检索结果-内蒙古大学图书馆

15th International Conference on Knowledge and Systems Engineering, KSE 2023

作者： Nguyen-Tat, Bao-Thien Khanh Nguyen, Duy University of Information Technology Faculty of Information Science and Engineering Ho Chi Minh Viet Nam Vietnam National University Ho Chi Minh Viet Nam Institute for Computational Science and Artificial Intelligence Van Lang University Laboratory for Computational Physics Ho Chi Minh Viet Nam School of Technology Van Lang University Faculty of Mechanical - Electrical and Computer Engineering Ho Chi Minh Viet Nam

ISBN: (纸本)9798350329742

This article discusses the classification of abnormal log messages from Juniper routers. Abnormal log messages reflect the unusual activities of these routers. Classifying abnormal log messages into different categories helps monitoring personnel quickly identify errors and take timely measures to protect the devices. We extract features from abnormal logs and use a Multilayer Perceptron (MLP) neural network model for classification. The MLP neural network model requires knowledge of the log data from Juniper routers so that the trained network can analyze and validate that knowledge. We collected log data from Juniper routers operating on SCTV's core network system, identified abnormal log messages, and classified them based on our experience and knowledge. © 2023 IEEE.

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

Temperature Characteristics of Oil-Immersed Transformer Based on Fiber Bragg Grating Temperature Sensing Technology

SSRN

引用

SSRN 2024年

作者： Luo, Chuan Li, Zizhou Yang, Bo Zhao, Zhengang Li, Chuan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

Oil-immersed transformer hot-spot temperature and location directly affects the load capacity and service life of the transformer. The traditional hot-spot temperature measurement is indirect and large deviation. Through the fiber Bragg grating technology, the temperature of the internal components of the transformer under variable operating conditions can be measured directly, reflecting the temperature characteristics of the transformer hot-spot location. Based on this, two full dielectric stress-insensitive FBG temperature sensors with different package forms are designed, with sensitivities of 10.22∼10.65 pm/◦C, linearity of 0.99%∼1.65%, and repeatability of 1.07%∼1.45%. Multiple FBG temperature sensors are installed on the iron-core, windings, and oil channel to build a monitoring system. In the temperature rise test, the temperature inside the transformer variation with space and time under variable operating conditions are obtained. The hot-spot winding temperature rise is 62.9◦C, and the top-oil temperature rise is 39.6◦C. The hot-spot location changes with the operating conditions. © 2024, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

来源：评论

学校读者我要写书评

暂无评论

Research and Application of Machine Learning in Automatic Program Generation

引用

Chinese Journal of Electronics 2020年第6期29卷 1001-1015页

作者： ZHANG Xiaojiang JIANG Ying Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming University of Science and Technology

With the development of artificial intelligence,machine learning has been applied in more and more *** order to improve the quality and efficiency of software,automatic program generation is becoming a research *** recent years,machine learning has also been gradually applied in automatic program *** trees,language models,and cyclic neural networks have been applied in code generation,code completion and code knowledge *** efficiency of software development has been improved to a certain extent using machine *** at the automatic program generation,this paper analyzes and summarizes the models of machine learning,the modifications involved in the models and the application *** research direction is discussed from the aspects of programmer behavior and automatic program generation of machine learning.

关键词： automatic programming data mining decision trees learning (artificial intelligence) neural nets software engineering programmer behavior cyclic neural networks language models decision trees code knowledge mining code completion code generation automatic program generation machine learning Machine learning Code generation Code completion Code knowledge mining Application effect

来源：评论

学校读者我要写书评

暂无评论

Application of Transfer Learning in Smart Agriculture to Combat Black Rot Bacteria

引用

Procedia computer science 2024年 236卷 356-362页

作者： Youness TACE Mohamed TABAA Sanaa ELFILALI Cherkaoui LEGHRIS Laboratory of Information Technologies and Modelling Faculty of Sciences Ben M'sik HIIU Casablanca Morocco Pluridisciplinary Laboratory of Research & Innovation (LPRI) EMSI Casablanca Morocco Computer Science Department RTM Team FST Mohammedia HIIU Casablanca Morocco

With diseases and weeds alone accounting for approximately 30% of productivity losses in agriculture worldwide, plant diseases are a significant factor. Farmers frequently consult experts to diagnose their crops in an effort to reduce these losses. On the other hand, conventional visual assessment techniques focused on elements like leaf color, shape, and texture can be inefficient, time-consuming, and expensive. This study suggests using symptom traits like yellow leaves, black lesions, wilting, and plant mortality in crops like apple and grape to anticipate plant diseases, notably black rot. AlexNet, VGGNet, ResNet, and Inception are the four deep learning models examined in the study; VGGNet achieved the greatest accuracy of 98.19%.

