检索结果-内蒙古大学图书馆

3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer engineering and Applications, CVIDL and ICCEA 2022

作者： Lin, Haicheng Gong, Jie Chen, Xiang Xie, Xiaohua Tang, Hengliang Jiang, Junfeng Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Electronics and Information Technology Guangzhou China Guangdong Xinguang Intelligent System Co. Ltd. Zhongshan China

ISBN: (纸本)9781665459112

The quality detection of furniture boards is one of the most important parts of the furniture board quality control process. The boards may contain round holes, grooves, and defects for various reasons. In industry, it is necessary to detect them in real time. This paper proposes a multi-objective detection method based on the Yolov4-tiny and Multi-Gate Mixture-of-Experts(MMOE) structure that can simultaneously detect round holes, grooves and defects in high resolution board images in real time. Considering the high resolution of the board image and the small size of the dataset, data pre-processing and data augmentation are performed to reduce the size of the image and increase the size of the dataset. In consideration of the difference in the characteristics of the hole, groove and defect. the task of detecting defects and the task of detecting holes and grooves are decoupled, and different task headers and gate networks are designed. Finally, it is tested on a real production line and the combined mAP reaches 75.49% and the detection takes only 20ms. © 2022 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

A Design and Implementation of Water Heater Model Plant with Temperature Estimation Model for IOT Studies 7

A Design and Implementation of Water Heater Model Plant with...

引用

7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Pradhana, Anak Agung Surya Funabiki, Nobuo Kotama, I Nyoman Darma Desnanjaya, I Gusti Made Ngurah Radhitya, Made Leo Mahajaya, Nyoman Sarasuartha Okayama University Graduate School of Environmental Life Natural Science and Technology Okayama Japan Okayama University Department of Electrical and Communication Engineering Okayama Japan Institute of Business and Technology Indonesia Department of Computer System Engineering Denpasar Indonesia Institute of Business and Technology Indonesia Department of Informatic Technology Denpasar Indonesia Institut Teknologi Dan Bisnis Stikom Departement of Magister Information Systems Denpasar Indonesia

ISBN: (纸本)9798331505103

This study presents the development of an Internet of Things (IoT) system for a water heater model, focusing on enhancing reliability during sensor malfunctions that could disrupt operations. Using the SEMAR IoT platform, the system efficiently collects and analyzes data, while the integration of the Holt-Winter algorithm ensures accurate temperature estimates, even with unreliable or missing sensor data. The algorithm successfully auto-calibrates sensors, predicts temperature at specific time intervals based on real sensor behavior, and fills in missing data during sensor failures. Tests show strong correlations between estimated and actual temperatures, allowing uninterrupted operation. This solution offers a flexible, cost-effective alternative to traditional redundancy, ensuring system reliability during sensor failures. Future research will explore alternative predictive models and conduct real-world testing to validate the system's resilience under complex conditions. © 2024 IEEE.

关键词： Temperature measurement

来源：评论

学校读者我要写书评

暂无评论

An Effective and Head-Friendly Long-Tailed Object Detection Network with Improving Data Enhancement and Classifier

An Effective and Head-Friendly Long-Tailed Object Detection ...

引用

International Conference on computer and Communications (ICCC)

作者： Huiyun Gong Jingxiu Yao Yanhong Liu School of Computer Science and Engineering Beihang University Beijing China School of Reliability and System Engineering Beihang University Beijing China Beijing Institute of Metrology Beihang University Beijing China

