurate phase fraction calculation via superior semantic segmentation by achieving an overall 8.6% performance increase over baseline with mean phase fraction error of 5.69%. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Spatio-Temporal Spiking Attention Recurrent Neural Network for EEG-Based Motor Imagery Classification

Spatio-Temporal Spiking Attention Recurrent Neural Network f...

引用

2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lim, Jin-Gyo Kim, Seong-Eun Seoul National University of Science and Technology Dept. Defence AI Convergence Engineering Seoul Korea Republic of Seoul National University of Science and Technology Dept. Applied Artificial Intelligence Dept. Defence AI Convergence Engineering Seoul Korea Republic of

ISBN: (纸本)9798331510756

Motor Imagery (MI), a primary research domain of brain-computer interface (BCI) studies, focuses on the recording and analysis of brain activity related to imagined movements, primarily using electroencephalography (EEG). Recent advance-ments in deep learning have significantly enhanced EEG-based MI classification. New challenges, raised with increasing interest in developing low-power neuromorphic devices, brought the demand for spiking neural networks (SNNs), which promise efficient MI classification with reduced energy requirements. However, the application of SNNs to EEG-based MI classification remains relatively unexplored. In this study, we propose a novel approach that integrates spatio-temporal spike encoding with an attention recurrent neural network (ARNN), referred to as spiking ARNN (SARNN). This architecture is designed to capture both the spatial dependencies across EEG channels and the temporal dynamics within the EEG signals, enabling more robust feature extraction and classification. Our method achieved an accuracy of 78.46% on the benchmark BCI Competition IV 2a dataset, demonstrating substantial improvements in MI classification performance and emphasizing the potential of SNNs in BCI applications. © 2025 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Banknote Fitness Judgment System Using MemSeg Based on K-Means Memory Update

Banknote Fitness Judgment System Using MemSeg Based on K-Mea...

引用

2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Bae, Hui-Jae Jung, Dae-Sik Kang, Byung-Jun Sangmyung University Dept. of Computer Science Seoul Korea Republic of College of Convergence Engineering Sangmyung University Dept. of Faculty of SW Convergence Seoul Korea Republic of LG AI Research Seoul Korea Republic of

ISBN: (纸本)9798331510756

In general, determining the authenticity of damaged banknotes can often be challenging. To address this, the Bank of Korea exchanges banknotes that are not suitable for circulation due to damage or wear. In this study, we propose a banknote authenticity judgment system using MemSeg based on K-means memory update. This system identifies damage to banknotes more objectively and quickly. When memory update is performed using K-means, normal images with various feature patterns are updated uniformly in the memory. The experimental results show that the image-AUROC is improved by an average of 4.27% compared to the existing method. This proves that the proposed method is more objective. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

A Family-History Based Conversational ai System Using LLM 15

A Family-History Based Conversational AI System Using LLM

引用

15th International Conference on Information and Communication Technology convergence, ICTC 2024

作者： Lim, Mika Ku, Jaebin Oh, Gyeong-Hwan Lee, Sinmin Kang, Yuju Kim, Jaejeung Chungnam National University Dept. of Bio-AI Convergence Daejeon Korea Republic of Chungnam National University Dept. of Computer Science and Engineering Daejeon Korea Republic of

ISBN: (纸本)9798350364637

The proportion of single residents is steadily increasing, and the lives of single residents can experience relational disconnection and isolation, as well as limitations in social solidarity and integration. This study aimed to develop an ai conversation system that allows users to feel intimacy with the aim of improving the loneliness of single residents. Therefore, the system persona was set to family, and the system was created using the LLM model. The ai conversation system was developed at the pilot level, and user testing was conducted. Suppose the system is refined in the future and upgraded by reflecting user test opinions. In that case, it is believed that it will be possible to develop a system that allows users to feel closeness and, as a result, improves the loneliness and depression of single residents. © 2024 IEEE.

关键词： ai conversation system Digital healthcare LLM Single family

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence to Combat Audio Fraud: A Flask-Deployed Hybrid Deep Learning System 12

Artificial Intelligence to Combat Audio Fraud: A Flask-Deplo...

引用

12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Solanki, Shashank Ravikumar, R. Devataval, Tushar Gajera, Jeet Kumar Singh, Sushil Gupta, Himanshu Marwadi University Dept. of Computer Engineering Rajkot India Marwadi University Dept. of Computer Engineering-AI Rajkot India

ISBN: (纸本)9798350340204

As the use of synthetic communication is growing widely spread, the ability to accurately identify counterfeit spoken words is now more important than ever. In this study, we have developed an advanced system using deep learning techniques that can differentiate between real and modified audio files. Our research employs Kaggle's DeepFake Voice Recognition Dataset and explores different deep learning architectures such as "Artificial Neural Networks (ANN)", "Convolutional Neural Networks (CNN)", and "Recurrent Neural Networks (RNN)". One crucial novelty of our work lies in merging these models with XGBoost, a machine learning algorithm, to create a hybrid system which greatly enhances the accuracy of detecting false audio tracks. What is surprising, however, is that RNN and XGBoost showed accuracy rate of 99.32% outperforming other settings. Additionally, we created an easy-to-use web application using Flask enabling users to upload and verify authenticity of voice records easily. This system tackles both the critical problem of voice forgery as well as advances in the field of sound authentication technology. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Bluetooth Low Energy-based Adaptive Scheme for IoT Services 14

Bluetooth Low Energy-based Adaptive Scheme for IoT Services

引用

14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Shan, Gaoyang Choi, Geunkyung Roh, Byeong-Hee Ajou University Dept. of Software and Computer Engineering Suwon16499 Korea Republic of Ajou University Dept. of Ai Convergence Nework Dept. of Computer Engineering Suwon16499 Korea Republic of

ISBN: (纸本)9798350335385

To adapt to various IoT services, Bluetooth Low Energy (BLE) specification has defined a variety of communication modes, such as advertising, extended advertising, and periodic advertising events. As services' environments, such as the number of users and QoS requirements, are changed, modes of protocols should be also adjusted to meet those services' needs. This paper proposes an adaptive BLE scheme to reflect dynamically changing services' environments. The effectiveness of the proposed scheme is verified through simulation experiments. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference 30

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

引用

30th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Metaverse Interview Room Creation With Virtual Interviewer Generation Using Diffusion Model

Metaverse Interview Room Creation With Virtual Interviewer G...

引用

2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Si, Jongwook Yang, Seongeun Kim, Daemin Kim, Sungyoung Kumoh National Institute of Technology Dept. Computer Ai Convergence Engineering Gumi Korea Republic of Kumoh National Institute of Technology Dept. Computer Engineering Gumi Korea Republic of

ISBN: (纸本)9798350341072

Virtual interviews are an effective way to respond quickly to the changing trends of our time and adapt flexibly to the hiring processes of various organizations. Through this method, applicants have the opportunity to practice their interview skills and receive feedback, greatly aiding their job preparation. Additionally, experiencing a virtual interview environment that is similar to an actual one enables them to adapt more easily to a variety of new interview situations. This paper delves deeply into the virtual interview environment implemented by combining cutting-edge metaverse technology and generative ai. Specifically, it focuses on creating an environment utilizing realistic Diffusion models to generate interviewers, enabling the provision of scenarios that are similar to actual interviews. © 2023 IEEE.

关键词： Virtual reality

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：