检索结果-内蒙古大学图书馆

Simulation Winter Conference

作者： Murat M. Gunal Yahya Ismail Osais Lee Schruben Gerd Wagner Enver Yücesan Faculty of Engineering and Architecture Fenerbahce University Istanbul Türkiye Computer Engineering Department Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Industrial Engineering and Operations Research University of California Berkeley Berkeley CA USA Department of Informatics Brandenburg University of Technology Cottbus Germany Technology and Operations Management Area INSEAD Singapore

Forty years ago, in 1983, Lee Schruben proposed the Event Graph formalism and modeling language, subsequently defining the paradigm of Event-Based Simulation, in a precise way, which had been pioneered 20 years before by SIMSCRIPT. The purpose of this panel is for a group of Event Graph researchers both from Operations Research and computer Science, including the inventor of Event Graphs and one of his former PhD students who has made essential contributions to their theory, to discuss their views on the history and potential of Event Graph modeling and simulation. In particular, the adoption of Event Graphs as a discrete process modeling language in Discrete Event Simulation and in computer Science, and their potential as a foundation for the entire field of Discrete Event Simulation and for the fields of process modeling and AI in computer Science is debated.

关键词：

来源：评论

学校读者我要写书评

暂无评论

INSTANCE SEGMENTATION AND TEETH CLASSIFICATION IN PANORAMIC X-RAYS

arXiv

引用

arXiv 2024年

作者： Budagam, Devichand Ghosh, Sayan Imanbayev, Azamat Zhanatuly Kaplun, Dmitrii Rychenkov, Artem Sinitca, Aleksandr Kumar, Ayush Shrivastav, Anuj Akhmetov, Iskander Rafailovich Antonov, Sergey Cyganov, Gleb Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Chemical Engineering Indian Institute of Technology Kharagpur Kharagpur India School of IT and Engineering Kazakh-British Technical University Almaty Kazakhstan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Centre for Digital Telecommunication Technologies St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Department of Humanities and Social Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur Kharagpur India Faculty of Digital Transformation ITMO University St. Petersburg Russia

Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3% increase in mAP score for teeth classification compared to existing methods, and a 10-15% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis. © 2024, CC BY.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

The artificial neural networks (Ann) for batik detection based on textural features 7

The artificial neural networks (Ann) for batik detection bas...

引用

7th Mathematics, Science, and computer Science Education International Seminar, MSCEIS 2019

作者： Kasim, Anita Ahmad Bakri, Muhammad Septiarini, Anindita Departemen of Information Technology Tadulako University Palu Indonesia Department of Architecture Tadulako University Palu Indonesia Department of Computer Science Mulawarman University Samarinda Indonesia

ISBN: (纸本)9781631902567

This study aims to utilize artificial neural networks to distinguish batik motifs and non-batik fabric motifs. Several important steps are needed, namely the process of acquiring batik and non-batik images, pre-transforming batik and non-batik images to gray scale forms, texture feature extraction in gray scale images and detection of motifs using networks artificial nerve. Image acquisition is done by collecting batik and not batik images from several different motifs. Processing data sets is divided into 70% as training data and 30% as testing data. Artificial neural network models used in this research use the Backpropagation learning algorithm by comparing the Scaled conjugate gradient algorithm (trainscg) training method and the Levenberg-Marquardt algorithm (trainlm) training method. The results obtained for the accuracy using the Scaled conjugate gradient algorithm (trainscg) training method were higher with an accuracy value of 84.12%, compared to the Levenberg-Marquardt algorithm (trainlm) method by 86.11%. © 2020 EAI.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

A Rapid GBDT-BiLSTM Based Damage Detection Technique in Bridges using Accurate Parameters

A Rapid GBDT-BiLSTM Based Damage Detection Technique in Brid...