关键词： Smart Agriculture Convolutional Neural Network Black Rot Bacteria Plant Image Detection

来源：评论

学校读者我要写书评

暂无评论

Anomaly Handwritten Text Detection for Automatic Descriptive Answer Evaluation 22

Anomaly Handwritten Text Detection for Automatic Descriptive...

引用

Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Nilanjana Chatterjee Palaiahnaakote Shivakumara Umapada Pal Tong Lu Yue Lu Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Faculty of Computer Science and Information Technology University of Malaya Malaysia National Key Lab for Novel Software Technology Nanjing University China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University China

ISBN: (纸本)9781450397056

Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and answers to the question. This paper presents a novel method for detecting anomaly handwritten text in the responses written by the students to the questions. The method is proposed based on the fact that when the students are confident in answering questions, the students usually write answers legibly and neatly while they are not confident, they write sloppy writing which may not be easy for the reader to understand. To detect such anomaly handwritten text, we explore a new combination of Fourier transform and deep learning model for detecting edges. This result preserves the structure of handwritten text. For extracting features for classification of anomaly text and normal text, the proposed method studies the behavior of writing style, especially the variation at ascenders and descenders. Therefore, the proposed work draws principal axis which is invariant to rotation, scaling and some extent to distortion for the edge images. With respect to principal axis, the proposed method draws medial axis using uppermost and lowermost points. The distance between the medial axis and principal axis points are considered as feature vector. Further, the feature vector is passed to Artificial Neural Network for classification of anomaly text. The proposed method is evaluated by testing on our own dataset, standard dataset of gender identification (IAM) and handwritten forgery detection dataset (ACPR 2019). The results on different datasets show that the proposed work outperforms the existing methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NextSeg: Automatic COVID-19 Lung Infection Segmentation from CT Images Based on Next-ViT

NextSeg: Automatic COVID-19 Lung Infection Segmentation from...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Aimei Dong Ruixin Wang Xuening Zhang Jian Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The spread of COVID-19 has brought great disaster to the world, and automatic segmentation of the infected region can help doctors make a quick diagnosis and reduce workload. However, automatic segmentation of infected regions in computed tomography (CT) images still faces many challenges. The most central problem at the moment is that when capturing low-frequency signals and high-frequency signals, the use of Transformer blocks for capturing low-frequency signals may deteriorate the high-frequency signals (local texture information) to some extent. For this reason, in this paper, we propose a new network for automatic segmentation of COVID-19 lung infections from CT images, called NextSeg. NextSeg follows an encoder-decoder architecture, where the encoder part uses NCB and NTB convolutional attention modules to extract better features while reducing the deterioration of texture profile information. The decoder part uses an attention fusion module to learn the weight relationship between features and retain more important feature information. In addition to the difficulty of extracting the boundary details of the COVID-19 infected region as well as the loss of details the deeper the network is, we designed a new hybrid strategy. Traditional and state-of-the-art approaches to the COVID-19 segmentation domain are compared. Extensive experiments show that our proposed framework outperforms existing competitors in both qualitative and quantitative results.

关键词： COVID-19 Image segmentation Convolution Computed tomography Neural networks Lung Medical services

来源：评论

学校读者我要写书评

暂无评论

A Reinforcement Learning-Based Image Mapping-Topology Optimization for Antenna Design

引用

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Dou, Jiangling Gong, Hao Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming Yunnan650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College China

A Reinforcement Learning (RL)-based Image Mapping Topology (IMT) optimization approach is proposed. To enable RL to optimize antenna topology and increase the efficiency of the optimization process. Two crucial components, Stratified Sampling Balance (SSB) and IMT are introduced. SSB is utilized to balance different types of data samples. IMT is utilized to facilitate the interaction between RL and simulation software, it also pretrains the actor network within the RL algorithm. This methodology allows the RL agent to acquire knowledge from the interaction of the antenna topology with the environment. Subsequently, the agent outputs antenna topology with the maximum reward signal. Compared with other machine learning-based optimization methods, the proposed method significantly alleviates the issue of learning inefficiency and reduces the exploration time for feasible solutions. To validate the effectiveness of the optimization method, a microstrip antenna is optimized. The measurement results show that the impedance bandwidth is doubled while maintaining the realized gain with fewer simulation samples and less optimization time. © 2025 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

