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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Fault diagnosis for the ship electric propulsion system

Fault diagnosis for the ship electric propulsion system

引用

international conference on Natural Computation (ICNC)

作者： Bing Li Meiyuan Chen Rongrong Wang Lanyong Zhang Harbin Engineering University Harbin Heilongjiang CN Communication Engineering Harbin Institute of Technology China

ISBN: (纸本)9781467376808

According to the actual condition of the ship electric propulsion system, the fault tree was built via analyzing the fault characteristics and reasons of the components of the system. the analytic hierarchy process was used to calculate the weight of the fault reasons and formed a corresponding intelligent fault tree. Some rules which were used to build the knowledge base of fault diagnosis expert system in Access could be extracted from the intelligent tree. the fault diagnosis system made use of the width-first search strategy and forward reasoning control strategy. the BP neural networks algorithm was used to make up the lack of a learning disability. the system could help the workers to diagnose and repair the faults of the marine electric propulsion system more quickly and reduce the cost.

关键词： Propulsion Fault trees Fault diagnosis Marine vehicles Expert systems Inverters

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ELA: A Time-Series Forecasting Model for Liner Shipping Based on EMD-LSTM and Attention 11th

ELA: A Time-Series Forecasting Model for Liner Shipping Bas...

引用

11th international conference on Computational data and Social Networks, CSoNet 2022

作者： Chen, Jiadong Gao, Xiaofeng Chen, Guihai MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031263026

the development of modern information technology has led to the digital and intelligent transformation of many traditional industries. In order to face the challenge of difficult transition, liner shipping industry is in urgent need of prediction methods for strategy adjustment, and much research remains to be done in this area. In our paper, we put forward a time-series forecasting model named ELA based on modified EMD-LSTM (Empirical Mode Decomposition and Long Short-Term Memory), AR (Autoregressive) and the attention mechanism. Tailored for handling the market characteristics of the liner shipping industry, our method effectively reduces the negative impact of noise on model training and learns the short-term dependencies and long-term trend in time series. Experiments on real-world liner shipping datasets show the powerful prediction performance of ELA. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Empirical mode decomposition

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Wireless Signal Recognition Based on ResNet-Transformer

Wireless Signal Recognition Based on ResNet-Transformer

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intelligent Computing and Wireless Optical Communications (ICWOC), international conference on

作者： Jianyong Chen Rui Zhao Chengyu Yang Yaoyao Dai College of Information Science and Engineering Huaqiao University Xiamen China

In the face of the rapid development of wireless technology in the Internet of things (IoT), the communication capacity that wireless spectrum can carry is increasing. this has led to wireless signal recognition becoming a key technology for intelligent spectrum management. this paper proposes a deep learning model for wireless signal recognition, called Hybrid net. this model consists of a residual block that extracts local information from the sample and a Transformer block that extracts global information from the sample. these two blocks are added together through a residual unit to form a more complete representation of the feature space. It also avoids the problem of high model complexity and long training time caused by using data in frequency domain format. In order to better simulate actual communication environments, in-phase and quadrature (IQ) and IQ with random phase offset datasets were constructed for training and testing. Experimental results show that the proposed Hybrid net can achieve recognition accuracy of over 80% at low signal to noise ratios (SNRs grater than −6 dB). the Hybrid net even reach 100% at 4 dB. In addition, Hybrid net also demonstrated good generalization performance on different datasets.

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Obtaining Dense Road Speed Estimates from Sparse GPS Measurements

Obtaining Dense Road Speed Estimates from Sparse GPS Measure...

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international conference on intelligent Transportation

作者： Andrew Phan Frank Ferrie Center for Intelligent Machines Department of Electrical and Computer Engineering McGill University Canada

A major challenge for traffic management systems is the inference of traffic flow in regions of the network for which there is little data. In this paper, GPS-based vehicle locator data from a fleet of 40-60 roving ambulances are used to estimate traffic congestion along a network of 20,000 streets in the city of Ottawa, Canada. Essentially, the road network is represented as a directed graph and a belief propagation algorithm is used to interpolate measurements from the fleet. the system incorporates a number of novel features. It makes no distinctions between freeways and surface streets, incorporates both historical and live sensor data, handles user inputs such as road closures and manual speed overrides, and is computationally efficient - providing updates every 5 to 6 minutes on commodity hardware. Experimental results are presented which address the key issue of validating the performance and reliability of the system.