Supervised learning methods achieve good results in large amounts of data and balanced data scenarios. However, the real world often has unbalanced data, such as long-tail distribution data sets, where the number of head categories is far exceeds the number of tail categories. Because the amount of tail data is small, using traditional supervised training methods will cause the model to be biased towards the head category. Therefore, when optimizing model performance, we often consider preventing bias from causing difficulty in improving model performance. A common approach is to balance the performance of the head and tail categories to achieve the purpose of improving the performance of the tail category and the overall performance of the model. Such approaches make a complex trade-off between the head and tail categories. Based on the above observations, we propose a head category-friendly long-tail object detection network (EHFL), including a data enhancement module (DEM) and a classifier enhancement module (CEM), and propose a decoupled performance adjustment strategy (DAS) for training, which can decouple the optimization of head category and tail category performance. In DEM, we propose using label information to generate training data to improve some categories’ performance. In CEM, we propose a classifer enhancement loss function to improve overall performance. Experimental results show that it reaches 26.48% on the LVIS-v0.5 data set, improving overall performance without reducing the performance of the head category, which is better than state-of-the-art methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mixed Structural Choice Operator: Enhancing Technology Mapping with Heterogeneous Representations

arXiv

引用

arXiv 2025年

作者： Hu, Zhang Pan, Hongyang Xia, Yinshui Wang, Lunyao Chu, Zhufei Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University Shanghai200433 China

—The independence of logic optimization and technology mapping poses a significant challenge in achieving high-quality synthesis results. Recent studies have improved optimization outcomes through collaborative optimization of multiple logic representations and have improved structural bias through structural choices. However, these methods still rely on technology-independent optimization and fail to truly resolve structural bias issues. This paper proposes a scalable and efficient framework based on Mixed Structural Choices (MCH). This is a novel heterogeneous mapping method that combines multiple logic representations with technology-aware optimization. MCH flexibly integrates different logic representations and stores candidates for various optimization strategies. By comprehensively evaluating the technology costs of these candidates, it enhances technology mapping and addresses structural bias issues in logic synthesis. Notably, the MCH-based lookup table (LUT) mapping algorithm set new records in the EPFL Best Results Challenge by combining the structural strengths of both And-Inverter Graph (AIG) and XOR-Majority Graph (XMG) logic representations. Additionally, MCH-based ASIC technology mapping achieves a 3.73% area and 8.94% delay reduction (balanced), 20.35% delay reduction (delay-oriented), and 21.02% area reduction (area-oriented), outperforming traditional structural choice methods. Furthermore, MCH-based logic optimization utilizes diverse structures to surpass local optima and achieve better results. Copyright © 2025, The Authors. All rights reserved.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Air-Space-Ground Synergistic Observations for Rapid Post-Seismic Disaster Assessment of 2025 Ms6.8 Xigaze Earthquake,Xizang

引用

Journal of Earth science 2025年

作者： Jie Dou Ke Xing Lizhe Wang Haixiang Guo Dun Wang Yigui Peng Xinjian Xiang Dunzhu Ciren Songcheng Zhang Lele Zhang Bo Peng Badong National Observation and Research Station of Geohazards China University of Geosciences National Engineering Research Center of Geographic Information System China University of Geosciences Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences School of Computer Science China University of Geosciences School of Economics and Management China University of Geosciences(Wuhan) School of Earth Sciences China University of Geosciences Xizang Autonomous Region Water Conservancy and Electric Power Planning Survey and Design Institute

On January 7,2025,an Ms6.8 earthquake struck Dingri County,Xigaze City,Xizang Autonomous *** epicenter,located near the Shenzha-Dingjie fault zone at the boundary between the Qinghai-Xizang Plateau and the Indian Plate,marked the largest earthquake in the region in recent *** Shenzha-Dingjie fault zone,situated at the boundary between the Qinghai-Xizang Plateau and the Indian Plate,is a key tectonic feature in the India-Eurasia collision process,characterized by active structural deformation manifested as both thrust and strike-slip *** study analyzed the disaster characteristics induced by the earthquake using differential synthetic aperture radar interferometry (D-InSAR) to process Sentinel-1 satellite data and derive pre-and post-earthquake surface deformation ***,high-resolution optical remote sensing data,UAV imagery,and airborne Li DAR data were employed to analyze the spatial distribution of the surface rupture zone,with field investigations validating the *** results include:(1) Potential landslide hazard points identified through optical image interpretation did not show secondary landslide activity upon field verification;(2) D-InSAR revealed the co-seismic surface deformation pattern,providing detailed deformation information for the Dingri region;(3) Integration of LiDAR and optical imagery further refined and validated surface rupture characteristics identified by optical-InSAR,indicating a predominantly north-south rupture ***,surface rupture features extending in a near east-west direction were observed on the southeast side of the epicenter,accompanied by some infrastructure damage;(4) Surface rupture was most severe in high-intensity seismic areas near the epicenter,with the maximum surface displacement approximately 28 km from the *** earthquake-induced surface deformation zone spanned approximately 6 km by 46 km,with deformation concentrated primarily on the western side