引用

Intelligent Technologies (ACOIT), Asian Conference on

作者： Uppara Raghu Babu Deepak T. Patil Jameela Qasim Mukesh Soni Neelam Sharma V. Bhoopathy Srinivasa Ramanujan Institute of Technology Anantapur Andhra Pradesh India Bharati Vidyapeeth college of Architecture University of Mumbai Navi Mumbai Maharashtra India Department of Bridge Engineering School of Civil Engineering Tongji University Shanghai China Department of Research and Development Lovely Professional University Phagwara India Physical Education Department Lovely Professional University Punjab India Department of Computer Science and Engineering Sree Rama Engineering College Tirupathi India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Conventional methods for diagnosing bridge damage have centred on tracking changes in modal-based Damage Sensitive Features (DSFs), which are strongly related to structural stiffness. Environmental and operational variations, inefficient use of machine learning approaches for damage detection, and an overall reliance on modal-based DSFs are some of the limits and hurdles that the system still faces, despite substantial progress towards practical deployment. According to the proposed method, the initial three steps are data preparation, feature extraction, and model training. In order to identify damage during the preprocessing step, the suggested approach utilises anomaly scores. Features in the timefrequency domain allow for parallel processing of signals in the signal-to-feature extraction pipeline. After the features have been extracted, the data is used to train GBDT-BiLSTM models. The proposed approach achieves better results than the wellknown alternatives, GBDT and BiLSTM.

关键词： Bridges Training Time-frequency analysis Pipelines Bidirectional long short term memory Signal processing Parallel processing Feature extraction Data models Monitoring

来源：评论

学校读者我要写书评

暂无评论

Blow-up and continuous dependence of solutions for a class of Love-type damped wave equations with p-Laplacian and memory term

引用

Arabian Journal of Mathematics 2025年 1-29页

作者： Nhan, Nguyen Huu Hanh, Nguyen Son Hong Triet, Nguyen Anh Ngoc, Le Thi Phuong Long, Nguyen Thanh Ho Chi Minh City University of Foreign Languages and Information Technology Ho Chi Minh City Vietnam Faculty of Mathematics and Computer Science University of Science Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam Department of Mathematics University of Architecture Ho Chi Minh City Ho Chi Minh City Vietnam University of Khanh Hoa Nha Trang City Vietnam

In this paper, we examine a general model of Love-type damped wave equations that includes the p-Laplacian and a memory term. Firstly, by utilizing linearization techniques, the Faedo-Galerkin method, and arguments of compactness, we establish the existence and uniqueness of solutions for the problem. Next, the techniques and estimations presented in [Appl. Math. 68 (2) (2023) 209-254] are used to obtain the continuous dependence of solutions on relaxation functions and nonlinear components of the problem. Furthermore, under several appropriate assumptions, a finite-time blow-up of solutions with negative initial energy is also proved. Finally, we discuss some open problems that arise from our findings.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection

arXiv

引用

arXiv 2022年

作者： Madan, Neelu Ristea, Nicolae-Cǎtǎlin Ionescu, Radu Tudor Nasrollahi, Kamal Khan, Fahad Shahbaz Moeslund, Thomas B. Shah, Mubarak Department of Computer Science University of Central Florida OrlandoFL32816 United States The Department of Architecture Design and Media Technology Aalborg University Denmark The Department of Telecommunications University Politehnica of Bucharest Romania The Department of Computer Science University of Bucharest Romania SecurifAI The Romanian Young Academy The Department of Computer Science University of Bucharest Romania Milestone Systems Denmark United Arab Emirates Linköping University Sweden

Anomaly detection has recently gained increasing attention in the field of computer vision, likely due to its broad set of applications ranging from product fault detection on industrial production lines and impending event detection in video surveillance to finding lesions in medical scans. Regardless of the domain, anomaly detection is typically framed as a one-class classification task, where the learning is conducted on normal examples only. An entire family of successful anomaly detection methods is based on learning to reconstruct masked normal inputs (e.g. patches, future frames, etc.) and exerting the magnitude of the reconstruction error as an indicator for the abnormality level. Unlike other reconstruction-based methods, we present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. The proposed self-supervised block is extremely flexible, enabling information masking at any layer of a neural network and being compatible with a wide range of neural architectures. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss. Furthermore, we show that our block is applicable to a wider variety of tasks, adding anomaly detection in medical images and thermal videos to the previously considered tasks based on RGB images and surveillance videos. We exhibit the generality and flexibility of SSMCTB by integrating it into multiple state-of-the-art neural models for anomaly detection, bringing forth empirical results that confirm considerable performance improvements on five benchmarks: MVTec AD, BRATS, Avenue, ShanghaiTech, and Thermal Rare Event. We release our code and data as open source at: https://***/ristea/ssmctb. Copyright © 2022, The Authors. All rights reserved

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Intelligent Permaculture: A Sustainable and Profitable Practice for Tropical and Maritime Climate Urban and Peri-urban Agricultural Ecosystems 13

Intelligent Permaculture: A Sustainable and Profitable Pract...

