检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Wang, Zhe Wu, Zhiying Li, Xingqiu Shao, Haidong Han, Te Xie, Min Department of Advanced Design and Systems Engineering City University of Hong Kong Hong Kong Centre for Intelligent Multidimensional Data Analysis Science Park Hong Kong School of Civil Aviation Northwestern Polytechnical University China College of Mechanical and Vehicle Engineering Hunan University China School of Management and Economics Beijing Institute of Technology China City University of Hong Kong Chengdu Research Institute China

intelligent heath monitoring and fault diagnosis have attracted intensive effort in machine predictive maintenance. However, the structural information from multi-sensor signal is not fully investigated and fused to boost the diagnosis performance. In this work, a novel temporal-spatial graph neural network with attention-aware module (A-TSGNN) is proposed to accomplish the multi-source information fusion. Firstly, the graph structure naturally organizes the diverse sensors which are deployed in the complex system. The graph convolution model realizes the feature representation in spatial dimension. Then, the time dependence learning of single-source signal is applied in the temporal dimension, and a temporal-spatial learning framework is built. Finally, an attention module is designed in the A-TSGNN, which can learn the flexible weights to model the importance of individual sensors and their correlations. The proposed A-TSGNN not only provides a potential model to comprehensively utilize the multi-sensor data in fault diagnosis, but also fully considers their spatial-temporal characteristics. The experimental analysis on wind turbine dataset and gearbox dataset verifies the effectiveness and superiority of the proposed A-TSGNN model. The comparisons with single-sensor scheme and other graph neural networks are conducted. The results demonstrate the stabilized performance of proposed method, showing a promising prospect. © 2023, The Authors. All rights reserved.

关键词： Failure analysis

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey on Tiny Machine Learning for Human Behavior Analysis

引用

IEEE Internet of Things Journal 2025年

作者： Lamaakal, Ismail Essahraui, Siham Maleh, Yassine Makkaoui, Khalid El Ouahbi, Ibrahim Bouami, Mouncef Filali El-Latif, Ahmed A. Abd Almousa, May Peng, Jialiang Sultan Moulay Slimane University Laboratory LaSTI ENSAK Khouribga Morocco Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Information Technology Department Riyadh Saudi Arabia Heilongjiang University School of Data Science and Technology Harbin150080 China Nanyang Technological University School of Computer Science and Engineering Nanyang Avenue Singapore639798 Singapore

The integration of Tiny Machine Learning (TinyML) with Human Behavior Analysis (HBA) represents a significant advancement in the field of Artificial Intelligence (AI), enabling real-time, efficient, and privacy-preserving analysis on resource-constrained devices. This paper provides the first comprehensive survey exploring this integration, presenting a detailed overview of TinyML, including its definitions, key concepts and advantages. The survey proposes a systematic taxonomy of TinyML applications in HBA, categorizing state-of-the-art implementations based on their use cases and specific methodologies. Furthermore, the challenges and limitations of integrating TinyML in HBA are thoroughly discussed, including technical constraints, data quality issues, and ethical considerations. Finally, future research directions and open issues are outlined, emphasizing the potential advancements and emerging trends in this field. This survey serves as a foundational resource, guiding researchers and practitioners in harnessing the capabilities of TinyML to advance HBA. © 2014 IEEE.

关键词： Embedded systems

来源：评论

学校读者我要写书评

暂无评论

Petri net modeling for Ising model formulation in quantum annealing

arXiv

引用

arXiv 2021年

作者： Nakamura, Morikazu Kaneshima, Kohei Yoshida, Takeo Computer Science and Intelligent Systems Faculty of Engineering University of the Ryukyus Okinawa903-0213 Japan Information Engineering Course Graduate School of Engineering and Science University of the Ryukyus Okinawa903-0213 Japan

Quantum annealing is an emerging new platform for combinatorial optimization, requiring an Ising model formulation for optimization problems. The formulation can be an essential obstacle to the permeation of this innovation into broad areas of everyday life. Our research is aimed at the proposal of a Petri net modeling approach for an Ising model formulation. Although the proposed method requires users to model their optimization problems with Petri nets, this process can be carried out in a relatively straightforward manner if we know the target problem and the simple Petri net modeling rules. With our method, the constraints and objective functions in the target optimization problems are represented as fundamental characteristics of Petri net models, extracted systematically from Petri net models, and then converted into binary quadratic nets, equivalent to Ising models. The proposed method can drastically reduce the difficulty of the Ising model formulation. Copyright © 2021, The Authors. All rights reserved.