关键词： Global Positioning System Velocity measurement Telecommunication traffic Roads Vehicles Cities and towns Belief propagation intelligent transportation systems intelligent networks Fluid flow measurement

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A Semi-automated Entity Relation Extraction Mechanism with Weakly Supervised learning for Chinese Medical Webpages

A Semi-automated Entity Relation Extraction Mechanism with W...

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international conference for Smarth Health (ICSH)

作者： Liu, Zhao Tong, Jian Gu, Jinguang Liu, Kai Hu, Bo Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430065 Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Hubei Peoples R China Kingdee Int Software Grp Co Ltd Kingdee Cloud Platform Dept Shenzhen 518057 Peoples R China

ISBN: (纸本)9783319598581;9783319598574

Medical entity relation extraction is of great significance for medical text data mining and medical knowledge graph. However, medical field requires very high data accuracy rate, the current medical entity relation extraction system is difficult to achieve the required accuracy. A main technical difficulty lies in how to obtain high-precision medical data, and automatically generate annotated training sample set. In this paper, a medical entity relation automatic extraction system based on weak supervision is proposed. At first, we designed a visual annotation tool, it can automatically generate crawl scripts, crawling the medical data from the site where the entity and its attributes are Separate stored. then, based on the acquired data structure, we propose a weakly supervised hypothesis to automatically generate positive sample training data. Finally, we use CNN model to extract medical entity relation. Experiments show that the method is feasible and accurate.

关键词： Medical entity Entity relation extraction Convolutional neural network Weakly supervised learning

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From Monolingual to Multilingual: Enhancing Hate Speech Detection with Multi-channel Language Models 28th

From Monolingual to Multilingual: Enhancing Hate Speech Dete...

引用

28th international conference on Knowledge Based and intelligent information and engineering Systems, KES 2024

作者： Kia, Mahsa Abazari Samiee, Dorsa Northeastern University London United Kingdom Royal Holloway University of London United Kingdom

the rise of social networking services (SNS) has reshaped communication dynamics in cyberspace, yet it has also exacerbated the proliferation of online hate speech due to the anonymity and fluidity these platforms offer. With manual hate speech detection by human annotators proving costly and time-intensive, the imperative for developing automated recognition algorithms is evident. Leveraging knowledge transfer through fine-tuning pre-trained language models has emerged as a promising strategy in natural language processing. Notably, Bidirectional Encoder Representations from Transformers (BERT) and HateBERT, a domain-specific variant, stand out for their ability to glean deep bidirectional representations from extensive corpora. this paper introduces a multichannel model integrating these two language models for multilingual hate speech detection. Furthermore, we explore the efficacy of augmenting input data through translation, ensuring compatibility with the English requirement of the HateBERT model. By evaluating our approach on three non-English datasets alongside an English dataset, we demonstrate achieving state-of-the-art or comparable performance, underscoring the effectiveness of our methodology in combating hate speech across linguistic barriers. © 2024 the Authors.

关键词： Deep learning

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Simulation System for Space Debris intelligent Detecting 11th

Simulation System for Space Debris Intelligent Detecting

引用

11th EAI international conference on Wireless and Satellite Systems, WiSATS 2020

作者： Shi, Feng Chang, Yuee He, Donglei Wang, Kunpeng Li, Huaifeng Beijing Institute of Spacecraft System Engineering Beijing100094 China Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education School of Optoelectronics Beijing Institute of Technology Beijing100081 China Beijing Institute of Tracking and Telecommunications Technology Beijing100094 China