引用

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： Concepcion, Ronnie Esguerra, Bennyvic Joyce Mendigoria, Christan Hail Aquino, Heinrick Alajas, Oliver John Francisco, Kate Montanez, John Joshua Bandala, Argel Dadios, Elmer De La Salle University Department of Manufacturing Engineering and Management Manila Philippines College of Engineering and Architecture Bicol State College of Applied Sciences and Technology Naga Philippines De La Salle University Department of Electronics and Computer Engineering Manila Philippines De La Salle University Department of Manufacturing Engineering and Management Manila Philippines

ISBN: (纸本)9781665401678

Permaculture is a land management and regenerative agriculture that is the integration of technological advancement and the natural agricultural ecosystems. It is usually manifested in green architecture and balcony gardens in an urban area. However, peri-urban geographical regions are those situated in between urban and rural areas that are experiencing the impacts of modernizing community and conventional farming. This study discusses the technological drivers making intelligent permaculture ecosystems and applications, the current issues and challenges in this emerging agricultural and horticultural scheme, and a set of future directives in intelligent permaculture ecosystems in a tropical and maritime climatic setting. Based on the systematic analysis, this study had established a guideline that compliance to UN sustainable development goals, controlled environment engineering, and planting of fruit-bearing trees and vegetables in sidewalks can solve the issues of public knowledge, ecological stability and environmental sustainability, profitability issue, and technological constraints concerning intelligent permaculture systems. © 2021 IEEE.

关键词： Ecosystems

来源：评论

学校读者我要写书评

暂无评论

Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

引用

2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks

引用

Journal of computer Science & technology 2020年第1期35卷 209-220页

作者： Sa Wang Yan-Hai Zhu Shan-Pei Chen Tian-Ze Wu Wen-Jie Li Xu-Sheng Zhan Hai-Yang Ding Wei-Song Shi Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518055China Alibaba Inc. Hangzhou 311121China Department of Computer Science Wayne State UniversityMichiganMI 48202U.S.A

Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be *** resource utilization increases the risk of resource contention between co-located workload,which makes latency-critical(LC)applications suffer unpredictable,and even unacceptable *** of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource *** this paper,we propose MAGI,a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference,and adjusts resource shares of corresponding applications to ensure the QoS of LC *** is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural *** experimental results show that MAGI could reduce up to 87.3%performance degradation of LC application when co-located with other antagonist applications.

关键词： resource management neural network resource efficiency tail latency

来源：评论

学校读者我要写书评

暂无评论

Autonomous Resource Management in Construction Companies Using Deep Reinforcement Learning Based on IoT

arXiv

引用

arXiv 2022年

作者： Soleymani, Maryam Bonyani, Mahdi Attarzadeh, Meghdad Department of Construction and Project Management Art University of Tehran Tehran Iran Department of Computer Engineering University of Tabriz Iran Department of Construction Management School of Architecture & Planning Morgan State University United States Department of Engineering Technology Old Dominion University United States

Resource allocation is one of the most critical issues in planning construction projects, due to its direct impact on cost, time, and quality. There are usually specific allocation methods for autonomous resource management according to the project’s objectives. However, integrated planning and optimization of utilizing resources in an entire construction organization are scarce. The purpose of this study is to present an automatic resource allocation structure for construction companies based on Deep Reinforcement Learning (DRL), which can be used in various situations. In this structure, Data Harvesting (DH) gathers resource information from the distributed Internet of Things (IoT) sensor devices all over the company’s projects to be employed in the autonomous resource management approach. Then, Coverage Resources Allocation (CRA) is compared to the information obtained from DH in which the Autonomous Resource Management (ARM) determines the project of interest. Likewise, Double Deep Q-Networks (DDQNs) with similar models are trained on two distinct assignment situations based on structured resource information of the company to balance objectives with resource constraints. The suggested technique in this paper can efficiently adjust to large resource management systems by combining portfolio information with adopted individual project information. Also, the effects of important information processing parameters on resource allocation performance are analyzed in detail. Moreover, the results of the generalizability of management approaches are presented, indicating no need for additional training when the variables of situations change. © 2022, CC BY-NC-ND.

关键词： Internet of things

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：