关键词： Ising model

来源：评论

学校读者我要写书评

暂无评论

An Extended Clinical EEG dataset with 15,300 Automatically Labelled Recordings for Pathology Decoding

SSRN

引用

SSRN 2023年

作者： Kiessner, Ann-Kathrin Schirrmeister, Robin Tibor Gemein, Lukas A.W. Boedecker, Joschka Ball, Tonio Neuromedical AI Lab Department of Neurosurgery Medical Center – University of Freiburg Faculty of Medicine University of Freiburg Engelbergerstr. 21 Freiburg79106 Germany University of Freiburg Georges-Köhler-Allee 201 Freiburg79110 Germany Autonomous Intelligent Systems Computer Science Department University of Freiburg Faculty of Engineering Georges-Köhler-Allee 80 Freiburg79110 Germany Neurorobotics Lab Computer Science Department University of Freiburg Faculty of Engineering Georges-Köhler-Allee 80 Freiburg79110 Germany

Automated clinical EEG analysis using machine learning (ML) methods is a growing EEG research area. Previous studies on binary EEG pathology decoding have mainly used the Temple University Hospital (TUH) Abnormal EEG Corpus (TUAB) which contains approximately 3,000 manually labelled EEG recordings. To evaluate and eventually even improve the generalisation performance of machine learning methods for EEG pathology, decoding larger, publicly available datasets is required. A number of studies addressed the automatic labelling of large open-source datasets as an approach to create new datasets for EEG pathology decoding, but little is known about the extent to which training on larger, automatically labelled dataset affects decoding performances of established deep neural networks. In this study, we automatically created additional pathology labels for the Temple University Hospital (TUH) EEG Corpus (TUEG) based on the medical reports. We generated a dataset of 15,300 newly labelled recordings, which we call the TUH Abnormal Expansion Corpus (TUABEX), and which is five times larger than the TUAB. Since the TUABEX contains more pathological (75%) than non-pathological (25%) recordings, we then selected a balanced subset of 8,879 recordings, the TUH Abnormal Expansion Balanced Corpus (TUABEXB). To investigate how training on a larger, automatically labelled dataset affects the decoding performance of deep neural networks, we applied four established deep convolutional neural networks (ConvNets) to the task of pathological versus non-pathological classification and compared the performance of each architecture after training on different datasets. The results show that training on the automatically labelled TUABEXB dataset rather than training on the manually labelled TUAB dataset increases accuracies on TUABEXB and even for TUAB itself for some architectures. We argue that automatically labelling of large open-source datasets can be used to efficiently utilise the massiv

关键词： Pathology

来源：评论

学校读者我要写书评

暂无评论

SelectQ: Calibration data Selection for Post-Training Quantization

TechRxiv

引用

TechRxiv 2022年

作者： Zhang, Zhao Gao, Yangcheng Fan, Jicong Zhao, Zhongqiu Yang, Yi Yan, Shuicheng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230009 China School of Data Science The Chinese University of Hong Kong Shenzhen China The College of Computer Science and Technology Zhejiang University Hangzhou China Singapore

Post-training quantization (PTQ) can reduce the memory footprint and latency of deep model inference while still preserving the accuracy of model, with only a small unlabeled calibration set and without the retraining on full training set. To calibrate a quantized model, current PTQ methods usually randomly select some unlabeled data from the training set as calibration data. However, we show the random data selection would result in performance instability and degradation due to the activation distribution mismatch. In this paper, we attempt to solve the crucial task on appropriate calibration data selection, and propose a novel one-shot calibration data selection method termed SelectQ, which selects specific data for calibration via dynamic clustering. The setting of our SelectQ uses the statistic information of activation and performs layer-wise clustering to learn an activation distribution on training set. For that purpose, a new metric called Knowledge Distance is proposed to calculate the distances of the activation statistics to centroids. Finally, after calibration with the selected data, quantization noise can be alleviated by mitigating the distribution mismatch within activations. Extensive experiments on ImageNet dataset show that our SelectQ increases the Top-1 accuracy of ResNet18 over 15% in 4-bit quantization, compared to randomly sampled calibration data. It’s noteworthy that SelectQ does not involve both the backward propagation and batch normalization parameters, which means that it has fewer limitations in practical applications. © 2022, CC BY.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