关键词：

来源：评论

学校读者我要写书评

暂无评论

Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

Circular Decomposition and Cross-Modal Recombination for Mul...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Agus Pratondo Astri Novianty Budi Laksono Putro Siska Komala Sari Department of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Department of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia Department of Computer Science Education Indonesia University of Education Bandung Indonesia Diploma Program of Information System School of Applied System Telkom University Bandung Indonesia

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Empowering Human-Machine Communication in Code Generation with NLP

Empowering Human-Machine Communication in Code Generation wi...

引用

Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： Upendra Singh Aswal Pappala Mohan Rao B. R. Supreeth G. Manikandan R. Srinivasan Mohit Tiwari Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Computer Science and System Engineering Andhra University College Of Engineering AU College Of Engineering Visakhapatnam AP Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Ullal Bangalore Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam India Department of Computer Science and Engineering St. Martins Engineering College Secunderabad Telangana Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Paschim Vihar Delhi

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

The symbiotic interaction between people and computers in the realm of software development is experiencing a fundamental transition, spurred by improvements in Natural Language Processing (NLP). This study investigates how natural language processing (NLP) is changing the software development process by facilitating better human-machine collaboration throughout the coding phase. Data collection, NLP model creation, and assessment are all part of the rigorous process used in our work, which yields important results. The possibility for improving code quality and efficiency while also democratizing code production is discussed in the context of a fictional situation. Ethical concerns are also highlighted, along with the need to minimize bias in NLP-driven code production. To the benefit of developers, organizations, and society at large, this study not only adds empirical insights but also imagines a future in which software development is more inclusive, efficient, and morally responsible. From improved natural language processing models to real-time feedback loops and multimodal techniques, the future of this transforming discipline promises to further influence the landscape of software development.

关键词： Symbiosis Codes Human-machine systems Computational modeling Production Organizations Market research

来源：评论

学校读者我要写书评

暂无评论

Provably Secure Authenticated Key-Management Mechanism for e-Healthcare Environment

引用

IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

MMM-RS: a multi-modal, multi-GSD, multi-scene remote sensing dataset and benchmark for text-to-image generation 24

MMM-RS: a multi-modal, multi-GSD, multi-scene remote sensing...

引用

Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Jialin Luo Yuanzhi Wang Ziqi Gu Yide Qiu Shuaizhen Yao Fuyun Wang Chunyan Xu Wenhua Zhang Dan Wang Zhen Cui PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China. Beijing Institute of Spacecraft System Engineering Beijing China.

ISBN: (纸本)9798331314385

Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes. In this paper, we propose a Multi-modal, Multi-GSD, Multi-scene Remote Sensing (MMM-RS) dataset and benchmark for text-to-image generation in diverse remote sensing scenarios. Specifically, we first collect nine publicly available RS datasets and conduct standardization for all samples. To bridge RS images to textual semantic information, we utilize a large-scale pretrained vision-language model to automatically output text prompts and perform hand-crafted rectification, resulting in information-rich text-image pairs (including multi-modal images). In particular, we design some methods to obtain the images with different GSD and various environments (e.g., low-light, foggy) in a single sample. With extensive manual screening and refining annotations, we ultimately obtain a MMM-RS dataset that comprises approximately 2.1 million text-image pairs. Extensive experimental results verify that our proposed MMM-RS dataset allows off-the-shelf diffusion models to generate diverse RS images across various modalities, scenes, weather conditions, and GSD. The dataset is available at https://***/ljl5261/MMM-RS.

关键词：

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：