ISBN: (纸本)9783030690687

Space debris has become a major potential safety hazard to the on-orbit spacecraft, which must be considered when launching a spacecraft. A simulation verification system which ground-based and can simulate the debris detecting onboard was established. the system consists of three sub-systems. the background generation sub-system could ensure maximum coverage of the debris targets by adjusting parameters such as satellite orbit, satellite attitude, optical load observation angles, and observation direction. the target identification and tracking sub-system could improve the ratio of track recognition and accuracy by adjusting the detection capability of optical load, switching target recognition and trajectory tracking algorithm. the orbit cataloging sub-system, the processing method of cataloging data can be determined by the process of determining the initial orbit, associating the initial orbit and determining the orbit accurately etc. the system will lay a foundation for updating and enriching the debris cataloging library in the future. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Orbits

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Using Deep learning Models to Predict the Electrical Conductivity of the Influent in a Wastewater Treatment Plant 24th

Using Deep Learning Models to Predict the Electrical Condu...

引用

24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Pereira, João Oliveira, Pedro Duarte, M. Salomé Martins, Gilberto Novais, Paulo LASI/ALGORITMI Centre University of Minho Braga Portugal CEB - Centre of Biological Engineering University of Minho Campus de Gualtar Braga4710-057 Portugal LABBELS - Associate Laboratory Braga/Guimarães Portugal

ISBN: (纸本)9783031482311

Nowadays, human population face increasing water pollution problems, so treating and managing this resource is crucial. Wastewater Treatment Plants (WWTPs) provide essential services for human life since they treat wastewater and monitor its parameters to preserve water quality standards. One of these parameters is electrical conductivity, essential in quantifying water salinity levels. therefore, this paper aims to forecast the influent conductivity in a WWTP for the next two timesteps. Hence, several experiments were conducted, considering the use of Transformers and Long Short-Term Memory (LSTMs) candidate models that were developed, tuned, and evaluated, utilising a recursive multi-step forecasting approach. the best candidate model was based on a Transformer architecture with encoding and obtained a RMSE of 155.2 μ S/cm. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Water pollution

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A Hybrid Architecture of 5G Private Network Solution for Urban Rail Transportation Systems 1

引用

11th international conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Zhou, Jianming Jing, Haoxian Ning, Lei Pang, Kailei China United Network Communications Corporation ShenZhen Branch Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China

ISBN: (数字)9789819912605

ISBN: (纸本)9789819912599

Recently, urban rail transportation is constantly developing to intelligent urban rail based on the Internet of things, artificial intelligence, and high-speed communication. Long Term Evolution for Urban Rail Transportation System (LTE-URTS) is a communication system for urban transportation service, which carries the essential services by urban rail with 4G LTE. However, with the continuous increase of new demands, LTE also faces the shortcomings of insufficient bandwidth and high construction costs. As an advanced solution, networking through 5G can bring better performance to satisfy multiple demands of the modern Urban Rail Transportation System (URTS) than LTE-URTS. this article provides an overview of the characteristics of 5G and LTE-URTS. In addition, we propose a new network architecture solution based on 5G for the URTS with field test results, which have better performance compared to LTE-URTS. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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Brain Tumor Segmentation on MRI Images 11

Brain Tumor Segmentation on MRI Images

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11th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Potnurwar, Archana Chaudhari, Abha Burbure, Nitanshu Wankhede, Kritik Badkhal, Kartik Parshatwar, Anisha Priyadarshini College of Engineering Department of Information Technology Nagpur India

ISBN: (纸本)9798350348422

One of the toughest issues in medically produced image processing and its analysis is generally the process of brain tumor segmentation. In order to accurately outline the areas of the tumor, brain tumor segmentation is done. Many approaches have recently demonstrated promising results in addressing a variety of computer vision issues, including image classification, entity identification, and segmentation of various semantics. Most of the advace deep learning-based techniques have been used to segment brain tumors with encouraging success. the ideal imaging technique for diagnosing brain tumors and obtaining comprehensive data on type of tumor, its size, location and most importantly its identification, and detection is medical resonance imaging (MRI) and suggesting appropriate treatment. In order to create a more straightforward image that is more relevant and simpler to study, segmentation breaks a digital image into numerous divisions and specifies the separation of the questionable region of brain through the process of pre-processing MRI scans. © 2023 IEEE.

关键词： Diagnosis

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