Deep machine learning based egyptian vehicle license plate recognition systems

arXiv

引用

arXiv 2021年

作者： Shehata, Mohamed Abou-Kreisha, Mohamed Taha Elnashar, Hany Systems & Computers Engineering Department Faculty of Engineering Al-Azhar University Egypt Mathematical & Computer Science Department Faculty of Science Al-Azhar University Egypt Project Development Unite Faculty of Computers and Artificial Intelligent Beni-Suef University

Automated Vehicle License Plate (VLP) detection and recognition have ended up being a significant research issue as of late. VLP localization and recognition are some of the most essential techniques for managing traffic using digital techniques. In this paper, four smart systems are developed to recognize Egyptian vehicles' license plates. Two systems are based on character recognition, which are (System1: Characters Recognition with Classical Machine Learning) and (System2: Characters Recognition with Deep Machine Learning). The other two systems are based on the whole plate recognition which are (System3: Whole License Plate Recognition with Classical Machine Learning) and (System4: Whole License Plate Recognition with Deep Machine Learning). We use object detection algorithms, and machine learning based object recognition algorithms. The performance of the developed systems has been tested on real images, and the experimental results demonstrate that the best detection accuracy rate for VLP is provided by using the deep learning method. Where the VLP detection accuracy rate is better than the classical system by 32%. However, the best detection accuracy rate for Vehicle License Plate Arabic Character (VLPAC) is provided by using the classical method. Where VLPAC detection accuracy rate is better than the deep learning-based system by 6%. Also, the results show that deep learning is better than the classical technique used in VLP recognition processes. Where the recognition accuracy rate is better than the classical system by 8%. Finally, the paper output recommends a robust VLP recognition system based on both statistical and deep machine learning. © 2021, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Additive Manufacturing of Ti6Al4V Alloy Through Laser Metal Deposition (LMD): An Elebotative Approach of Process Modelling Via Computational Fluid Dynamics

SSRN

引用

SSRN 2023年

作者： Rehman, Asif Ur Azher, Kashif Rehman, Aqsa Shah, Mussadiq Mahmood, Muhammed Arif Tüfekci, Celal Sami Salamci, Metin U. ERMAKSAN Bursa Turkey Department of Mechanical Engineering Faculty of Engineering Gazi University Ankara Turkey Additive Manufacturing Technologies Application and Research Center-EKTAM Ankara Turkey Department of Computer Science NFC-IET Punjab Multan60000 Pakistan Advanced Manufacturing Technologies Center of Excellence-URTEMM Ankara Turkey Intelligent Systems Center Missouri University of Science and Technology RollaMO65409 United States

Additive manufacturing (AM) has revolutionized many industries. Laser Melting Deposition (LMD) offers remarkable utility in size and is an effective method in terms of energy efficiency. The interaction of powder with laser before coming in contact with the substrate helps to attain fair melting and deposition. However, there is a problem associated with non-uniformity and accumulation of metallic melt on the edges of the manufactured parts which needs to be understood. Herein, LMD process of Ti6Al4V alloy is presented. The forces defining the melt pool flow have been elaborated. The phenomenon of recoil pressure and Marangoni actions along the single tracks of the melt pool is defined via the computational fluid dynamics (CFD) analysis. The behaviour of the melt pool in conduction mode and to check whether it will form a keyhole region or not. The main factor which influences the melt pool dynamics are found to be surface tension and density differences. Two modes of heat transfer conduction and depression are discussed with the Marangoni actions. Conduction mode is mainly governed by Marangoni actions while the depression mode is by recoil pressure. The stream traces and velocity vectors are observed for single tracks LMD process. A similar Marangoni actions experimental investigation is compared with the phenomenon observed under CFD analysis. The Discrete Element Modelling (DEM) adds value to the study as variable particle diameters are considered which are near to the actual powder size distribution. Moreover, considering the conduction mode flow dynamics to avoid the depression mode and generation of keyhole region in the melt pool will lead to porosity. © 2023, The Authors. All rights reserved.