An optimized facial recognition model for identifying criminal activities using deep learning strategy

引用

International Journal of information Technology (Singapore) 2023年第7期15卷 3907-3921页

作者： Gokulakrishnan, S. Chakrabarti, Prasun Hung, Bui Thanh Shankar, S. Siva Artificial Intelligence Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of Computer Science and Engineering KG Reddy College of Engineering and Technology (Autonomous) Telangana Hyderabad India

The most important research field in the current period is facial recognition and detection. Furthermore, facial expression recognition applications are essential in various research areas such as criminal investigation, security, data base management systems (DBMS), innovative card application, and video surveillance systems. A criminal investigation is an important area of study. Thus, in today’s world, crime activities are fast expanding in response to psychological trauma. As a result, applying deep learning (DL) algorithms for face expression identification and crime activity monitoring is a growing area of investigation. In terms of pre-processing, feature extraction, and recognizing diverse facial emotions, DL produces better results. As a result, the current study seeks to offer a revolutionary intelligent Strawberry-based convolution neural network (SbCNN). When applied to the Kaggle face expression database, the created SbCNN technique performed best. Face verification and criminal face detection can be accomplished using this approach and the optimization fitness function. Even though the established design improves the robustness of feature extraction and classification, it is a time-consuming process. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Data base management system Deep learning Facial recognition and detection Kaggle Optimization Psychological harm Video surveillance systems

来源：评论

学校读者我要写书评

暂无评论

EmoSpeaker: One-shot Fine-grained Emotion-Controlled Talking Face Generation

arXiv

引用

arXiv 2024年

作者： Feng, Guanwen Cheng, Haoran Li, Yunan Ma, Zhiyuan Li, Chaoneng Qian, Zhihao Miao, Qiguang Pun, Chi-Man School of Computer Science and Technology Xidian University Xi’an710071 China Xi’an Key Laboratory of Big Data and Intelligent Vision Xidian University Xi’an710071 China Key Laboratory of Collaborative lntelligence Systems Ministry of Education Xidian University Xi’an710071 China Department of Computer and Information Science University of Macau 999078 China

—Implementing fine-grained emotion control is crucial for emotion generation tasks because it enhances the expressive capability of the generative model, allowing it to accurately and comprehensively capture and express various nuanced emotional states, thereby improving the emotional quality and personalization of generated content. Generating fine-grained facial animations that accurately portray emotional expressions using only a portrait and an audio recording presents a challenge. In order to address this challenge, we propose a visual attribute-guided audio decoupler. This enables the obtention of content vectors solely related to the audio content, enhancing the stability of subsequent lip movement coefficient predictions. To achieve more precise emotional expression, we introduce a fine-grained emotion coefficient prediction module. Additionally, we propose an emotion intensity control method using a fine-grained emotion matrix. Through these, effective control over emotional expression in the generated videos and finer classification of emotion intensity are accomplished. Subsequently, a series of 3DMM coefficient generation networks are designed to predict 3D coefficients, followed by the utilization of a rendering network to generate the final video. Our experimental results demonstrate that our proposed method, EmoSpeaker, outperforms existing emotional talking face generation methods in terms of expression variation and lip synchronization. Project page: https://***/EmoSpeaker/ Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Visual Explanations of ResNet 101 for Blister Package Classification

Visual Explanations of ResNet 101 for Blister Package Classi...

引用

2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Hnoohom, Narit Maitrichit, Nagorn Wongpatikaseree, Konlakorn Yuenyong, Sumeth Mekruksavanich, Sakorn Jitpattanakul, Anuchit Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakorn Pathom Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

This paper presents an efficient approach to generate visual explanations from a ResNet 101 model for identification of medication. The blister package dataset is used to train a deep learning model built on the PyTorch framework's ResNet 101 pre-trained model. Visual inspections and a quantitative localization benchmark demonstrate that the model approach correctly identifies the critical components of blister packs for medicine identification. A Gradient-weighted Class Activation Mapping (Grad-CAM) method is used to extract the feature map, and then the attention mechanism is utilized to extract the high-level attention maps, that emphasizes the part of the image that is significant to the target class, which can be considered as a visual representation. The experimental results indicated that the Grad-CAM achieved the better visualization and interpretation of ResNet 101 in blister package classification. © 2022 IEEE.

关键词： Deep learning

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：