关键词： Computational fluid dynamics

来源：评论

学校读者我要写书评

暂无评论

Effect of deep levels on the temperature dependence of the capacitance-voltage characteristics and the effective density of NiO/Ga2O3 heterojunction diodes

SSRN

引用

SSRN 2024年

作者： Senouci, Yasmine Sengouga, Nouredine Almalki, Abdulaziz Henini, Mohamed Qin, Yuan Zhang, Yuhao Labed, Madani University of Biskra Biskra07000 Algeria School of Physics and Astronomy University of Nottingham NottinghamNG7 2RD United Kingdom Physics Department Faculty of Science Taibah University-Yanbu King Khalid Rd. Al Amoedi 46423 Yanbu El-Bahr51000 Saudi Arabia Center for Power Electronics Systems Virginia Polytechnic Institute State University BlacksburgVA24060 United States Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of

This work investigates the temperature-dependent capacitance-voltage (C-V) characteristics of NiO/Ga2O3 heterojunction diodes (HJDs) before and after thermal annealing. The voltage barrier (VB) and the effective doping density (Neff) are extracted from these characteristics. Neff showed a decrease with increasing temperature, which is an unusual behaviour related to deep defects in Ga2O3. Deep levels revealed by deep level transient spectroscopy (DLTS) and Laplace-DLTS (LDLTS) measurements were exploited to perform simulation using SCAPS. First, an ideal NiO/Ga2O3 HJD is considered, then the defects of the fresh and annealed samples detected by DLTS and LDLTS are introduced. The results confirmed the effect of deep levels and have shown a decrease in the effective doping density of the fresh and annealed samples compared to the ideal NiO/Ga2O3 *** addition, the plots of the band diagram evolution with temperature have provided further insight explanation about this phenomenon. © 2024, The Authors. All rights reserved.

关键词： Nickel oxide

来源：评论

学校读者我要写书评

暂无评论

Creating Own AI datasets from Different Language Sources Efficiently: Advantages, Disadvantages and Best Pracitces to Make Own datasets

Creating Own AI Datasets from Different Language Sources Eff...

引用

International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： György Molnár József Cserkó Enikő Nagy Zoltán Balogh Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Széchenyi University Doctoral School of Multidisciplinary Engineering Sciences (MMTDI) Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Institute of Cyberphysical Systems Óbuda University Budapest Hungary Department of Informatics Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia

Artificial Intelligence (AI) has become a ubiquitous technology that has the potential to revolutionize many industries. However, AI requires access to vast amounts of data to achieve its full potential. By understanding these data sources and the best practices for working with them, we can unlock the full potential of AI and create groundbreaking solutions that benefit society as a whole. data sources can be utilized to train AI algorithms, including structured and unstructured data, but finding relevant datasets is challenging. The main problem is the international sources. Most of the datasets are available only in English or other common languages. However these are good sources, but translating them is expensive and time-consuming. This paper shows how to generate own datasets from open-source data with translation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis 28

METNet: A Mutual Enhanced Transformation Network for Aspect-...

引用

28th International Conference on Computational Linguistics, COLING 2020

作者： Jiang, Bin Hou, Jing Zhou, Wanyue Yang, Chao Wang, Shihan Pang, Liang College of Computer Science and Electronic Engineering Hunan University China Intelligent Systems Utrecht University Netherlands CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781952148279

Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of each specific aspect in a given sentence. Existing researches have realized the importance of the aspect for the ABSA task and have derived many interactive learning methods that model context based on specific aspect. However, current interaction mechanisms are ill-equipped to learn complex sentences with multiple aspects, and these methods underestimate the representation learning of the aspect. In order to solve the two problems, we propose a mutual enhanced transformation network (METNet) for the ABSA task. First, the aspect enhancement module in METNet improves the representation learning of the aspect with contextual semantic features, which gives the aspect more abundant information. Second, METNet designs and implements a hierarchical structure, which enhances the representations of aspect and context iteratively. Experimental results on SemEval 2014 datasets demonstrate the effectiveness of METNet, and we further prove that METNet is outstanding in multi-aspect scenarios. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Sentiment analysis

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：